├── README
├── march_cps_codebook.pdf
├── more_examples
    ├── Python
    │   ├── marchCPS_2010.txt
    │   └── pandas_examples.py
    └── R
    │   └── weird_formula.r
├── original
    ├── Python
    │   ├── marchCPS_2010.txt
    │   └── py_stats_analysis.py
    ├── R
    │   ├── marchCPS_2010.txt
    │   └── r_stats_analysis.R
    └── summary.txt
└── revised
    ├── Python
        ├── marchCPS_2010.txt
        └── py_stats_analysis.py
    ├── R
        ├── marchCPS_2010.txt
        └── r_stats_analysis.R
    └── summary.txt


/README:
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 1 | This contains a comparison of R and Python for a simple OLS analyis 
 2 | of a dateset. 
 3 | 
 4 | The original comparison is in the folder original. 
 5 | 
 6 | A revised comparison, using comments from the scipy users list,
 7 | is in the folder revised. 
 8 | 
 9 | This revised comparison has simpler R and Python code and uses pandas
10 | for part of the analysis. (However, it is pandas 0.4, which is 
11 | currently in development.) 
12 | 


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/march_cps_codebook.pdf:
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https://raw.githubusercontent.com/chrisjordansquire/r_vs_py/e99b73048f007aec0d3b9790a186a9c3338d9936/march_cps_codebook.pdf


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/more_examples/Python/pandas_examples.py:
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  1 | 
  2 | 
  3 | import numpy as np
  4 | import scikits.statsmodels.api as sm
  5 | import matplotlib.pyplot as plt
  6 | import scipy.stats as sps
  7 | import pandas as pa
  8 | 
  9 | 
 10 | 
 11 | """
 12 | Load in the data as in the other files, but then we stick it
 13 | into a pandas DataFrame. We then use the new 0.4 pandas to
 14 | generate some simple summary statistics to understand the data.
 15 | 
 16 | The major new feature in pandas 0.4 is the multi-index, and associated
 17 | functionality to manipulate them. In a nutshell:
 18 | 
 19 |     *The multi-index is a tuple of objects to index on. This is convenient
 20 |         for categorical or binned variables. (e.g. race, education level,
 21 |         age range, weight range, etc.)
 22 |     *These multi-indexes can be used to index both rows and columns.
 23 |     *The pivot command lets you take a column of data and convert it
 24 |         to an index in a multi-index.
 25 |     *The stack/unstack commands let you re-organize indices between
 26 |         indexing rows or columns.
 27 |     *The groupby functionality plays well with the new multi-indices,
 28 |         exactly as one would hope. In particular, you can also
 29 |         use groupby on these new multi-indexes.
 30 | 
 31 | This new multi-index functionality is very nice for replicating the
 32 | sort of split-apply-combine functionality that can be accomplished
 33 | in SQL or using the plyr package in R.
 34 | 
 35 | Some potential gotchas I ran into while creating this were:
 36 | 
 37 |     *Thus far, groupby can't respect any non-lexigraphic ordering
 38 |         on grouping variables. So when grouping on ordinal variables
 39 |         you must ensure the lexigraphic ordering is the same as
 40 |         the natural ordering if you want groupby to respect it.
 41 |     *If you want the number of items in each group you must use
 42 |         apply instead of aggregate. aggregate will again return
 43 |         a DataFrame, whereas you just want a Series. This is true
 44 |         in general if your desired aggregation would yield the same
 45 |         value across all columns.
 46 | 
 47 | In the following I freely make use of awful, awful names simply
 48 | because in the interpreter they're easy to remember and type.
 49 | I do this just because that's a not unreasonable style for iteractive
 50 | use. b in front of a variable I create denotes binned, g denotes
 51 | a groupby.
 52 | """
 53 | 
 54 | # Load and clean the data, removing a few people that don't seem
 55 | # to fit most reasonable models.
 56 | 
 57 | dat = np.genfromtxt('marchCPS_2010.txt', names=True, dtype=None,
 58 |                         missing_values='NA')
 59 | # Remove everyone without hourly wage data
 60 | indhr = ~np.isnan(dat['hrwage'])
 61 | hrdat = dat[indhr]
 62 | hrdat = np.delete(hrdat, [407, 515, 852, 1197])
 63 | 
 64 | 
 65 | ###
 66 | # Create a dataframe and start the exploratory analysis
 67 | ###
 68 | 
 69 | d = pa.DataFrame(hrdat)
 70 | 
 71 | # The names corresponding to the numerical codes
 72 | sex_names = {1:'Male', 2:'Female'}
 73 | race_names = {1:'White', 2:'Black', 3:'First Nation', 4:'Asian', 6:'Mixed'}
 74 | ptft_names = {1:'1-Part time', 2:'2-Full time'}
 75 | occ_names = {1:'Management', 2:'Professional', 3:'Service', 4:'Sales',
 76 |                 5:'Office Support', 7:'Construction',
 77 |                 8:'Maintenance', 9:'Production', 10:'Transportation'}
 78 | b_age_names = {0:'18-24', 1:'25-29', 2:'30-34', 3:'35-39', 4:'40-44',
 79 |                 5:'45-49', 6:'50-54', 7:'55-59',8:'60+'}
 80 | 
 81 | # Function to create 5-ish year bins for ages
 82 | def time_bucket(age):
 83 |     return np.digitize(age, [25,30,35,40,45,50,55,60])
 84 | 
 85 | # Function to bucket similar education levels
 86 | # Leadning integers are adding to force groupby to use the correct
 87 | # ordering, as these variables have an order.
 88 | def educ_bucket(educ):
 89 |     if educ<39:
 90 |         return '1:<HS'
 91 |     elif educ==39:
 92 |         return '2:HS'
 93 |     elif educ==40:
 94 |         return '3:Some College'
 95 |     elif educ in [41,42]:
 96 |         return '4:Associate'
 97 |     elif educ == 43:
 98 |         return '5:Bachelor'
 99 |     elif educ > 43:
100 |         return '6:Grad/Prof'
101 | 
102 | # Replace with numerical codes with the corresponding string names
103 | d['sex'] = d['sex'].apply(sex_names.get)
104 | d['race'] = d['race'].apply(race_names.get)
105 | d['PTFT'] = d['PTFT'].apply(ptft_names.get)
106 | d['occ'] = d['occ'].apply(occ_names.get)
107 | 
108 | # b for binned
109 | d['bage'] = d['age'].apply(time_bucket)
110 | d['bage'] = d['bage'].apply(b_age_names.get)
111 | d['beduc'] = d['educ'].apply(educ_bucket)
112 | 
113 | # Get rid of the long, obscure name for the weights
114 | d['wt'] = d['A_ERNLWT']
115 | del d['A_ERNLWT']
116 | 
117 | # age, sex groupby
118 | gas = d.groupby(['bage', 'sex'])
119 | # age, ptft, sex groupby
120 | gaps = d.groupby(['bage','PTFT', 'sex'])
121 | # occ, sex groupby
122 | gos = d.groupby(['occ','sex'])
123 | # educ, sex groupby
124 | ges = d.groupby(['beduc','sex'])
125 | 
126 | def mf_ratio(df):
127 |     df['ratio'] = df['Female']/df['Male']
128 |     return df
129 | 
130 | def wt_avg(group):
131 |     return np.average(group['hrwage'], weights = group['wt'])
132 | 
133 | # I suspect the following scheme leaves something to be desired numerically.
134 | # It was just translated verbatim from wikipedia's formula for the weighted
135 | # sample variance
136 | def wt_std(group):
137 |     mean = np.average(group['hrwage'], weights = group['wt'])
138 |     sos = np.average((group['hrwage'] - mean)**2, weights=group['wt'])
139 |     wt_sum = group['wt'].sum()
140 |     wt_sos = (group['wt']**2).sum()
141 |     return np.sqrt((wt_sum)/(wt_sum**2 - wt_sos) * sos)
142 | 
143 | # Poke around each of the groupby's.
144 | 
145 | # How many observations, unweighted, for each combination
146 | print gas.apply(len).unstack()
147 | print gaps.apply(len).unstack(level=1).unstack()
148 | print gos.apply(len).unstack()
149 | print ges.apply(len).unstack()
150 | 
151 | # How many observations according to the weights
152 | print gas['wt'].sum().unstack()
153 | print gaps['wt'].sum().unstack(level=1).unstack()
154 | print gos['wt'].sum().unstack()
155 | print ges['wt'].sum().unstack()
156 | 
157 | # First look at the weighted mean for each
158 | print gas.apply(wt_avg).unstack()
159 | print gaps.apply(wt_avg).unstack(level=1).unstack()
160 | print gos.apply(wt_avg).unstack()
161 | print ges.apply(wt_avg).unstack()
162 | 
163 | # Then look at the weighted sample stardard errors
164 | #   (Turns out that because of the weights they're all tiny,
165 | #   so there's no need to think about the confidence intervals.
166 | #   But it would be simple to create a function that returned confidence
167 | #   intervals as a tuple instead of a scalar point estimate.)
168 | print gas.apply(wt_std).unstack()
169 | print gaps.apply(wt_std).unstack(level=1).unstack()
170 | print gos.apply(wt_std).unstack()
171 | print ges.apply(wt_std).unstack()
172 | 
173 | # Take a closer look at how the ratios change
174 | # Dealing with the multi-index is slightly ungainly, and can probably
175 | # be improved.
176 | print mf_ratio(gas.apply(wt_avg).unstack())
177 | print mf_ratio(gaps.apply(wt_avg).unstack()).stack().unstack(level=1).unstack()
178 | print mf_ratio(gos.apply(wt_avg).unstack())
179 | print mf_ratio(ges.apply(wt_avg).unstack())
180 | 
181 | 
182 | 
183 | 


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/more_examples/R/weird_formula.r:
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 1 | # This is the code for an example that popped up on the statsmodels
 2 | # list a year or so ago, showing a bizarre example of how things
 3 | # can work when R is creating model matrices for the user. 
 4 | 
 5 | # (At least that's what the discussion thread was about. I'm not
 6 | # sure if the real issue is the model matrix or how the anova
 7 | # command works.)
 8 | 
 9 | # n is the sample size
10 | # d1 is the number of categories in x
11 | # d2 is the number of categories in y
12 | # It's left arbitrary just to show this works for any d1, d2
13 | n<-200
14 | d1<-3
15 | d2<-5
16 | 
17 | x<-sample(1:d1, n, replace=T)
18 | y<-sample(1:d2, n, replace=T)
19 | 
20 | x<-factor(x)
21 | y<-factor(y)
22 | 
23 | X<-model.matrix(~x*y)
24 | k<-dim(X)[2]
25 | 
26 | # Generate some output values with an
27 | # arbitrary model
28 | out<- X %*% 1:k + rnorm(n, sd=2)
29 | 
30 | # Fit two different models, with m1 nested in m2
31 | # in the sense that y*x is y+x+y:x
32 | m1<-lm(out~y+y:x)
33 | m2<-lm(out~y*x)
34 | 
35 | # Now look at the difference in sum of squares between them
36 | anova(m1,m2)
37 | 
38 | # The models are the "same" in the sense that they have the
39 | # same fitted values
40 | 
41 | # However, the models are not the same. 
42 | # The model matrices are not the same. 
43 | all(model.matrix(m1) == model.matrix(m2))
44 | # And the interaction coefficients are not the same
45 | (coef(m1)-coef(m2))/coef(m1)
46 | 


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/original/Python/py_stats_analysis.py:
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  1 | """
  2 | This is an experiment to see how easily an analysis
  3 | done in R could be done in Python. The rcode is in a
  4 | companion file. The comments are cut and pasted from
  5 | the R code, and similar variable names are used as
  6 | much as possible to make comparisons easier.
  7 | """
  8 | 
  9 | #An example analysis of a fairly simple dataset.
 10 | #The data is from the 2010 CPS March supplement.
 11 | #(Also called the Annual Social and Economic Supplement.)
 12 | #The goal is examining the difference in hourly pay
 13 | #between males and females. The column A_ERNLWT is
 14 | #survey weights, PTFT is part-time/full-time status,
 15 | #educ is education level, ind is what industry the
 16 | #person works in and occ is what occupation they work in.
 17 | #A full description of most of the variables sometimes with
 18 | #slightly different names, can be found at
 19 | #http://www.census.gov/apsd/techdoc/cps/cpsmar10.pdf
 20 | 
 21 | 
 22 | 
 23 | import numpy as np
 24 | import scikits.statsmodels.api as sm
 25 | import matplotlib.pyplot as plt
 26 | import scipy.stats as sps
 27 | 
 28 | dat = np.genfromtxt('marchCPS_2010.txt', names=True, dtype=None,
 29 |                         missing_values='NA')
 30 | 
 31 | print dat.shape
 32 | print dat.dtype
 33 | print len(dat.dtype)
 34 | 
 35 | 
 36 | ####Actual analysis code
 37 | 
 38 | #Remove everyone that doesn't have an houlry wage data
 39 | 
 40 | 
 41 | 
 42 | indhr = ~np.isnan(dat['hrwage'])
 43 | hrdat = dat[indhr]
 44 | 
 45 | 
 46 | #Remove several people who just didn't fit the models (using the
 47 | #standard model checking techniques below)
 48 | 
 49 | 
 50 | hrdat = np.delete(hrdat, [407, 515, 852, 1197])
 51 | 
 52 | 
 53 | indf = np.flatnonzero(dat['sex'] == 2)
 54 | indm = np.flatnonzero(dat['sex'] == 1)
 55 | 
 56 | print len(indf)
 57 | print len(indm)
 58 | 
 59 | #With each of these models, typically do some
 60 | #commands to look more at the models, like summary(),
 61 | #, anova for the model on its own or betwen two models to see
 62 | #how much additional explantory power you get with the added
 63 | #variables, and plots to look at residuals, qqplot, and hist of residuals
 64 | #Currently can't do anova or lowess in python, and the qqplots are annoying
 65 | #to make.
 66 | 
 67 | 
 68 | #Initial model, only look at log(hrwage)~sex
 69 | X1 = hrdat['sex']==2
 70 | X1 = sm.add_constant(X1, prepend=True)
 71 | model1 = sm.WLS(np.log(hrdat['hrwage']), X1, weights = hrdat['A_ERNLWT'])
 72 | results1 = model1.fit()
 73 | 
 74 | print results1.summary()
 75 | 
 76 | 
 77 | 
 78 | 
 79 | #More complicated model, log(hrwage)~sex+educ+age+PTFT
 80 | n = len(hrdat)
 81 | logwage = np.log(hrdat['hrwage'])
 82 | w = hrdat['A_ERNLWT']
 83 | 
 84 | X2 = np.hstack((sm.categorical(hrdat['sex'])[:,2:],
 85 |                 sm.categorical(hrdat['educ'])[:,2:],
 86 |                 hrdat['age'].reshape(n,1),
 87 |                 sm.categorical(hrdat['PTFT'])[:,2:]))
 88 | 
 89 | X2 = sm.add_constant(X2, prepend=True)
 90 | model2 = sm.WLS(logwage, X2, weights = w)
 91 | results2 = model2.fit()
 92 | 
 93 | print results2.summary()
 94 | 
 95 | 
 96 | 
 97 | #Now include ind and occ (industry and occupation codings)
 98 | X2_5 = np.hstack((sm.categorical(hrdat['sex'])[:,2:],
 99 |                     sm.categorical(hrdat['educ'])[:,2:],
100 |                     hrdat['age'].reshape(n,1),
101 |                     (hrdat['age']**2).reshape(n,1),
102 |                     sm.categorical(hrdat['PTFT'])[:,2:],
103 |                     sm.categorical(hrdat['ind'])[:,2:],
104 |                     sm.categorical(hrdat['occ'])[:,2:]))
105 | X2_5 = sm.add_constant(X2_5, prepend=True)
106 | model2_5 = sm.WLS(logwage, X2_5, weights=w)
107 | results2_5 = model2_5.fit()
108 | 
109 | print results2_5.summary()
110 | 
111 | 
112 | #Residual diagnostics for model2_5
113 | 
114 | plt.subplot(1,3,1)
115 | plt.hist(results2_5.resid, 30, normed=1, facecolor='green', alpha=0.75)
116 | plt.title('Histogram of Residuals')
117 | plt.xlabel('Probability')
118 | plt.grid(True)
119 | 
120 | plt.subplot(1,3,2)
121 | plt.plot(results2_5.fittedvalues, results2_5.resid, 'bo')
122 | plt.title('Residuals vs. Fitted Values')
123 | plt.xlabel('Fitted Values')
124 | plt.ylabel('Residuals')
125 | plt.axhline(lw=3, color = 'r')
126 | plt.grid(True)
127 | 
128 | plt.subplot(1,3,3)
129 | normal_rv_q = sps.norm.ppf(np.linspace(0,1,n+1)[1:])
130 | ordered_resid = np.copy(results2_5.resid)
131 | ordered_resid.sort()
132 | plt.plot(normal_rv_q, ordered_resid, 'ro')
133 | plt.title('Normal qq Plot')
134 | plt.xlabel('Theoretical Quantiles')
135 | plt.ylabel('Sample Quantiles')
136 | plt.grid(True)
137 | 
138 | 
139 | #Used mean replacement for all the people whose hours varied,
140 | #where the mean of 22 was the mean over all part time workers
141 | #w/o missing data and using their weights
142 | #Done just as a sanity check on the above models
143 | 
144 | 
145 | 
146 | #Throw in everything and the kitchen sink for reality check
147 | indic = np.flatnonzero(hrdat['PMHRUSLT']<=0)
148 | tmp = np.copy(hrdat['PMHRUSLT'])
149 | tmp[indic] = 22
150 | 
151 | X3 = np.hstack((sm.categorical(hrdat['sex'])[:,2:],
152 |                   sm.categorical(hrdat['educ'])[:,2:],
153 |                   sm.categorical(hrdat['PTFT'])[:,2:],
154 |                   hrdat['age'].reshape(n,1),
155 |                   (hrdat['age']**2).reshape(n,1),
156 |                   sm.categorical(hrdat['marstat'])[:,2:],
157 |                   sm.categorical(hrdat['GEDIV'])[:,2:],
158 |                   sm.categorical(hrdat['race'])[:,2:],
159 |                   sm.categorical(hrdat['hispanic'])[:,2:],
160 |                   tmp.reshape(n,1),
161 |                   sm.categorical(hrdat['disabled'])[:,2:]))
162 | 
163 | X3 = sm.add_constant(X3, prepend=True)
164 | model3 = sm.WLS(logwage, X3, weights=w)
165 | results3 = model3.fit()
166 | 
167 | print results3.summary()
168 | 
169 | 
170 | #These models bin the works by age group, <=30, 31-40, 41-50, >50.
171 | #This was done to see what the difference betweens males and
172 | #females was in each bin. This was done as a sanity check for the
173 | #later exploratory analysis that fit lowess curves across age to
174 | #males and females seperately. I wanted to make sure that the trends
175 | #observed were real. (Where the gap was smaller for younger workers,
176 | #expanded for middle age workers, and then contracted again.)
177 | 
178 | 
179 | lt30 = (hrdat['age']<=30)
180 | btw30_40 = (np.logical_and(30 <hrdat['age'], hrdat['age']<=40))
181 | btw40_50 = (np.logical_and(40< hrdat['age'], hrdat['age']<=50))
182 | gt50 = (50<hrdat['age'])
183 | 
184 | 
185 | def age_design(indices):
186 |   tmp = np.hstack((sm.categorical(hrdat['sex'][indices])[:,2:],
187 |                   sm.categorical(hrdat['educ'][indices])[:,2:],
188 |                   sm.categorical(hrdat['PTFT'][indices])[:,2:],
189 |                   hrdat['age'].reshape(n,1)[indices,:],
190 |                   (hrdat['age']**2).reshape(n,1)[indices,:]))
191 |   return sm.add_constant(tmp, prepend = True)
192 | 
193 | 
194 | def age_model(indices):
195 |   return sm.WLS(logwage[indices], age_design(indices), weights = w[indices])
196 | 
197 | model7_1 = age_model(lt30)
198 | model7_2 = age_model(btw30_40)
199 | model7_3 = age_model(btw40_50)
200 | model7_4 = age_model(gt50)
201 | 
202 | results7_1 = model7_1.fit()
203 | results7_2 = model7_2.fit()
204 | results7_3 = model7_3.fit()
205 | results7_4 = model7_4.fit()
206 | 
207 | print results7_1.summary()
208 | print results7_2.summary()
209 | print results7_3.summary()
210 | print results7_4.summary()
211 | 
212 | ####
213 | 
214 | 
215 | #This analysis switches gears and focuses on occupation.
216 | #The males and females are broken down by occupation and the
217 | #weighted mean of their hourly wage are compared.
218 | #(The weights used are again the survey weights.)
219 | #The matrix wocc stores all of that, and
220 | #Use xtable to format tables in latex
221 | 
222 | 
223 | 
224 | occs = sorted(set(hrdat['occ']))
225 | wocc = np.zeros( (len(occs), 4))
226 | for i,occ in enumerate(occs):
227 |   tmp_male = np.logical_and(hrdat['sex']==1, hrdat['occ'] == occ)
228 |   tmp_female = np.logical_and(hrdat['sex']==2, hrdat['occ'] == occ)
229 |   if np.any(tmp_male):
230 |     wocc[i, 0] = sum(hrdat['A_ERNLWT'][tmp_male])
231 |     wocc[i, 1] = np.average(hrdat['hrwage'][tmp_male],
232 |         weights = hrdat['A_ERNLWT'][tmp_male])
233 |   if np.any(tmp_female):
234 |     wocc[i, 2] = np.sum(hrdat['A_ERNLWT'][tmp_female])
235 |     wocc[i, 3] = np.average(hrdat['hrwage'][tmp_female],
236 |                               weights = hrdat['A_ERNLWT'][tmp_female])
237 | 
238 | print wocc
239 | 
240 | 
241 | #These were just some quick computations to make sure wocc had
242 | #the values I thought it had. Nothing more embarassing than
243 | #bad statistics because you didn't double-check your output.
244 | 
245 | 
246 | 
247 | males = hrdat['sex']==1
248 | females = hrdat['sex'] ==2
249 | 
250 | np.average(hrdat['hrwage'][males])
251 | np.average(hrdat['hrwage'][males],
252 |             weights = hrdat['A_ERNLWT'][males])
253 | sum(wocc[:,0]*wocc[:,1])/sum(wocc[:,0])
254 | np.average(hrdat['hrwage'][females],
255 |             weights = hrdat['A_ERNLWT'][females])
256 | tmp = np.delete(wocc, 5, axis=0)
257 | sum(tmp[:,0] * tmp[:,3]) / sum(tmp[:,0])
258 | 
259 | 
260 | """
261 | The final analysis using lowess can't be done in python (yet!)
262 | since there's no analogous function in the numpy/scipy stack. Alas.
263 | """
264 | 
265 | 
266 | 


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/original/R/r_stats_analysis.R:
--------------------------------------------------------------------------------
  1 | 
  2 | #An example analysis of a fairly simple dataset.
  3 | #The data is from the 2010 CPS March supplement. 
  4 | #(Also called the Annual Social and Economic Supplement.)
  5 | #The goal is examining the difference in hourly pay
  6 | #between males and females. The column A_ERNLWT is 
  7 | #survey weights, PTFT is part-time/full-time status, 
  8 | #educ is education level, ind is what industry the
  9 | #person works in and occ is what occupation they work in. 
 10 | #A full description of most of the variables sometimes with 
 11 | #slightly different names, can be found at
 12 | #http://www.census.gov/apsd/techdoc/cps/cpsmar10.pdf
 13 | 
 14 | 
 15 | dat<-read.table("marchCPS_2010.txt", header=T)
 16 | 
 17 | dim(dat)
 18 | 
 19 | 
 20 | ####Actual analysis code
 21 | 
 22 | #Remove everyone that doesn't have an houlry wage data
 23 | 
 24 | indhw<-which(!is.na(dat$hrwage))
 25 | hrdat<-dat[indhw,]
 26 | 
 27 | #Remove several people who just didn't fit the models (using the
 28 | #standard model checking techniques below) 
 29 | 
 30 | hrdat<-hrdat[-c(408,516,853,1198),]
 31 | 
 32 | indf<-which(hrdat$sex==2)
 33 | indm<-which(hrdat$sex==1)
 34 | 
 35 | #With each of these models, typically do some 
 36 | #commands to look more at the models, like summary(),
 37 | #, anova for the model on its own or betwen two models to see
 38 | #how much additional explantory power you get with the added
 39 | #variables, and plots to look at residuals, qqplot, and hist of residuals
 40 | 
 41 | model1<-lm(I(log(hrwage))~as.factor(sex), data = hrdat, weights = A_ERNLWT)
 42 | 
 43 | model2<-lm(I(log(hrwage))~as.factor(sex)+as.factor(educ)+age+as.factor(PTFT), data=hrdat, weights=A_ERNLWT)
 44 | 
 45 | model2.5<-lm(I(log(hrwage))~as.factor(sex)+as.factor(educ)+age+I(age^2)+as.factor(PTFT)+as.factor(ind)+as.factor(occ), data=hrdat, weights=A_ERNLWT)
 46 | 
 47 | 
 48 | pdf('model_diagnostics.pdf', width=16,height=7)
 49 | par(mfrow=c(1,3))
 50 | hist(model2.5$res, breaks=40, main="Histogram of Residuals", xlab="Residuals", cex.main=2, cex.lab=1.5)
 51 | plot(model2.5$fitted, model2.5$res, main="Residual vs. Fitted Values", xlab="Fitted Values", ylab="Residuals",cex.main=2, cex.lab=1.5)
 52 | lines(lowess(model2.5$fitted, model2.5$res), col='red', lwd=3)
 53 | abline(h=0, col='green', lwd=3)
 54 | qqnorm(model2.5$res,cex.main=2, cex.lab=1.5)
 55 | qqline(as.vector(model2.5$res), col='red')
 56 | dev.off()
 57 | 
 58 | 
 59 | indic<-(hrdat$PMHRUSLT>0)
 60 | 
 61 | 
 62 | #Used mean replacement for all the people whose hours varied,
 63 | #where the mean of 22 was the mean over all part time workers 
 64 | #w/o missing data and using their weights
 65 | #Done just as a sanity check on the above models
 66 | 
 67 | model3<-lm(I(log(hrwage))~ as.factor(sex)+as.factor(educ)+as.factor(PTFT)+age +I(age^2)+ as.factor(marstat) +  
 68 |     as.factor(GEDIV)+ as.factor(race) + 
 69 |     as.factor(hispanic)+I(PMHRUSLT*indic+22*(1-indic))+as.factor(disabled), data = hrdat, weights = A_ERNLWT)
 70 | 
 71 | 
 72 | 
 73 | #These models bin the works by age group, <=30, 31-40, 41-50, >50.
 74 | #This was done to see what the difference betweens males and 
 75 | #females was in each bin. This was done as a sanity check for the
 76 | #later exploratory analysis that fit lowess curves across age to 
 77 | #males and females seperately. I wanted to make sure that the trends 
 78 | #observed were real. (Where the gap was smaller for younger workers,
 79 | #expanded for middle age workers, and then contracted again.)
 80 | 
 81 | model7.1<-lm(I(log(hrwage))~as.factor(sex)+as.factor(educ)+as.factor(PTFT)+age+I(age^2), data = hrdat[hrdat$age<=30,] , weights=A_ERNLWT )
 82 | 
 83 | model7.2<-lm(I(log(hrwage))~as.factor(sex)+as.factor(educ)+as.factor(PTFT)+age+I(age^2), data = hrdat[hrdat$age<=40 & hrdat$age>30,] , weights=A_ERNLWT )
 84 | 
 85 | model7.3<-lm(I(log(hrwage))~as.factor(sex)+as.factor(educ)+as.factor(PTFT)+age+I(age^2), data = hrdat[hrdat$age<=50 & hrdat$age>40,] , weights=A_ERNLWT )
 86 | 
 87 | model7.4<-lm(I(log(hrwage))~as.factor(sex)+as.factor(educ)+as.factor(PTFT)+age+I(age^2), data = hrdat[hrdat$age>50,] , weights=A_ERNLWT )
 88 | 
 89 | 
 90 | 
 91 | ####
 92 | 
 93 | 
 94 | #This analysis switches gears and focuses on occupation. 
 95 | #The males and females are broken down by occupation and the
 96 | #weighted mean of their hourly wage are compared.
 97 | #(The weights used are again the survey weights.)
 98 | #The matrix wocc stores all of that, and 
 99 | #Use xtable to format tables in latex
100 | 
101 | lab<-sort(unique(hrdat$oc))
102 | occrow<-length(unique(hrdat$oc))
103 | wocc<-matrix(rep(0,4*occrow),nrow=occrow, ncol=4)
104 | for(i in 1:length(lab)){
105 | 	tmp<-which(hrdat$sex==1 & hrdat$occ==lab[i])		
106 | 	
107 | 	if(length(tmp)>0){
108 | 		wocc[i,1]<-sum(hrdat$A_ERNLWT[tmp])
109 | 		wocc[i,2]<-weighted.mean(hrdat$hrwage[tmp], hrdat$A_ERNLWT[tmp])	
110 | 	}
111 | 	tmp<-which(hrdat$sex==2 & hrdat$occ==lab[i])		
112 | 	if(length(tmp)>0){
113 | 		wocc[i,3]<-sum(hrdat$A_ERNLWT[tmp])
114 | 		wocc[i,4]<-weighted.mean(hrdat$hrwage[tmp], hrdat$A_ERNLWT[tmp])	
115 | 	}
116 | }
117 | 
118 | #These were just some quick computations to make sure wocc had
119 | #the values I thought it had. Nothing more embarassing than 
120 | #bad statistics because you didn't double-check your output. 
121 | 
122 | males = hrdat$sex == 1
123 | females = hrdat$sex ==2
124 | 
125 | mean(hrdat$hrwage[males])
126 | weighted.mean(hrdat$hrwage[males], hrdat$A_ERNLWT[males])
127 | sum(wocc[,1]*wocc[,2])/sum(wocc[,1])
128 | weighted.mean(hrdat$hrwage[females],hrdat$A_ERNLWT[females])
129 | sum(wocc[-6,1]*wocc[-6,4])/sum(wocc[-6,1])
130 | 
131 | 
132 | 
133 | 
134 | #Plotting hourly wage versus age and fitting a lowess curve to it. 
135 | #Done sepereately for both males and females. Just to get a feel for 
136 | #what their salary trajectories look like. Though it's not totally
137 | #clear from the picture, the gap is smaller proportionally at younger 
138 | #ages than at older ones. (This can be seen as above by binning the ages,
139 | #or via a separate analysis by fitting an interaction term between
140 | #sex and age.)
141 | 
142 | plot(lowess(hrdat$age[indm], (hrdat$hrwage)[indm]), col='blue', ylim=c(7,25), xlab="Age", ylab = "Hourly Wage", main="Hourly Wage vs. Age", type='l', , cex.main=2, cex.lab=1.5, lwd=2)
143 | lines(lowess(hrdat$age[indf], (hrdat$hrwage)[indf]), col='deeppink')
144 | legend('topleft', c("Males", "Females"), cex=1.25, col=c("blue", "deeppink"), lty=1, lwd=2)
145 | 
146 | 
147 | 
148 | 
149 | 
150 | 


--------------------------------------------------------------------------------
/original/summary.txt:
--------------------------------------------------------------------------------
 1 | A simple comparison of R and Python (statsmodels) for doing some
 2 | exploratory data analysis and fitting of simple OLS models. 
 3 | 
 4 | This is a rather condensed version of an analysis I did in a  
 5 | statistics course. It uses CPS data to look at wage differences
 6 | according to gender, examining first a baseline simple model 
 7 | and then successively more complex models.
 8 | 
 9 | For the OLS models R is much easier to use than Python. In 
10 | python the design matrices must be constructed explicitly, 
11 | which it both painstaking and annoying for complicated
12 | models. Furthermore, the resulting coefficient fits are 
13 | unlabelled. Python also doesn't have an anova command like
14 | in R to compare between nested models. 
15 | 
16 | There is also some simple exploratory, non-model based analysis. 
17 | In this area R clearly dominates. Python currently doesn't have
18 | one of the main graphical exploratory tools I used, lowess, and
19 | it doesn't print the contents of matrices in a user friendly 
20 | manner.
21 | 
22 | Both sets of code are used best when cut and pasted into their
23 | respective gui's. In R's case the standard R gui, and in Python's
24 | case the IPython qtconsole. Non-gui interpreters are generally
25 | much more frustrating to use for interactive data analysis, which
26 | is how these analyses were generated and explored.
27 | 
28 | In fact, using Python without the IPython qtconsole is practically
29 | impossible for this sort of cut and paste, interactive analysis. 
30 | The shell IPython doesn't allow it because it automatically adds
31 | whitespace on multiline bits of code, breaking pre-formatted code's
32 | alignment. Cutting and pasting works for the standard python shell,
33 | but then you lose all the advantages of IPython. 
34 | 
35 | Other issues I ran into were:
36 | 
37 | *Inability to easily add new columns and rows to data. (However, 
38 | this will hopefully be fixed with pandas, and additionally it 
39 | wasn't an issue for me in this analysis.) 
40 | 
41 | *Python doesn't pretty print its arrays in any way. This makes it
42 | much more difficult to inspect output from interactive data
43 | analysis if the user was just shoving results/summary statistics
44 | into a single array. 
45 | 
46 | Even if you use the %precision magic in IPython, that only
47 | helps so much because numpy prints numpy arrays by row instead
48 | of by column. The lack of being able to name rows and columns 
49 | after array creation is also an impediment to analysis. 
50 | 
51 | *No matplotlib keyboard shortcuts for closing windows. This makes it
52 | annoying to open a plot and then have to move your fingers away from
53 | the keyboard to close the plot. 
54 | 


--------------------------------------------------------------------------------
/revised/Python/py_stats_analysis.py:
--------------------------------------------------------------------------------
  1 | """
  2 | This is an experiment to see how easily an analysis
  3 | done in R could be done in Python. The rcode is in a
  4 | companion file. The comments are cut and pasted from
  5 | the R code, and similar variable names are used as
  6 | much as possible to make comparisons easier.
  7 | """
  8 | 
  9 | #An example analysis of a fairly simple dataset.
 10 | #The data is from the 2010 CPS March supplement.
 11 | #(Also called the Annual Social and Economic Supplement.)
 12 | #The goal is examining the difference in hourly pay
 13 | #between males and females. The column A_ERNLWT is
 14 | #survey weights, PTFT is part-time/full-time status,
 15 | #educ is education level, ind is what industry the
 16 | #person works in and occ is what occupation they work in.
 17 | #A full description of most of the variables sometimes with
 18 | #slightly different names, can be found at
 19 | #http://www.census.gov/apsd/techdoc/cps/cpsmar10.pdf
 20 | 
 21 | 
 22 | 
 23 | import numpy as np
 24 | import scikits.statsmodels.api as sm
 25 | import matplotlib.pyplot as plt
 26 | import scipy.stats as sps
 27 | 
 28 | dat = np.genfromtxt('marchCPS_2010.txt', names=True, dtype=None,
 29 |                         missing_values='NA')
 30 | 
 31 | print dat.shape
 32 | print dat.dtype
 33 | print len(dat.dtype)
 34 | 
 35 | 
 36 | ####Actual analysis code
 37 | 
 38 | #Remove everyone that doesn't have an houlry wage data
 39 | 
 40 | 
 41 | 
 42 | indhr = ~np.isnan(dat['hrwage'])
 43 | hrdat = dat[indhr]
 44 | 
 45 | 
 46 | #Remove several people who just didn't fit the models (using the
 47 | #standard model checking techniques below)
 48 | 
 49 | 
 50 | hrdat = np.delete(hrdat, [407, 515, 852, 1197])
 51 | 
 52 | 
 53 | indf = np.flatnonzero(dat['sex'] == 2)
 54 | indm = np.flatnonzero(dat['sex'] == 1)
 55 | 
 56 | print len(indf)
 57 | print len(indm)
 58 | 
 59 | #With each of these models, typically do some
 60 | #commands to look more at the models, like summary(),
 61 | #, anova for the model on its own or betwen two models to see
 62 | #how much additional explantory power you get with the added
 63 | #variables, and plots to look at residuals, qqplot, and hist of residuals
 64 | #Currently can't do anova or lowess in python, and the qqplots are annoying
 65 | #to make.
 66 | 
 67 | 
 68 | #Initial model, only look at log(hrwage)~sex
 69 | X1 = hrdat['sex']==2
 70 | X1 = sm.add_constant(X1, prepend=True)
 71 | model1 = sm.WLS(np.log(hrdat['hrwage']), X1, weights = hrdat['A_ERNLWT'])
 72 | results1 = model1.fit()
 73 | 
 74 | print results1.summary()
 75 | 
 76 | 
 77 | #Pre-defining model matrix components for more complicated models
 78 | #dat_mat is DATa model MATtrices
 79 | n = len(hrdat)
 80 | dat_mat = {}
 81 | dat_names = {}
 82 | factor_vars = ['sex', 'educ', 'PTFT', 'ind', 'occ', 'marstat',
 83 |             'GEDIV', 'race', 'hispanic', 'disabled']
 84 | for name in factor_vars:
 85 |     dat_mat[name],dat_names[name] = sm.categorical(hrdat[name],
 86 |                                                    dictnames=True)
 87 |     dat_mat[name] = dat_mat[name][:,2:]
 88 | dat_mat['age'] = hrdat['age'].reshape(n,1)
 89 | dat_mat['age^2'] = (hrdat['age']**2).reshape(n,1)
 90 | dat_mat['const'] = np.ones((n,1))
 91 | dat_names['age'] = ['age']
 92 | dat_names['age^2'] = ['age^2']
 93 | dat_names['const'] = ['const']
 94 | 
 95 | for name in factor_vars:
 96 |     fact_names = sorted(dat_names[name].values())[1:]
 97 |     dat_names[name] = [''.join([name, str(val)]) for
 98 |                             val in fact_names]
 99 | 
100 | #helper function to spit out design matrix and names
101 | 
102 | def get_mat(var_list):
103 |     terms = map(dat_mat.get, var_list)
104 |     mat = np.hstack(tuple(terms))
105 | 
106 |     return mat
107 | 
108 | def get_names(var_list):
109 |     names = []
110 |     map(names.extend, map(dat_names.get, var_list))
111 | 
112 |     return names
113 | 
114 | #More complicated model, log(hrwage)~sex+educ+age+PTFT
115 | logwage = np.log(hrdat['hrwage'])
116 | w = hrdat['A_ERNLWT']
117 | 
118 | m2_vars = ['const', 'sex', 'educ', 'age', 'PTFT']
119 | 
120 | X2 = get_mat(m2_vars)
121 | m2_names = get_names(m2_vars)
122 | 
123 | model2 = sm.WLS(logwage, X2, weights = w)
124 | results2 = model2.fit()
125 | 
126 | print results2.summary(xname=m2_names)
127 | 
128 | 
129 | 
130 | #Now include ind and occ (industry and occupation codings)
131 | 
132 | m2_5_vars = list(m2_vars)
133 | m2_5_vars.extend(['ind','occ'])
134 | m2_5_vars.insert(4, 'age^2')
135 | 
136 | X2_5 = get_mat(m2_5_vars)
137 | m2_5_names = get_names(m2_5_vars)
138 | 
139 | model2_5 = sm.WLS(logwage, X2_5, weights=w)
140 | results2_5 = model2_5.fit()
141 | 
142 | print results2_5.summary(xname=m2_5_names)
143 | 
144 | 
145 | #Residual diagnostics for model2_5
146 | 
147 | plt.subplot(1,3,1)
148 | plt.hist(results2_5.resid, 30, normed=1, facecolor='green', alpha=0.75)
149 | plt.title('Histogram of Residuals')
150 | plt.xlabel('Probability')
151 | plt.grid(True)
152 | 
153 | plt.subplot(1,3,2)
154 | plt.plot(results2_5.fittedvalues, results2_5.resid, 'bo')
155 | plt.title('Residuals vs. Fitted Values')
156 | plt.xlabel('Fitted Values')
157 | plt.ylabel('Residuals')
158 | plt.axhline(lw=3, color = 'r')
159 | plt.grid(True)
160 | 
161 | plt.subplot(1,3,3)
162 | normal_rv_q = sps.norm.ppf(np.linspace(0,1,n+1)[1:])
163 | ordered_resid = np.copy(results2_5.resid)
164 | ordered_resid.sort()
165 | plt.plot(normal_rv_q, ordered_resid, 'ro')
166 | plt.title('Normal qq Plot')
167 | plt.xlabel('Theoretical Quantiles')
168 | plt.ylabel('Sample Quantiles')
169 | plt.grid(True)
170 | 
171 | 
172 | #Used mean replacement for all the people whose hours varied,
173 | #where the mean of 22 was the mean over all part time workers
174 | #w/o missing data and using their weights
175 | #Done just as a sanity check on the above models
176 | 
177 | 
178 | 
179 | #Throw in everything and the kitchen sink for reality check
180 | indic = np.flatnonzero(hrdat['PMHRUSLT']<=0)
181 | tmp = np.copy(hrdat['PMHRUSLT'])
182 | tmp[indic] = 22
183 | tmp = tmp.reshape(n,1)
184 | 
185 | m3_vars = ['const','sex', 'educ', 'PTFT', 'age', 'age^2',
186 |             'marstat', 'GEDIV', 'race', 'hispanic',
187 |             'tmp', 'disabled']
188 | dat_mat['tmp'] = tmp
189 | dat_names['tmp'] = ['Varying Hours']
190 | 
191 | X3 = get_mat(m3_vars)
192 | m3_names = get_names(m3_vars)
193 | 
194 | model3 = sm.WLS(logwage, X3, weights=w)
195 | results3 = model3.fit()
196 | 
197 | print results3.summary(xname=m3_names)
198 | 
199 | 
200 | #These models bin the works by age group, <=30, 31-40, 41-50, >50.
201 | #This was done to see what the difference betweens males and
202 | #females was in each bin. This was done as a sanity check for the
203 | #later exploratory analysis that fit lowess curves across age to
204 | #males and females seperately. I wanted to make sure that the trends
205 | #observed were real. (Where the gap was smaller for younger workers,
206 | #expanded for middle age workers, and then contracted again.)
207 | 
208 | 
209 | lt30 = (hrdat['age']<=30)
210 | btw30_40 = (np.logical_and(30 <hrdat['age'], hrdat['age']<=40))
211 | btw40_50 = (np.logical_and(40< hrdat['age'], hrdat['age']<=50))
212 | gt50 = (50<hrdat['age'])
213 | 
214 | #Earlier design matrix helper function doesn't apply because it
215 | #doesn't allow for taking indices
216 | #Earlier name helper function also doesn't apply because some of the factors
217 | #in the subset of data considered have fewer levels than in the full dataset
218 | def age_design(indices):
219 |   tmp = np.hstack((sm.categorical(hrdat['sex'][indices])[:,2:],
220 |                   sm.categorical(hrdat['educ'][indices])[:,2:],
221 |                   sm.categorical(hrdat['PTFT'][indices])[:,2:],
222 |                   hrdat['age'].reshape(n,1)[indices,:],
223 |                   (hrdat['age']**2).reshape(n,1)[indices,:]))
224 |   return sm.add_constant(tmp, prepend = True)
225 | 
226 | 
227 | def age_model(indices):
228 |   return sm.WLS(logwage[indices], age_design(indices), weights = w[indices])
229 | 
230 | model7_1 = age_model(lt30)
231 | model7_2 = age_model(btw30_40)
232 | model7_3 = age_model(btw40_50)
233 | model7_4 = age_model(gt50)
234 | 
235 | results7_1 = model7_1.fit()
236 | results7_2 = model7_2.fit()
237 | results7_3 = model7_3.fit()
238 | results7_4 = model7_4.fit()
239 | 
240 | print results7_1.summary()
241 | print results7_2.summary()
242 | print results7_3.summary()
243 | print results7_4.summary()
244 | 
245 | ####
246 | 
247 | 
248 | #This analysis switches gears and focuses on occupation.
249 | #The males and females are broken down by occupation and the
250 | #weighted mean of their hourly wage are compared.
251 | #(The weights used are again the survey weights.)
252 | #The matrix wocc stores all of that, and
253 | #Use xtable to format tables in latex
254 | 
255 | occs = sorted(set(hrdat['occ']))
256 | wocc = np.zeros( (len(occs), 4))
257 | for i,occ in enumerate(occs):
258 |   tmp_male = np.logical_and(hrdat['sex']==1, hrdat['occ'] == occ)
259 |   tmp_female = np.logical_and(hrdat['sex']==2, hrdat['occ'] == occ)
260 |   if np.any(tmp_male):
261 |     wocc[i, 0] = sum(hrdat['A_ERNLWT'][tmp_male])
262 |     wocc[i, 1] = np.average(hrdat['hrwage'][tmp_male],
263 |         weights = hrdat['A_ERNLWT'][tmp_male])
264 |   if np.any(tmp_female):
265 |     wocc[i, 2] = np.sum(hrdat['A_ERNLWT'][tmp_female])
266 |     wocc[i, 3] = np.average(hrdat['hrwage'][tmp_female],
267 |                               weights = hrdat['A_ERNLWT'][tmp_female])
268 | 
269 | print wocc
270 | 
271 | # In the next iteration of pandas the above computations can be done more
272 | # concisely and in a more readable manner.
273 | # The following example code showing this is due to Wes McKinney
274 | #
275 | # hrdf = pa.DataFrame(hrdat)
276 | # hrdf['sex'] = np.where(hrdf['sex'] == 1, 'male', 'female')
277 | #
278 | # def compute_stats(group):
279 | #     sum_weight = group['A_ERNLWT'].sum()
280 | #     wave_hrwage = (group['hrwage'] * group['A_ERNLWT']).sum()/sum_weight
281 | #     return Series({'sum_weight':sum_weight, 'wave_hrwage':wave_hrwage})
282 | #
283 | # pawocc = hrdf.groupby(['sex','occ']).apply(compute_stats)
284 | 
285 | 
286 | 
287 | #These were just some quick computations to make sure wocc had
288 | #the values I thought it had. Nothing more embarassing than
289 | #bad statistics because you didn't double-check your output.
290 | 
291 | males = hrdat['sex']==1
292 | females = hrdat['sex'] ==2
293 | 
294 | np.average(hrdat['hrwage'][males])
295 | np.average(hrdat['hrwage'][males],
296 |             weights = hrdat['A_ERNLWT'][males])
297 | sum(wocc[:,0]*wocc[:,1])/sum(wocc[:,0])
298 | np.average(hrdat['hrwage'][females],
299 |             weights = hrdat['A_ERNLWT'][females])
300 | tmp = np.delete(wocc, 5, axis=0)
301 | sum(tmp[:,0] * tmp[:,3]) / sum(tmp[:,0])
302 | 
303 | 
304 | """
305 | The final analysis using lowess can't be done in python (yet!)
306 | since there's no analogous function in the numpy/scipy stack. Alas.
307 | """
308 | 
309 | 
310 | 


--------------------------------------------------------------------------------
/revised/R/marchCPS_2010.txt:
--------------------------------------------------------------------------------
1 | PMERELG	clswrkr	GEDIV	A_ERNLWT	educ	marstat	sex	union	ERN_SRCE	ERN_VAL	WEWKRS	WEXP	WEMIND	WEIND	WEMOCG	POCCU2	occ	ind	PMHRUSLT	race	hispanic	age	disabled	HEFAMINC	PTFT	hrwage
1	1	1	0	39	1	2	0	1	21000	2	7	10	16	17	40	5	10	30	1	2	53	1	13	1	NA
1	2	1	1934.9	39	1	1	0	1	32500	2	7	6	8	17	40	5	6	30	1	2	47	2	14	1	15.15
1	3	1	1918.79	44	1	1	2	1	100000	1	1	8	11	1	1	1	8	40	1	2	62	2	16	2	48.075
1	1	1	0	39	7	2	0	1	10000	4	9	10	16	17	40	5	10	32	1	2	33	2	13	1	NA
1	1	1	0	40	7	1	0	1	59000	2	7	10	15	14	35	3	10	-4	1	2	20	2	12	1	NA
1	1	1	3135.77	39	7	1	2	1	50000	1	1	6	9	20	48	8	6	40	1	2	22	2	7	2	22
1	1	1	2416.22	40	7	2	2	1	9000	4	8	10	15	6	17	2	10	20	1	2	19	2	15	1	10
1	1	1	0	39	1	1	0	1	38002	1	1	10	16	20	48	8	10	40	1	2	39	2	11	2	NA
1	1	1	0	41	1	2	0	1	47000	1	1	10	16	11	28	3	10	46	1	2	46	2	15	2	NA
1	1	1	1819.79	39	5	2	2	1	19000	1	1	11	18	13	32	3	11	35	1	2	56	2	11	2	9.75
1	4	1	2514.63	43	1	1	0	1	34000	1	1	10	15	8	21	2	10	50	1	2	43	2	14	2	13.08
1	1	1	2612.15	46	1	1	2	1	15000	2	7	9	13	5	13	2	9	50	1	2	36	2	15	2	18.46
1	1	1	2795.79	43	1	1	2	1	48000	1	1	10	15	3	8	2	10	40	1	2	27	2	12	2	22.825
1	1	1	0	37	7	1	0	1	5500	4	11	5	7	17	40	5	5	20	1	2	18	2	13	1	NA
1	1	1	0	39	7	2	0	1	12000	4	9	10	16	11	28	3	10	32	1	2	20	2	8	1	NA
1	1	1	2105.09	40	1	2	2	1	26520	1	1	10	16	17	40	5	10	40	1	2	29	1	10	2	12.75
1	1	1	2194.59	35	7	2	2	1	23000	1	1	11	18	14	35	3	11	35	1	2	61	2	6	2	8.25
1	1	1	2461.05	40	7	2	2	1	18000	2	7	10	15	12	31	3	10	-4	1	2	20	2	15	1	7.65
1	1	1	2123.22	38	5	2	2	1	55000	1	1	5	7	16	38	4	5	40	1	2	46	2	15	2	5.625
1	1	1	1937.28	39	1	1	2	1	50000	1	1	5	7	16	38	4	5	-4	1	2	49	2	10	1	NA
1	1	1	2123.22	39	5	2	2	1	25000	1	1	11	18	13	32	3	11	48	1	2	47	2	5	2	11.6
1	1	1	0	39	7	2	0	1	21000	1	1	4	5	14	35	3	4	36	1	2	35	2	10	2	NA
1	1	1	1946.85	42	1	1	2	1	200000	1	1	4	4	16	39	4	4	52	1	2	61	2	15	2	22.173077
1	1	1	2493.97	40	7	2	2	1	14000	1	1	5	7	16	39	4	5	30	1	2	22	2	4	1	11.5
1	1	1	0	39	7	1	0	1	29000	1	1	2	2	19	42	7	3	40	1	2	35	2	9	2	NA
1	1	1	2615.36	43	1	1	2	1	65000	1	1	5	6	16	39	4	5	40	1	2	38	2	13	2	57.3
1	1	1	1921.15	40	1	1	2	1	15000	1	1	10	16	15	37	3	10	70	1	2	61	2	12	2	9
1	1	1	2517.72	44	1	1	0	1	105000	1	1	6	8	22	50	10	6	48	1	2	43	2	15	2	42.0625
1	4	1	2144.62	43	1	2	0	1	35000	1	1	10	15	8	21	2	10	40	1	2	32	2	14	2	16.825
1	1	1	0	40	5	1	0	1	52000	1	1	9	13	3	8	2	9	70	1	2	42	2	8	2	NA
1	1	1	0	44	1	1	0	1	409068	1	1	4	4	1	1	1	4	60	6	2	46	2	16	2	NA
1	1	1	0	40	7	2	0	1	5000	2	7	5	7	16	39	4	5	15	1	2	18	2	14	1	NA
1	1	1	2123.22	39	5	2	2	1	19000	1	1	10	16	10	27	2	10	40	1	2	46	2	14	2	37
1	1	1	2615.36	42	1	1	2	1	63000	1	1	10	16	3	8	2	10	45	1	2	39	2	14	2	26.933333
1	1	1	2105.09	43	1	2	2	1	32000	1	1	10	15	8	21	2	10	40	1	2	26	2	9	2	16
1	1	1	2615.36	44	1	1	2	1	100000	1	1	9	13	4	12	2	9	55	1	2	37	2	16	2	35.454545
1	1	1	1822.03	42	4	2	2	1	16000	1	1	10	16	14	35	3	10	40	1	2	56	2	6	2	8.75
1	1	1	1822.03	44	7	2	2	1	85000	1	1	10	16	8	21	2	10	53	1	2	58	2	14	2	30.849057
1	1	1	3139.63	39	7	1	2	0	0	5	13	15	23	24	53	4	5	40	1	2	21	2	15	2	20
1	1	1	0	39	1	1	0	1	52000	1	1	4	4	21	49	9	4	36	1	2	47	2	7	2	NA
1	4	1	2022.49	43	1	2	1	1	38000	3	3	10	15	8	21	2	10	21	1	2	29	2	12	1	29.52381
1	4	1	0	40	1	2	0	1	52000	1	1	13	21	1	3	1	13	40	1	2	45	2	14	2	NA
1	1	1	1965.83	39	1	1	2	1	26000	3	2	3	3	19	42	2	9	40	1	2	42	2	10	2	25.35
1	1	1	1958.34	39	1	1	2	1	56916	1	1	4	4	1	1	1	4	-4	1	2	49	2	15	1	NA
1	1	1	0	42	3	2	0	1	40000	2	7	10	16	10	25	2	10	30	1	2	38	2	11	1	NA
1	1	1	0	43	1	1	0	1	120000	1	1	9	13	1	1	1	9	50	1	2	32	2	16	2	NA
1	1	1	0	40	7	2	0	1	9000	1	1	10	16	11	28	3	12	45	1	2	24	2	16	2	NA
1	1	1	0	40	1	1	0	1	48000	1	1	5	6	22	51	10	5	50	1	2	48	2	12	2	NA
1	1	1	2414.73	43	7	2	2	0	0	5	13	15	23	24	53	2	10	25	1	2	23	2	11	1	13.46
1	4	1	1901.62	41	1	1	2	1	7000	4	11	6	8	19	46	7	6	-4	1	2	61	2	13	1	12.7
1	3	1	2246.05	44	1	2	0	1	65002	1	1	13	21	2	6	1	13	38	1	2	62	2	15	2	32.894737
1	1	1	0	35	1	1	0	1	50000	1	1	3	3	19	44	7	3	55	1	2	52	2	15	2	NA
1	1	1	2033.27	44	1	2	0	1	40000	3	3	10	15	8	21	2	10	40	1	2	33	2	15	2	25
1	1	1	1917.59	39	1	1	2	1	84000	1	1	4	4	21	49	9	4	48	1	2	49	2	16	2	25
1	1	1	2400.41	40	7	1	2	1	6000	3	4	4	4	22	51	10	4	15	1	2	20	2	13	1	12.42
1	1	1	2364.48	40	7	2	2	1	802	4	10	10	15	16	39	4	10	12	1	2	21	2	3	1	7.25
1	1	1	2140.15	43	7	1	2	1	50000	1	1	5	7	16	39	4	5	40	1	2	25	2	13	2	12.5
1	1	1	2400.41	37	7	1	2	1	6000	2	7	5	7	16	39	4	5	-4	1	2	21	2	14	1	9.8
1	1	1	0	44	6	2	0	1	42002	1	1	10	16	10	25	2	10	40	1	2	54	2	11	2	NA
1	1	1	0	39	1	2	0	1	24000	1	1	4	4	21	49	9	4	40	6	2	55	2	8	2	NA
1	1	1	2022.36	43	1	1	2	1	65335	3	3	9	14	2	6	1	9	40	1	2	56	2	15	2	25
1	1	1	2000.25	39	5	1	2	1	30000	1	1	3	3	19	44	7	3	40	1	2	50	2	13	2	26
1	1	1	1930.47	40	1	1	2	1	34000	1	1	10	15	17	40	5	4	40	1	2	40	2	10	2	12.925
1	1	1	2028.32	43	3	2	2	1	6000	3	5	12	20	17	40	5	12	40	1	2	26	2	4	2	5.75
1	1	1	2042.07	44	1	1	2	1	110000	1	1	9	13	3	8	2	9	50	1	2	38	2	16	2	46.92
1	1	1	2179.26	42	7	2	2	1	42000	1	1	4	4	22	50	10	4	40	1	2	45	1	11	2	22
1	1	1	0	40	1	1	0	1	62000	1	1	6	8	22	51	10	6	-4	1	2	41	2	14	1	NA
1	4	1	0	39	1	1	0	1	26000	1	1	10	15	14	35	3	10	40	1	2	34	2	15	2	NA
1	1	1	1397.98	39	5	2	2	1	42000	1	1	4	5	17	40	5	4	42	1	2	37	2	14	2	22.07
1	1	1	0	43	1	1	0	1	70000	1	1	4	4	20	48	8	4	50	1	2	51	2	15	2	NA
1	1	1	1243.79	40	1	1	2	1	41600	1	1	3	3	19	45	7	3	40	1	2	45	2	12	2	19.5
1	1	1	1454.33	39	1	2	2	1	40000	1	1	4	4	21	49	9	4	40	1	2	32	2	11	2	9.8
1	1	1	0	39	7	2	0	2	20782	1	1	10	16	15	37	3	10	99	1	2	49	2	7	2	NA
1	1	1	0	42	1	2	0	1	60000	1	1	10	16	10	25	2	10	48	1	2	38	2	12	2	NA
1	1	1	1329.71	43	1	2	2	1	2000	4	9	11	17	15	37	3	11	3	1	2	42	2	15	1	25
1	1	1	1827.4	40	7	1	2	1	3600	4	11	9	14	14	35	3	11	28	6	2	20	2	14	1	4.5
1	1	1	1373.51	42	7	2	2	1	62379	1	1	4	4	4	12	2	4	40	1	2	53	2	14	2	15.28
1	1	1	1220.38	39	5	2	2	1	38500	1	1	10	16	1	3	1	10	40	1	2	55	2	14	2	20
1	1	1	1304.88	39	5	1	2	1	47840	1	1	10	16	12	29	3	10	40	1	2	56	2	14	2	23
1	4	1	0	44	1	2	0	1	35000	2	7	10	15	8	21	2	10	40	1	2	48	2	16	2	NA
1	1	1	0	43	1	1	0	1	75000	1	1	4	4	4	12	2	4	60	1	2	31	2	15	2	NA
1	1	1	1477.46	39	1	2	2	1	2711	4	11	10	16	15	37	3	10	-4	1	2	32	2	11	1	4.65
1	1	1	1352	39	1	2	2	1	15000	1	1	11	18	13	32	3	10	55	1	2	50	2	12	2	14.672727
1	1	1	1691.23	40	7	2	2	1	500	4	12	11	17	13	33	2	12	-4	1	2	21	2	9	1	8.25
1	1	1	1352	39	1	2	2	1	40000	1	1	5	7	16	38	4	5	55	1	2	50	2	15	2	13.981818
1	1	1	1308.89	43	1	2	2	1	65000	1	1	9	13	1	1	1	9	40	1	2	40	2	14	2	31.25
1	1	1	1201.27	39	5	2	2	1	24880	3	4	10	16	17	40	5	10	-4	1	2	55	2	10	1	7.25
1	1	1	0	39	1	1	0	1	40000	3	4	2	2	19	47	7	3	40	1	2	36	2	14	2	NA
1	1	1	1502.93	39	1	1	2	1	20000	1	1	9	14	12	31	3	9	40	1	2	37	1	13	2	11
1	1	1	14767.37	41	1	2	2	1	65000	1	1	10	16	17	40	5	10	40	1	2	61	2	15	2	10
1	1	1	16656.63	41	7	2	2	1	3500	4	11	10	16	17	40	5	10	23	1	2	24	2	13	1	12
1	1	1	0	39	7	1	0	1	7000	1	1	4	5	22	51	10	4	35	1	1	20	2	3	2	NA
1	1	1	0	39	1	2	0	1	54920	2	7	8	11	2	5	1	8	-4	1	2	45	2	16	1	NA
1	1	1	12260.42	40	5	2	2	1	34000	1	1	10	16	17	40	5	10	61	1	2	57	2	11	2	15
1	1	1	16772.25	43	1	1	0	1	70000	1	1	7	10	9	23	2	11	40	1	2	39	2	11	2	24.025
1	1	1	13123.47	43	7	1	2	1	108000	1	1	4	4	5	14	2	4	40	1	2	53	1	15	2	51.925
1	1	1	0	44	1	1	0	1	115000	1	1	4	4	3	8	2	4	40	4	2	47	2	16	2	NA
1	1	1	0	44	1	2	0	1	40000	1	1	5	7	17	40	5	5	40	1	2	49	2	16	2	NA
1	1	1	12891.05	39	5	2	2	1	29000	1	1	5	7	17	40	5	5	35	1	2	41	2	8	2	15.2
1	1	1	13798.84	39	1	2	2	1	45000	1	1	8	11	2	7	1	8	40	1	2	54	2	16	2	13.45
1	1	1	16196.44	40	7	1	2	1	28000	2	7	5	7	16	39	4	5	32	2	2	32	2	7	1	10.4
1	1	1	15520.82	33	7	2	2	1	14000	2	7	4	5	16	39	4	4	27	2	1	41	2	4	1	7.5
1	1	1	12631.61	41	1	1	2	1	36000	3	3	11	18	13	32	3	11	40	4	2	48	2	12	2	17.67
1	4	1	0	44	1	1	0	1	170000	1	1	13	21	12	30	3	13	80	1	2	32	2	16	2	NA
1	1	1	0	39	1	1	0	1	33000	1	1	4	4	16	39	4	4	50	1	2	40	2	11	2	NA
1	1	1	12134.34	39	5	1	2	1	120000	1	1	9	13	16	39	4	9	40	1	2	57	2	16	2	57.7
1	1	1	15477.39	39	1	1	0	1	91000	1	1	10	16	20	48	8	10	40	1	2	42	2	11	2	25.5
1	4	1	0	44	1	2	0	1	64000	1	1	10	15	5	15	2	10	40	1	2	36	2	16	2	NA
1	1	1	12134.34	39	5	1	2	1	35000	1	1	4	4	20	48	8	9	45	1	2	55	2	13	2	23.488889
1	1	1	13779.48	39	1	2	2	1	16612	2	7	5	7	16	38	4	5	24	1	2	45	2	14	1	13.6
1	1	1	0	42	1	2	0	1	37000	1	1	10	16	1	3	1	10	40	1	1	46	2	11	2	NA
1	1	1	0	39	3	2	0	1	12500	2	7	12	20	16	39	4	12	30	1	2	46	2	13	1	NA
1	1	1	18300.98	40	7	1	2	1	5408	2	7	11	18	13	33	3	11	25	1	2	21	2	12	1	7.5
1	4	1	13798.84	41	5	2	2	1	21672	3	5	10	15	8	21	2	10	35	1	2	53	2	15	2	17.2
1	1	1	13268.52	43	5	1	2	1	46434	1	1	8	11	1	1	1	8	40	1	2	55	2	11	2	23.175
1	1	1	10047.09	39	1	2	2	0	0	5	13	15	23	24	53	9	9	-4	1	1	42	2	11	1	9.5
1	1	1	0	39	1	2	0	1	43967	1	1	4	4	17	40	5	4	40	1	2	41	2	13	2	NA
1	1	1	1525.03	39	1	1	0	1	60000	1	1	4	4	2	5	1	4	40	1	2	60	2	13	2	30
1	1	1	0	35	1	1	0	1	12000	2	7	11	18	13	33	3	11	25	1	1	50	2	4	1	NA
1	1	1	2036.87	45	1	2	2	1	60000	2	7	10	16	10	25	2	10	28	1	2	61	2	14	1	25
1	1	1	2191.9	40	1	2	2	1	32000	1	1	10	15	1	1	1	10	37	1	2	40	2	15	2	16.621622
1	1	1	2422.53	37	7	2	2	1	5500	2	7	11	18	13	33	3	11	20	1	2	27	2	13	1	11.33
1	1	1	2012.81	43	5	1	2	1	25000	1	1	4	5	21	49	9	4	40	1	2	32	2	12	2	12.5
1	1	1	1537.84	40	1	1	0	1	60000	1	1	6	8	22	51	10	6	40	1	2	49	2	13	2	19
1	3	1	2832.17	42	7	2	2	1	10000	2	7	11	17	13	33	3	11	35	1	2	23	2	14	2	8
1	1	1	1760.55	39	1	2	2	1	20000	2	7	6	8	22	51	10	6	25	1	2	63	2	12	1	16
1	1	1	1537.84	44	1	1	2	1	110000	1	1	9	13	3	8	2	9	45	1	2	45	2	15	2	29.911111
1	1	1	2370.04	43	7	2	2	1	37000	1	1	5	7	1	1	1	5	50	1	2	25	2	10	2	14.24
1	1	1	1681.14	46	1	1	2	1	409068	1	1	10	16	10	24	2	10	46	4	2	51	2	16	2	55.47
1	1	1	1691.09	43	1	2	2	1	106201	1	1	10	16	10	25	2	10	50	1	2	57	2	14	2	37
1	4	1	1525.03	40	1	1	2	1	5460	2	7	13	21	19	46	7	13	3	1	2	61	2	15	1	35
1	1	1	2191.9	42	5	2	2	1	69680	1	1	11	17	2	6	1	11	45	1	2	40	2	13	2	30.8
1	1	1	2107.28	43	7	1	2	1	33000	1	1	10	15	9	23	2	10	40	1	2	28	2	13	2	18.62
1	1	1	0	37	7	2	0	1	4942	2	7	11	18	13	32	3	11	17	1	2	20	2	15	1	NA
1	1	1	0	40	1	1	0	1	49000	1	1	5	6	16	38	4	5	40	1	2	36	2	13	2	NA
1	1	1	1588.11	40	1	1	2	1	110000	1	1	4	4	21	49	9	4	60	1	2	55	2	15	2	19.5
1	1	1	2036.87	44	1	2	2	1	49500	3	3	10	15	8	21	2	10	60	1	2	61	2	15	2	19.65
1	1	1	1588.11	40	1	1	2	1	34000	1	1	5	7	16	38	4	5	60	1	2	59	2	13	2	18
1	1	1	0	41	1	1	0	1	65000	1	1	11	18	13	32	3	11	60	1	2	41	2	14	2	NA
1	1	1	4184.47	44	1	1	2	1	115000	1	1	4	4	1	1	1	4	40	1	2	57	2	15	2	55.3
1	1	1	3935.28	39	1	1	2	1	60000	1	1	5	7	16	39	4	5	50	1	2	60	2	14	2	13.1
1	1	1	4490.19	42	7	2	2	1	65000	1	1	10	16	3	8	2	10	45	1	2	48	2	13	2	30
1	1	1	4349.17	45	5	2	2	1	50000	1	1	9	13	7	18	2	9	40	1	2	38	2	15	2	24.025
1	1	1	0	39	1	1	0	1	80000	1	1	4	4	20	48	8	4	40	1	2	59	2	15	2	NA
1	1	1	0	44	1	2	0	1	58000	1	1	10	15	3	8	2	10	35	1	2	40	2	15	2	NA
1	1	1	4338.11	43	1	1	2	1	45002	1	1	7	10	4	12	2	7	40	4	2	51	2	14	2	21.57
1	1	1	3968.32	45	7	1	2	1	100000	1	1	10	16	10	24	2	10	35	1	2	48	2	16	2	54.942857
1	1	1	3935.28	43	1	1	2	1	48000	3	2	4	4	4	12	2	4	40	1	2	61	2	14	2	67.3
1	4	1	4500.49	44	7	2	0	1	42000	3	3	10	15	8	21	2	10	40	1	2	27	2	14	2	26.25
1	1	1	0	33	3	1	0	1	15600	1	1	11	18	13	32	3	11	-4	1	1	33	2	11	1	NA
1	1	1	0	33	5	2	0	1	24000	1	1	11	18	13	32	3	11	28	1	1	37	2	11	1	NA
1	1	1	0	39	7	2	0	1	7000	4	9	5	7	16	39	4	11	40	1	2	19	2	15	2	NA
1	1	1	0	39	5	2	0	1	8000	2	7	11	18	13	32	3	11	14	2	2	49	2	4	1	NA
1	1	1	0	43	1	1	0	1	30000	3	4	10	15	8	21	2	10	60	1	2	31	2	12	2	NA
1	1	1	0	40	7	2	0	0	0	5	13	15	23	24	53	3	5	18	1	2	18	2	13	1	NA
1	1	1	0	43	1	1	0	1	110000	1	1	8	11	3	8	2	8	45	1	2	59	2	15	2	NA
1	1	1	0	41	1	2	0	1	105000	1	1	8	12	16	39	4	8	40	1	2	40	2	14	2	NA
1	1	1	0	39	1	1	0	1	60000	3	3	3	3	19	47	7	3	40	1	2	39	2	15	2	NA
1	1	1	4247.18	43	1	2	0	1	100000	1	1	10	16	6	17	2	10	60	1	2	53	2	15	2	32.05
1	1	1	3968.32	43	1	1	0	1	72000	1	1	4	4	20	48	8	4	50	1	2	48	2	14	2	29
1	1	1	0	39	7	2	0	1	12000	4	10	5	7	22	51	10	5	15	1	2	19	1	14	1	NA
1	1	1	0	43	1	1	0	1	88000	1	1	9	13	17	40	5	9	40	1	2	40	2	14	2	NA
1	1	1	3972.4	43	1	1	2	1	72000	1	1	4	4	5	13	2	4	40	1	2	35	2	13	2	34.625
1	4	1	4072.03	44	1	2	0	1	60000	1	1	10	15	8	21	2	10	40	1	2	43	2	15	2	30.3
1	1	1	4397.47	39	5	2	2	1	35000	1	1	4	4	22	51	10	4	40	1	2	49	2	11	2	12.17
1	1	1	3968.32	39	1	1	2	1	32000	1	1	5	7	17	40	5	5	55	1	2	47	2	14	2	15
1	1	1	0	43	7	1	0	1	65002	1	1	7	10	1	1	1	7	40	2	2	29	2	15	2	NA
1	1	1	0	39	7	2	0	1	13433	2	7	5	7	22	51	10	5	20	2	2	37	1	3	1	NA
1	1	1	0	39	5	2	0	1	35000	1	1	11	17	15	36	3	11	50	1	2	43	2	15	2	NA
1	1	1	0	39	1	2	0	1	19000	1	1	4	4	21	49	9	4	40	4	2	44	2	8	2	NA
1	1	1	0	40	7	2	0	1	1200	4	12	10	15	13	33	3	10	10	4	2	21	2	8	1	NA
1	2	1	3968.32	44	1	1	2	1	47000	1	1	14	22	23	52	1	10	40	1	2	49	2	16	2	26.425
1	1	1	4247.18	46	1	2	2	1	115000	1	1	13	21	7	18	2	4	40	1	2	51	2	16	2	36.05
1	1	1	0	42	1	2	0	1	39000	1	1	11	17	15	37	3	11	40	4	2	53	2	11	2	NA
1	1	1	0	42	1	1	0	1	17500	1	1	11	17	14	35	3	11	40	4	2	55	2	11	2	NA
1	4	1	0	40	5	2	0	1	51000	1	1	11	17	1	3	1	11	50	1	2	35	2	12	2	NA
1	1	1	4152.71	44	1	1	2	1	100000	1	1	5	6	17	40	5	5	65	1	2	57	2	14	2	29.584615
1	1	1	5331.66	40	7	2	2	1	2000	3	5	8	11	17	40	5	8	8	1	2	22	2	14	1	10
1	4	1	4021.24	40	5	1	0	1	75000	1	1	3	3	1	2	1	3	40	1	2	48	2	13	2	9
1	3	1	5331.66	40	7	2	2	1	8000	2	7	8	11	17	40	5	10	10	1	2	22	2	13	1	10
1	1	2	13635.98	40	1	2	2	1	21000	2	7	6	8	17	40	5	6	20	1	2	28	2	13	1	9.75
1	4	2	12895.18	40	1	1	0	1	75000	1	1	13	21	12	30	3	13	40	1	2	50	2	15	2	36.05
1	1	2	0	39	7	1	0	1	1000	2	7	12	20	14	35	3	12	4	1	2	22	1	5	1	NA
1	3	2	12952.48	42	5	2	0	1	70000	1	1	13	21	3	8	2	13	38	1	2	62	2	14	2	35.421053
1	1	2	12786.89	39	1	1	0	1	50000	1	1	3	3	19	42	7	3	40	1	2	48	2	15	2	24.05
1	1	2	15661.06	40	7	1	2	1	3000	2	7	11	18	13	33	3	11	8	1	2	20	2	15	1	9
1	1	2	12531.25	41	1	2	2	1	20000	2	7	10	16	10	25	2	10	30	1	2	48	2	15	1	12.833333
1	1	2	13810.89	45	7	2	2	1	80000	1	1	10	16	10	24	2	10	40	1	2	28	2	14	2	38.45
1	1	2	13810.89	43	1	2	2	1	50000	1	1	10	15	8	21	2	10	40	1	2	27	2	12	2	27.4
1	3	2	12845.26	45	1	1	2	1	151200	1	1	13	21	7	18	2	13	45	1	2	63	2	16	2	50
1	1	2	14070.2	41	7	1	2	1	40000	1	1	8	11	2	6	1	8	40	4	2	27	2	16	2	18
1	3	2	0	44	1	2	0	1	30000	1	1	13	21	6	17	2	10	50	2	2	56	2	16	2	NA
1	2	2	13045.92	39	7	1	0	1	25000	1	1	13	21	12	31	3	13	50	1	2	39	2	11	2	35
1	1	2	12102.98	39	5	2	2	1	45000	1	1	8	12	16	39	4	8	40	1	2	51	1	7	2	38.45
1	1	2	12496.04	40	1	2	2	1	35000	1	1	9	13	7	19	2	9	35	1	2	42	2	13	2	19.657143
1	1	2	13177.49	40	7	2	0	1	50000	1	1	4	4	20	48	8	4	40	1	2	39	2	12	2	20.93
1	1	2	13177.49	39	1	2	2	1	8200	2	7	5	7	16	39	4	5	25	1	2	36	2	12	1	7.85
1	3	2	15670.61	40	7	2	2	1	5000	4	11	10	15	17	40	5	10	8	1	2	19	2	13	1	7.5
1	1	2	0	39	1	1	0	1	85000	3	4	4	4	20	48	7	6	40	1	2	55	2	16	2	NA
1	1	2	12080.87	43	7	2	0	1	70000	1	1	10	16	10	25	2	10	40	2	2	44	2	15	2	33.65
1	1	2	16911.13	40	5	2	2	1	10000	2	7	12	20	15	37	3	12	20	1	1	39	2	9	1	7.5
1	1	2	13382.4	39	1	2	0	1	1900	4	12	6	9	17	40	5	6	20	1	2	47	1	9	1	35
1	1	2	0	43	1	2	0	1	44000	1	1	10	16	3	9	2	10	60	2	2	51	2	9	2	NA
1	4	2	0	40	7	1	0	1	20000	1	1	6	8	22	51	10	6	40	2	2	46	2	10	2	NA
1	1	2	12040.26	40	3	2	2	1	40000	1	1	10	15	8	22	2	10	40	2	2	33	2	10	2	14.1
1	2	2	16537.14	44	7	1	0	1	53000	1	1	10	15	8	21	2	10	40	1	2	27	2	15	2	19.225
1	3	2	15249.69	43	1	2	2	1	35000	1	1	13	21	17	40	5	13	40	1	2	27	2	9	2	5.75
1	1	2	11028.4	43	1	2	2	1	100000	1	1	4	4	3	8	2	4	40	4	2	57	2	15	2	50
1	1	2	11484.03	43	6	2	2	1	31200	1	1	10	16	11	28	3	10	40	1	2	55	2	11	2	15
1	1	2	0	39	6	2	0	1	17000	1	1	5	7	17	40	5	5	40	2	1	52	2	12	2	NA
1	1	2	0	43	1	1	0	1	29000	1	1	5	7	17	40	5	5	40	1	1	50	2	14	2	NA
1	1	2	0	36	6	2	0	1	42000	1	1	11	17	14	35	3	11	40	2	2	41	2	9	2	NA
1	1	2	0	43	7	2	0	1	25000	1	1	7	10	9	23	2	7	50	1	2	42	2	15	2	NA
1	1	2	14303.09	39	1	1	2	1	18002	1	1	6	8	17	40	5	6	40	1	1	28	2	3	2	10
1	1	2	13843.12	39	7	1	2	1	20800	1	1	9	14	12	31	4	5	30	1	1	22	2	8	1	10
1	4	2	14399.89	43	1	1	0	1	76000	1	1	13	21	12	30	3	13	40	1	2	29	2	13	2	8.15
1	1	2	12481.61	39	5	2	2	1	53790	1	1	9	13	17	40	5	9	40	1	2	50	2	12	2	25.41
1	1	2	12923.28	39	1	2	0	1	30000	1	1	10	16	10	27	2	10	40	1	2	46	2	15	2	20.13
1	1	2	15119.15	39	1	1	2	1	65000	1	1	5	6	17	40	5	5	40	4	2	55	2	14	2	25.475
1	1	2	13077.1	43	5	1	2	1	108000	1	1	8	11	16	38	4	8	35	1	2	61	2	14	2	10.971429
1	1	2	0	44	1	2	0	0	0	5	13	15	23	24	53	2	12	0	1	2	33	2	16	1	NA
1	1	2	12193.17	39	7	2	2	1	40000	1	1	10	16	11	28	3	10	35	1	1	52	2	11	2	28.92
1	1	2	16537.14	43	7	1	2	1	120000	1	1	8	12	2	6	1	8	60	1	2	27	2	14	2	38.466667
1	1	2	16911.13	43	7	2	2	1	45000	1	1	7	10	16	39	4	7	40	1	1	37	2	15	2	21.625
1	1	2	12508.05	42	1	1	2	1	5000	1	1	5	7	16	39	4	5	45	4	2	24	2	13	2	7.688889
1	1	2	12361.37	40	1	2	2	1	20000	2	7	6	8	22	51	10	6	20	1	1	57	2	14	1	12
1	4	2	12193.17	39	1	2	2	1	20000	1	1	5	6	17	40	5	13	40	1	1	51	2	14	2	11.25
1	1	2	17903.56	35	6	1	2	1	18000	1	1	11	18	13	33	3	11	40	1	1	44	2	4	2	8.75
1	1	2	17525.1	40	7	2	2	1	8000	3	3	11	18	1	3	1	11	40	1	2	19	2	6	2	16.325
1	4	2	13797.86	44	1	2	0	1	85000	1	1	10	15	8	21	2	10	40	1	2	41	2	15	2	40.875
1	1	2	14736.93	43	7	2	1	1	31200	1	1	8	12	2	6	1	8	40	1	2	35	2	12	2	15
1	3	2	0	39	1	2	0	1	22000	3	2	11	18	13	33	3	10	35	2	2	52	2	8	2	NA
1	4	2	0	42	1	2	0	1	40000	1	1	13	21	17	40	5	13	45	1	1	38	2	13	2	NA
1	1	2	0	43	1	1	0	1	95000	1	1	11	18	13	33	3	11	45	4	2	41	2	12	2	NA
1	1	2	0	39	7	2	0	1	1200	2	7	5	7	16	38	4	5	25	2	2	20	2	14	1	NA
1	1	2	0	44	1	2	0	1	82500	1	1	10	16	10	25	2	10	74	2	2	33	2	16	2	NA
1	1	2	0	39	1	2	0	1	16000	1	1	11	18	17	40	5	11	40	4	2	57	2	15	2	NA
1	1	2	0	39	1	1	0	1	65000	1	1	8	12	1	3	1	8	40	1	2	47	2	13	2	NA
1	1	2	0	39	1	1	0	1	20000	1	1	11	18	14	35	3	11	38	4	2	46	2	12	2	NA
1	1	2	0	39	5	2	0	1	36400	1	1	10	16	10	27	2	10	37	2	2	42	2	12	2	NA
1	1	2	0	39	1	1	0	1	12382	4	11	12	20	22	51	10	12	30	4	2	50	2	9	1	NA
1	1	2	0	44	1	1	0	1	150000	1	1	9	13	1	1	1	9	45	1	2	46	2	13	2	NA
1	4	2	13832.23	44	4	2	0	1	93000	1	1	10	15	8	21	2	10	46	1	2	61	2	16	2	38.869565
1	4	2	17627	40	1	1	0	1	50000	1	1	6	8	20	48	8	6	40	1	1	53	2	16	2	24.05
1	1	2	0	43	1	2	0	1	50002	1	1	10	16	10	25	2	10	40	2	2	39	2	15	2	NA
1	1	2	0	43	5	2	0	1	50000	1	1	5	7	17	40	2	10	40	1	2	34	2	13	2	NA
1	1	2	13186.91	45	1	1	2	1	150000	1	1	9	13	7	18	2	9	50	1	2	45	2	16	2	57.7
1	1	2	0	40	1	1	0	1	45000	1	1	9	14	17	40	5	9	40	2	2	62	2	13	2	NA
1	1	2	12886.96	39	1	2	2	1	58000	2	7	9	13	17	40	5	9	33	1	2	44	2	14	1	33.787879
1	1	2	12951.75	39	1	1	2	1	51002	1	1	5	7	16	38	4	5	55	1	2	44	2	14	2	18.527273
1	1	2	0	43	1	1	0	1	409068	1	1	8	12	16	39	4	8	75	1	2	45	2	16	2	NA
1	4	2	0	45	1	1	0	1	409068	1	1	9	13	7	18	2	13	40	1	2	54	2	16	2	NA
1	1	2	12923.28	43	1	2	0	1	65000	1	1	10	16	10	25	2	10	49	1	2	45	2	16	2	25.510204
1	4	2	13247.1	44	1	1	0	1	97000	1	1	10	15	6	17	2	10	35	1	2	63	2	15	2	53.285714
1	1	2	0	39	7	2	0	1	9600	2	7	10	16	6	17	2	10	20	2	2	18	2	3	1	NA
1	1	2	0	42	1	1	0	1	60000	1	1	3	3	1	1	1	3	55	1	2	39	2	16	2	NA
1	4	2	0	44	5	2	0	1	120000	1	1	10	15	8	21	2	10	40	1	2	44	2	15	2	NA
1	1	2	13077.1	42	1	1	0	1	100000	1	1	10	16	10	25	2	10	40	1	2	61	1	15	2	50
1	1	2	13469.18	35	5	1	2	1	35000	1	1	5	6	22	51	10	5	40	1	2	53	2	14	2	16.825
1	1	2	0	39	5	1	0	1	50000	1	1	9	13	17	40	5	9	36	1	1	63	2	11	2	NA
1	1	2	13963.08	39	7	1	2	1	90000	1	1	3	3	19	46	7	3	40	1	2	25	2	13	2	17.25
1	1	2	13810.89	45	7	2	2	1	60000	4	10	9	13	7	18	2	9	35	1	2	26	2	15	2	32.971429
1	1	2	10753.63	42	1	2	2	1	125000	1	1	10	16	10	25	2	10	40	1	2	58	2	12	2	23.5
1	3	2	10246.89	42	5	2	0	1	73000	1	1	13	21	2	6	1	13	35	6	2	58	2	13	2	40.114286
1	1	2	0	40	7	2	0	1	12000	2	7	11	18	13	33	3	11	25	1	2	18	2	14	1	NA
1	1	2	0	39	7	1	0	1	70000	1	1	5	6	1	2	1	5	40	1	2	44	2	14	2	NA
1	1	2	13045.92	44	6	1	2	1	60002	1	1	4	5	3	8	2	10	40	1	2	37	2	14	2	15
1	4	2	16571.53	39	1	2	0	1	12000	2	7	10	16	15	37	3	10	26	2	2	57	2	6	1	9.5
1	3	2	12531.25	42	5	2	2	1	85000	1	1	10	16	10	25	2	10	40	1	2	45	2	13	2	20.13
1	1	2	12680.41	40	1	1	2	1	14621	3	5	5	6	20	48	8	5	40	1	2	60	2	14	2	20
1	1	2	0	39	1	2	0	1	33000	1	1	5	7	17	40	5	5	45	1	2	50	2	14	2	NA
1	1	2	0	44	7	1	0	1	1500	2	7	10	15	8	21	2	10	30	1	2	24	2	14	1	NA
1	1	2	16146.24	43	7	2	2	1	10908	3	4	9	14	17	40	5	9	35	1	2	22	2	6	2	9.5
1	4	2	0	45	1	2	0	1	80000	1	1	13	21	7	18	2	13	40	1	2	44	2	15	2	NA
1	1	2	0	40	7	1	0	1	5000	4	11	11	17	15	37	3	11	8	1	2	22	2	14	1	NA
1	1	2	8739.25	40	7	2	2	1	10000	2	7	5	7	17	40	5	5	25	1	1	19	1	6	1	9
1	3	2	13388.07	45	4	1	2	1	200000	1	1	9	13	7	18	2	13	40	1	2	59	2	16	2	72.125
1	1	2	10908.61	39	7	1	2	1	52000	1	1	5	7	16	38	4	5	40	1	2	31	2	14	2	25
1	1	2	12405.83	43	1	1	2	1	50000	1	1	7	10	20	48	8	7	40	4	2	30	2	15	2	23.75
1	1	2	11229.61	43	1	2	2	1	90000	1	1	9	13	1	1	1	9	40	6	2	27	2	15	2	40.375
1	1	2	10864.65	39	1	2	2	1	13000	1	1	8	12	22	51	10	8	36	1	1	31	2	9	2	8.24
1	1	2	0	40	3	1	0	1	18000	1	1	10	16	15	37	3	11	-4	3	2	21	2	15	1	NA
1	1	2	0	39	1	1	0	1	70000	1	1	2	2	4	12	2	3	40	1	2	46	2	15	2	NA
1	1	2	9708.79	43	1	2	2	1	120000	1	1	9	13	16	39	4	9	35	1	2	41	2	13	2	65.942857
1	1	2	0	43	7	2	0	1	240	4	11	10	16	17	40	5	10	6	4	2	22	2	14	1	NA
1	1	2	14680.58	40	6	1	2	1	12000	3	5	3	3	19	43	7	3	-4	1	1	30	2	9	1	NA
1	1	2	11047.5	38	1	2	2	1	23700	3	2	13	21	17	40	5	4	40	1	1	25	2	2	2	8
1	2	2	0	40	1	1	0	1	40000	1	1	6	8	17	40	5	6	40	1	1	57	2	12	2	NA
1	3	2	0	44	5	2	0	1	50000	1	1	10	15	8	21	2	10	40	4	2	32	2	14	2	NA
1	1	2	12697.48	43	1	2	2	1	47593	1	1	5	7	16	38	4	5	40	1	1	50	2	9	2	11
1	1	2	12685.73	43	1	1	2	1	45000	1	1	9	13	1	1	1	9	50	1	2	49	2	15	2	57.68
1	1	2	13636.53	44	1	2	2	1	15600	2	7	10	15	17	40	5	10	12	1	2	49	1	15	1	13.02
1	1	2	9672.85	44	1	1	2	1	85000	1	1	8	11	3	8	2	8	40	4	2	42	2	13	2	40.875
1	1	2	0	40	1	2	0	1	22620	2	7	5	6	9	23	2	5	30	1	2	29	2	13	1	NA
1	1	2	12544.81	40	7	2	0	1	35000	1	1	5	7	16	38	4	5	49	1	2	22	2	15	2	18
1	1	2	0	43	1	1	0	1	38400	3	4	4	5	16	39	4	4	40	1	2	48	2	15	2	NA
1	1	2	11959.01	41	1	2	2	1	49000	1	1	8	11	9	23	2	8	40	1	2	57	2	16	2	14
1	1	2	11673.55	40	1	1	2	1	15000	3	5	4	5	5	16	2	4	40	1	2	28	2	16	2	25.4
1	4	2	0	43	1	1	0	1	62000	1	1	10	15	8	21	2	10	52	1	2	44	2	13	2	NA
1	1	2	0	42	1	2	0	1	70000	1	1	9	13	2	5	1	9	40	1	2	43	2	16	2	NA
1	1	2	0	43	1	2	0	1	42000	2	7	5	6	16	39	4	5	40	4	2	58	2	16	2	NA
1	1	2	12331.45	37	7	2	2	1	4000	4	9	5	7	16	39	4	5	8	1	2	18	2	15	1	7.5
1	1	2	16498.87	35	7	1	2	1	17000	1	1	9	14	14	35	3	9	40	6	2	28	2	11	2	10
1	1	2	10021.73	39	7	2	2	1	12000	2	7	5	7	16	39	4	5	25	1	1	23	2	15	1	7
1	1	2	0	40	1	2	0	1	77000	1	1	9	13	17	40	5	9	40	1	2	43	2	16	2	NA
1	1	2	11595.07	38	2	1	2	1	22000	1	1	12	20	15	37	3	12	-4	1	1	28	2	10	1	NA
1	1	2	17696	40	7	1	2	1	9500	2	7	10	16	6	17	2	10	18	2	2	20	2	9	1	9.722222
1	3	2	0	46	1	1	0	1	90002	1	1	10	15	8	20	2	10	40	4	2	57	2	15	2	NA
1	1	2	0	39	1	1	0	1	28000	1	1	11	18	13	32	3	11	40	2	2	38	2	12	2	NA
1	3	2	14678.35	43	5	1	2	1	71900	1	1	13	21	6	17	2	13	35	1	1	62	2	11	2	39.514286
1	1	2	12095.44	32	7	1	2	1	21600	3	4	3	3	19	43	7	9	40	1	1	35	2	11	2	10
1	4	2	13935.89	43	5	2	2	1	51000	1	1	13	21	17	40	5	13	40	1	2	47	2	12	2	24.525
1	1	2	0	39	1	2	0	1	30000	1	1	10	16	15	37	3	10	40	1	1	43	2	8	2	NA
1	1	2	10369.57	43	5	2	2	1	40000	1	1	8	12	17	40	5	8	40	1	2	38	2	8	2	19.225
1	1	2	11959.01	42	7	2	2	1	58500	1	1	10	16	10	27	2	10	40	1	2	55	2	16	2	26.8
1	3	2	11959.01	43	4	2	2	1	6000	2	7	10	15	8	21	2	10	16	1	2	59	2	7	1	10
1	1	2	13459.61	46	1	2	2	1	65000	2	7	10	16	10	25	2	10	32	1	2	53	2	14	1	35
1	1	2	0	39	7	2	0	1	11000	4	10	10	16	15	37	3	10	10	2	2	48	1	15	1	NA
1	1	2	0	40	1	1	0	1	110000	1	1	11	17	1	3	1	11	40	1	2	44	2	15	2	NA
1	1	2	10908.61	43	1	1	2	1	92000	1	1	9	13	4	12	2	9	45	1	2	34	2	16	2	64.111111
1	1	2	11843.2	39	1	1	2	1	32000	1	1	4	4	21	49	9	4	40	1	2	47	2	10	2	15.5
1	1	2	12338.85	39	1	1	2	1	78000	1	1	6	8	22	51	10	6	80	1	2	50	2	13	2	18.75
1	1	2	11997.06	39	7	2	2	1	17760	1	1	4	4	21	49	9	4	40	1	2	51	2	14	2	9.5
1	1	2	11913.15	40	1	2	2	1	23660	1	1	9	14	17	40	5	8	40	1	2	26	2	11	2	13.5
1	1	2	12455.8	43	7	2	2	1	22000	1	1	10	16	6	17	2	10	40	1	2	33	2	11	2	10.97
1	1	2	14484.8	40	7	2	2	1	25000	1	1	10	16	6	17	2	10	40	1	2	23	2	11	2	9.08
1	1	2	11024.38	39	1	2	2	1	23000	1	1	12	20	15	37	3	12	40	1	2	26	2	12	2	11
1	1	2	11024.38	43	7	2	2	1	14000	1	1	11	18	13	33	3	11	45	1	2	25	2	11	2	5
1	1	2	0	40	1	1	0	1	12666	1	1	11	18	13	32	3	11	36	1	2	55	2	13	2	NA
1	1	2	14342.55	43	1	1	2	1	28000	1	1	4	5	17	40	5	4	40	2	2	27	2	14	2	13
1	1	2	12150.21	42	1	2	2	1	30000	1	1	10	16	11	28	3	10	40	1	2	49	2	13	2	14.425
1	1	2	0	43	1	2	0	1	50000	1	1	10	15	8	21	2	10	-4	1	2	48	2	15	1	NA
1	1	2	0	43	1	1	0	1	110000	1	1	5	7	16	38	4	5	-4	1	2	50	2	15	1	NA
1	1	2	13129.75	41	1	2	2	1	18500	1	1	8	11	17	40	5	8	40	1	2	48	2	12	2	8.3
1	1	2	14829.01	39	7	1	2	2	15000	1	1	11	17	15	37	3	11	40	1	2	24	2	8	2	12.5
1	1	2	11688.5	39	7	1	2	1	4000	1	1	5	7	17	40	5	5	40	1	2	31	2	3	2	8.65
1	1	2	10659.52	41	5	2	2	1	54000	1	1	8	11	10	25	2	8	40	1	2	55	2	15	2	25
1	1	2	0	43	1	1	0	1	90000	1	1	12	20	1	1	1	12	40	1	2	33	2	15	2	NA
1	1	2	11431.23	39	1	1	2	1	32000	1	1	4	5	19	42	7	3	50	1	2	35	2	12	2	23
1	1	2	10891.64	44	1	2	0	1	30000	3	4	10	15	8	21	2	10	40	1	2	36	2	15	2	39.475
1	1	2	9974.81	39	1	2	0	1	27000	1	1	10	16	11	28	3	10	40	1	2	44	2	9	2	11.54
1	1	2	13513.61	43	1	1	2	1	46000	1	1	8	11	1	1	1	8	40	1	2	29	2	15	2	25
1	1	2	11085.75	43	1	2	2	1	3150	4	12	11	18	13	33	3	11	20	4	2	41	2	16	1	10
1	1	2	13508.68	44	1	1	2	1	175000	1	1	9	14	1	1	1	9	40	1	2	57	2	16	2	72.125
1	1	2	12176.61	43	1	2	2	1	8840	2	7	5	7	10	27	2	5	20	1	2	25	2	14	1	8.5
1	1	2	11989.47	39	1	2	2	1	3800	2	7	10	16	13	32	3	11	25	1	2	64	2	14	1	8.84
1	1	2	0	39	7	1	0	1	32000	3	4	3	3	19	46	7	3	40	2	2	51	2	12	2	NA
1	1	2	0	43	1	1	0	1	200000	1	1	4	5	1	1	1	4	40	1	2	46	2	16	2	NA
1	1	2	13996.98	43	7	2	2	1	80000	1	1	10	16	10	25	2	10	44	2	2	48	2	13	2	46
1	1	2	13352.26	39	7	2	0	1	29950	2	7	5	7	17	40	5	5	32	2	2	50	1	9	1	18
1	4	2	0	35	7	2	0	1	25688	1	1	13	21	9	23	2	13	38	1	1	34	2	7	2	NA
1	1	2	0	43	1	1	0	1	25000	1	1	11	18	13	33	3	11	35	1	2	34	2	8	2	NA
1	1	2	11591.26	40	1	1	2	1	36800	1	1	12	20	15	36	3	12	40	1	2	35	2	14	2	5
1	1	2	11591.26	39	1	1	0	1	60840	1	1	3	3	19	43	7	3	40	1	2	36	2	14	2	29.25
1	1	2	10114.45	43	1	2	2	1	80000	1	1	8	11	1	1	1	10	50	1	2	43	2	16	2	57.7
1	1	2	12266.05	42	1	1	0	1	40000	1	1	9	13	22	51	10	9	40	1	2	54	2	11	2	21.625
1	1	2	13229.15	42	1	1	2	1	75000	1	1	10	16	19	45	7	10	50	1	2	60	2	14	2	19.22
1	4	2	0	40	1	1	0	1	48000	1	1	8	12	19	43	7	8	-4	2	2	50	2	13	1	NA
1	1	2	11899.24	42	1	2	2	1	35000	1	1	7	10	20	48	8	7	40	1	2	51	2	14	2	20.93
1	1	2	11238.29	43	7	2	2	1	36000	1	1	8	11	3	8	2	8	40	1	2	32	2	14	2	17.3
1	1	2	11899.24	39	5	2	2	1	27000	1	1	5	7	16	39	4	5	35	1	2	50	2	12	2	7.8
1	4	2	11591.26	40	1	1	0	1	49000	1	1	13	21	12	29	3	13	40	1	2	38	2	13	2	23.075
1	4	2	12320.31	42	6	2	0	1	23000	1	1	10	16	11	28	3	10	-4	1	2	48	2	8	1	NA
1	1	2	11593.2	39	7	1	2	1	55000	1	1	5	6	20	48	8	5	60	1	2	33	2	12	2	17.633333
1	4	2	11593.2	44	1	1	0	1	61000	1	1	10	15	1	3	1	10	40	1	2	31	2	13	2	29.325
1	1	2	11006.91	39	1	2	0	1	8000	4	9	6	8	15	37	3	6	20	1	2	63	2	11	1	10.15
1	1	2	0	43	1	2	0	1	40000	1	1	10	16	8	21	2	10	35	1	2	38	2	14	2	NA
1	1	2	12952.29	38	7	2	2	1	22000	1	1	5	7	21	49	9	4	36	1	2	18	2	6	2	10
1	1	2	11250.24	39	1	1	1	1	70000	3	3	4	4	22	51	10	6	60	1	2	41	2	15	2	25
1	4	2	11843.2	43	1	1	2	1	55000	1	1	10	15	8	21	2	10	-4	1	2	45	2	13	1	NA
1	1	2	9974.81	43	1	2	2	1	25000	2	7	10	16	10	25	2	10	40	1	2	41	2	13	2	46.625
1	1	2	0	43	1	2	0	1	22000	2	7	11	18	13	33	3	11	20	1	2	54	2	12	1	NA
1	1	2	0	36	7	1	0	1	1000	4	12	5	7	17	40	5	5	30	2	2	47	2	6	1	NA
1	1	2	12503.57	40	1	1	2	1	50000	1	1	5	6	16	39	4	5	60	1	2	59	2	12	2	18
1	1	2	0	39	1	1	0	1	20000	1	1	4	4	21	49	9	4	40	1	2	47	2	10	2	NA
1	4	2	0	43	1	1	0	1	50000	1	1	6	9	4	12	2	6	40	1	2	30	2	15	2	NA
1	4	3	0	39	5	2	0	1	29000	1	1	10	15	14	35	3	10	40	1	2	60	2	9	2	NA
1	1	3	10709.96	40	5	2	2	1	17000	2	7	11	18	13	33	3	11	-4	1	2	44	2	7	1	3.15
1	1	3	10709.96	41	5	2	1	1	28000	1	1	10	16	10	27	2	10	40	1	2	44	2	4	2	16.6
1	1	3	11158.21	39	1	1	0	1	54000	1	1	6	9	21	49	9	6	40	1	2	49	2	13	2	25
1	1	3	12083.37	42	1	2	2	1	35000	1	1	10	16	10	27	2	10	36	1	2	51	2	14	2	20
1	1	3	0	39	5	1	0	1	30000	1	1	4	4	21	49	9	4	40	1	1	38	2	14	2	NA
1	1	3	0	43	1	1	0	1	60000	1	1	8	11	17	40	5	8	-4	1	2	44	2	13	1	NA
1	1	3	0	39	1	2	0	1	14000	3	5	9	14	17	40	5	9	40	1	2	37	2	14	2	NA
1	1	3	0	42	1	2	0	1	20000	1	1	11	18	1	3	1	11	40	1	2	42	2	12	2	NA
1	1	3	0	39	1	1	0	1	13000	1	1	12	20	15	37	3	12	40	1	2	48	2	13	2	NA
1	1	3	12083.37	40	5	2	2	1	40000	1	1	5	6	2	5	1	5	60	1	2	51	2	11	2	12.816667
1	1	3	11158.21	44	1	1	2	1	110000	3	2	4	5	4	12	2	4	40	1	2	48	2	16	2	50.475
1	1	3	13603.07	37	7	2	2	1	680	4	11	5	7	16	39	4	5	8	1	2	18	2	9	1	8.5
1	1	3	15177.52	40	7	1	2	1	10000	4	9	5	7	16	39	4	11	25	1	2	23	2	15	1	9.4
1	1	3	10709.96	40	5	2	2	1	2033	2	7	5	7	16	39	4	11	32	1	2	41	2	15	1	7.3
1	1	3	0	39	1	1	0	1	30000	3	5	4	5	22	51	9	4	40	1	2	22	1	8	2	NA
1	1	3	14076.42	40	1	1	0	1	30000	1	1	3	3	19	45	7	3	40	1	2	27	2	9	2	15.14
1	1	3	14150.38	35	7	2	2	1	9000	4	9	11	18	13	33	3	11	32	1	2	19	2	5	1	3.65
1	1	3	0	39	1	2	0	1	12000	2	7	5	7	16	39	4	5	25	1	1	40	2	9	1	NA
1	1	3	0	36	1	1	0	1	48000	1	1	9	14	22	51	10	9	40	1	2	37	2	15	2	NA
1	1	3	13014.33	39	1	2	2	1	24000	1	1	10	16	17	40	5	10	40	1	2	49	2	13	2	11.5
1	1	3	13109.65	43	1	1	2	1	70000	1	1	7	10	1	3	1	7	40	1	2	39	2	15	2	33.65
1	1	3	11132.37	43	1	2	2	1	2500	4	9	10	16	17	40	2	10	8	1	2	57	2	15	1	12
1	4	3	13360.24	40	5	2	0	1	32000	1	1	13	21	12	30	3	13	40	2	2	52	2	11	2	18.62
1	1	3	12273.66	39	7	2	2	1	31600	1	1	10	16	17	40	5	10	40	1	2	27	2	9	2	15.2
1	4	3	13109.65	41	1	1	2	1	35000	1	1	11	17	14	34	3	11	40	1	2	39	2	9	2	9
1	1	3	11569.94	40	1	1	2	1	90000	1	1	6	9	3	8	2	6	50	1	2	49	2	15	2	32.4
1	4	3	12972.6	39	1	2	2	1	30000	1	1	10	15	17	40	5	10	40	1	2	47	2	15	2	15.375
1	1	3	12529.24	39	1	2	2	1	43000	1	1	8	11	17	40	5	8	40	1	2	54	2	16	2	21.15
1	4	3	0	43	7	2	0	1	15680	4	11	10	15	8	21	2	10	28	1	2	29	2	7	1	NA
1	1	3	0	40	1	2	0	1	16000	1	1	6	8	16	39	4	9	40	1	2	42	2	14	2	NA
1	1	3	0	40	1	2	0	1	33280	1	1	6	9	16	39	4	6	40	1	2	41	2	14	2	NA
1	1	3	12660.81	43	1	1	2	1	409068	1	1	8	11	1	1	1	8	55	1	2	44	2	16	2	52.454545
1	1	3	15788.19	39	1	1	2	1	30000	1	1	4	4	21	49	9	4	40	1	2	22	2	10	2	15.3
1	1	3	0	40	7	1	0	1	12520	1	1	12	20	8	21	2	12	48	2	2	35	2	5	2	NA
1	1	3	0	42	7	1	0	1	38000	1	1	8	11	17	40	5	8	40	2	2	56	2	13	2	NA
1	1	3	13151.82	40	7	1	2	1	24000	1	1	9	13	17	40	5	9	40	1	2	36	2	12	2	20.5
1	1	3	11607.16	43	1	1	2	1	113000	1	1	9	13	1	1	1	9	45	1	2	45	2	15	2	49.155556
1	1	3	11899.12	42	5	2	2	1	9000	4	11	8	12	16	39	4	8	29	1	2	35	2	6	1	5
1	1	3	13151.82	45	1	1	2	1	53000	1	1	10	16	10	24	2	10	60	1	2	35	2	11	2	16.983333
1	4	3	11543.83	44	1	1	0	1	60000	1	1	10	15	8	21	2	10	40	2	2	53	2	13	2	28.85
1	1	3	11158.21	45	1	1	2	1	100000	1	1	9	13	7	18	2	9	60	1	2	47	2	14	2	22.3
1	1	3	11523.02	42	1	1	0	1	43000	3	3	4	4	21	49	9	4	45	1	2	55	2	11	2	22.644444
1	1	3	0	39	5	1	0	1	37500	1	1	4	4	21	49	9	4	40	1	2	49	2	15	2	NA
1	3	3	0	40	1	2	0	1	15537	2	7	10	15	17	40	5	10	32	1	2	40	2	14	1	NA
1	1	3	12510.95	40	5	2	2	1	19000	2	7	5	7	13	33	3	5	16	1	2	48	2	7	1	7.3
1	1	3	12660.81	40	1	1	2	1	20000	1	1	4	4	21	49	9	4	40	1	2	44	2	15	2	24.025
1	1	3	10484.76	41	1	2	2	1	25000	1	1	9	14	16	39	4	9	-4	1	2	43	2	15	1	9
1	1	3	11157.39	40	1	1	2	1	11000	3	5	4	4	21	49	9	4	48	1	2	54	2	11	2	17.4
1	1	3	0	39	1	1	0	1	30000	1	1	4	4	21	49	9	4	40	1	2	36	2	12	2	NA
1	1	3	0	43	1	2	0	1	47000	1	1	4	4	2	6	1	4	40	1	2	37	2	14	2	NA
1	1	3	0	43	1	2	0	1	19000	2	7	10	16	6	17	2	10	40	1	2	42	2	12	2	NA
1	4	3	11829.28	39	1	2	2	1	27000	1	1	13	21	17	40	5	13	40	1	2	52	2	14	2	27.875
1	1	3	14858.37	39	7	1	2	1	2677	3	6	4	4	21	49	9	4	40	1	2	21	2	7	2	8.75
1	1	3	11362.91	43	7	1	0	1	25000	2	7	10	15	8	21	2	10	20	2	2	52	2	3	1	14.75
1	1	3	12456.79	40	1	2	2	1	16002	2	7	10	16	14	35	3	10	32	1	2	30	2	9	1	9
1	1	3	12567.27	39	7	1	2	1	30000	1	1	4	4	21	49	9	4	40	1	2	25	2	7	2	22.5
1	1	3	0	39	1	1	0	1	45000	1	1	10	16	20	48	8	10	40	1	2	64	2	15	2	NA
1	1	3	11925.78	40	5	2	2	1	38500	1	1	11	18	1	3	1	11	-4	1	2	53	2	8	1	NA
1	1	3	10669.03	42	1	1	2	1	35000	2	7	6	8	1	3	1	6	35	1	2	46	2	14	2	17.21
1	1	3	10493.85	39	7	1	0	1	10000	1	1	4	4	19	42	7	4	40	1	2	56	2	8	2	32
1	1	3	0	40	5	2	0	1	90000	1	1	8	11	1	1	1	8	40	1	2	44	2	14	2	NA
1	1	3	0	40	5	1	0	1	27900	1	1	5	6	16	39	4	5	50	2	2	45	2	10	2	NA
1	1	3	12990.65	43	1	1	2	1	65000	1	1	4	4	4	12	2	4	50	1	2	27	2	14	2	27.5
1	1	3	12876.45	43	1	2	2	0	0	5	13	15	23	24	53	4	5	40	1	2	31	2	11	2	7.5
1	1	3	10408.45	39	1	1	2	1	30000	1	1	5	7	16	39	4	5	40	4	2	46	2	11	2	12.7
1	1	3	0	39	7	2	0	1	32000	1	1	12	20	16	38	4	12	40	2	2	30	2	7	2	NA
1	3	3	10077.01	39	1	1	2	1	77000	1	1	13	21	17	40	5	13	40	1	2	60	2	16	2	17.875
1	1	3	11578.5	43	1	1	2	1	140000	1	1	5	7	16	38	4	5	40	1	2	39	2	14	2	67.3
1	1	3	11807.6	44	1	2	2	1	86000	1	1	4	5	1	3	1	4	45	1	2	39	2	15	2	36.933333
1	1	3	11997.82	43	7	1	0	1	45000	1	1	5	7	22	51	10	5	50	2	2	56	2	13	2	23.82
1	1	3	10376.07	39	5	1	2	1	22000	1	1	4	4	14	35	3	4	40	2	2	52	2	13	2	13
1	1	3	10208.01	43	1	1	2	1	20000	1	1	5	7	17	40	5	5	40	1	2	63	2	11	2	9.625
1	1	3	12294.06	39	7	2	2	1	14700	2	7	9	14	17	40	5	9	30	2	2	48	2	9	1	6
1	1	3	11923.27	39	7	1	2	1	18973	1	1	5	7	16	39	4	5	40	1	2	44	2	10	2	10
1	1	3	0	40	7	2	0	1	30000	1	1	12	20	20	48	8	12	40	2	2	26	2	9	2	NA
1	1	3	0	40	1	1	0	1	30000	1	1	9	14	20	48	8	9	40	1	2	40	2	13	2	NA
1	1	3	0	40	1	1	0	1	80000	1	1	4	4	1	2	1	4	60	1	2	42	2	14	2	NA
1	1	3	10219.13	39	5	1	2	1	32000	1	1	3	3	19	46	7	3	40	1	2	61	2	9	2	15.5
1	1	3	0	39	7	1	0	1	6000	2	7	9	14	14	35	3	11	30	2	2	35	2	7	1	NA
1	3	3	15358.94	43	1	2	2	1	32002	1	1	10	15	8	21	2	10	35	1	2	23	2	9	2	17.571429
1	3	3	12567.27	42	7	1	2	1	31926	1	1	13	21	12	30	3	13	-4	1	2	25	2	14	1	14
1	1	3	0	43	1	1	0	1	100000	1	1	9	13	3	8	2	9	40	4	2	40	2	15	2	NA
1	1	3	11747.04	39	7	1	2	1	9000	2	7	1	1	18	41	9	4	48	1	2	19	2	14	2	11
1	1	3	13650.19	43	1	1	2	1	55000	2	7	9	13	7	19	2	9	30	1	2	26	2	15	1	35.266667
1	2	3	13487.14	43	1	1	0	1	95000	1	1	13	21	5	14	2	13	40	2	2	55	2	15	2	45.675
1	3	3	12067.43	44	1	2	2	0	0	5	13	15	23	24	53	2	13	37	2	2	49	2	14	2	31.162162
1	1	3	11704.81	40	7	1	2	1	3640	2	7	12	20	20	48	8	12	10	1	2	19	2	15	1	7
1	1	3	13113.9	40	1	1	2	1	409068	1	1	5	7	17	40	10	6	28	1	2	28	2	13	1	13.75
1	1	3	11983.52	39	1	1	2	1	45000	1	1	12	20	14	35	3	12	40	1	2	52	2	7	2	9
1	1	3	13558.42	39	3	1	2	1	3300	3	5	4	5	21	49	9	4	16	1	1	29	2	6	1	7.5
1	1	3	12212.18	40	7	1	2	1	75000	1	1	3	3	19	46	7	3	40	1	2	45	1	7	2	19.225
1	1	3	11983.52	39	1	1	0	1	43000	1	1	7	10	21	49	9	7	38	1	2	50	2	14	2	21.763158
1	1	3	14280.51	39	1	1	2	1	49000	1	1	10	16	14	35	3	10	40	2	2	43	2	9	2	21.625
1	1	3	11511.58	40	1	2	2	0	0	5	13	15	23	24	53	1	10	40	2	2	53	2	9	2	24
1	1	3	11982.35	39	7	2	2	1	11700	3	6	5	7	16	39	4	5	40	2	2	54	2	4	2	9
1	1	3	12753.58	40	7	1	2	1	64000	1	1	9	13	3	8	2	9	40	1	2	35	2	13	2	29.8
1	1	3	12339.89	39	1	1	0	0	0	5	13	15	23	24	53	7	3	40	1	1	46	2	13	2	32
1	4	3	11171.11	44	1	2	0	1	60000	1	1	10	15	8	21	2	10	40	1	2	41	2	15	2	28.85
1	1	3	11531.19	40	7	2	2	1	4500	1	1	10	15	15	37	3	10	40	1	1	23	2	14	2	14.4
1	1	3	14188.79	40	7	2	2	0	0	5	13	15	23	24	53	3	11	20	1	2	19	2	13	1	10
1	1	3	0	32	1	1	0	1	41600	3	5	3	3	19	43	7	3	40	1	1	54	2	2	2	NA
1	1	3	0	40	7	1	0	1	10400	2	7	12	20	22	51	10	12	20	1	1	20	2	2	1	NA
1	1	3	0	35	1	1	0	1	19240	1	1	4	4	21	49	9	4	40	1	1	42	2	7	2	NA
1	1	3	0	40	1	1	0	1	25000	1	1	8	12	16	39	4	8	40	2	2	45	2	7	2	NA
1	1	3	0	44	1	1	0	1	123000	1	1	8	11	1	3	1	8	50	1	2	40	2	15	2	NA
1	1	3	0	38	1	2	0	1	16000	3	3	11	18	13	33	3	11	50	4	2	52	2	14	2	NA
1	1	3	0	40	1	1	0	1	10000	2	7	11	18	13	32	3	11	60	4	2	21	2	14	2	NA
1	1	3	0	44	5	2	0	1	125000	1	1	8	11	1	1	1	8	40	1	2	36	2	16	2	NA
1	1	3	0	39	1	1	0	1	30000	1	1	9	14	12	31	3	9	40	2	2	38	2	5	2	NA
1	4	3	0	40	7	2	0	1	50000	1	1	6	8	17	40	5	6	40	2	2	44	2	3	2	NA
1	1	3	15310.12	40	7	1	2	1	8000	1	1	5	7	16	39	4	5	15	1	2	23	2	8	1	8.5
1	1	3	12149.95	40	1	2	2	1	75000	1	1	8	12	16	39	4	8	-4	1	2	49	2	15	1	7.25
1	4	3	11732.38	40	1	1	0	1	36000	1	1	13	21	12	30	3	13	77	1	2	49	2	15	2	12.480519
1	1	3	11983.52	43	1	1	2	1	82000	1	1	4	5	1	3	1	4	45	1	2	50	2	15	2	19.222222
1	1	3	11795.08	39	1	1	2	1	22880	1	1	9	13	1	1	1	9	40	1	2	41	2	13	2	11
1	1	3	0	43	1	2	0	1	111000	1	1	9	13	3	9	2	9	48	1	2	43	2	16	2	NA
1	1	3	0	44	1	1	0	1	190000	1	1	4	5	16	39	4	4	40	1	2	49	2	15	2	NA
1	1	3	14197.46	34	1	1	2	1	35000	1	1	9	14	22	51	10	9	40	1	1	41	2	8	2	17.4
1	1	3	11983.52	45	1	1	2	1	150000	1	1	10	16	10	24	2	10	-4	1	2	52	2	16	1	NA
1	1	3	10426.41	41	1	2	2	1	38000	1	1	10	16	17	40	5	10	38	1	2	56	2	15	2	20
1	1	3	0	43	1	1	0	1	85000	3	2	4	4	16	39	4	4	50	1	2	44	2	15	2	NA
1	1	3	0	39	1	1	0	1	50000	1	1	6	8	1	2	1	6	40	1	1	52	2	15	2	NA
1	1	3	0	37	1	2	0	1	51000	1	1	8	11	17	40	5	8	40	1	1	48	2	15	2	NA
1	1	3	11855.09	39	1	1	2	1	46000	1	1	12	20	20	48	8	12	40	1	1	45	2	15	2	13
1	1	3	11734.71	43	1	2	2	1	50000	1	1	5	7	16	38	4	5	40	1	2	54	2	15	2	24.05
1	1	3	0	35	1	1	0	1	20500	1	1	9	14	1	3	1	9	40	1	1	32	2	6	2	NA
1	1	3	0	40	1	1	0	1	30000	1	1	5	7	22	51	10	5	40	1	1	40	2	11	2	NA
1	1	3	12606.85	40	7	1	2	1	8000	4	10	4	4	21	49	9	4	40	1	1	21	2	12	2	9
1	1	3	0	40	1	2	0	1	4000	4	9	10	15	8	22	2	10	30	1	2	42	2	7	1	NA
1	1	3	12755.72	43	1	1	2	1	65000	1	1	4	4	4	12	2	4	40	1	2	30	2	16	2	25
1	1	3	11931.42	40	1	2	0	1	6480	2	7	10	15	13	32	3	10	15	1	2	37	2	12	1	12
1	2	3	11785.94	39	1	1	2	1	62000	1	1	3	3	22	51	10	3	40	1	2	63	2	15	2	29
1	1	3	0	39	1	2	0	1	29000	1	1	5	7	16	38	4	5	40	1	2	53	2	9	2	NA
1	1	3	10344.09	39	5	2	2	1	30000	1	1	5	7	16	39	4	5	37	1	2	57	2	9	2	16.78
1	1	3	0	39	1	1	0	1	55000	1	1	3	3	19	42	7	3	40	1	2	33	2	11	2	NA
1	1	3	11760.94	43	1	2	2	1	62000	1	1	8	11	3	8	2	8	50	1	2	33	2	15	2	20
1	1	3	12084.26	43	1	1	0	1	30000	1	1	4	4	21	49	9	4	40	1	2	38	2	15	2	12.5
1	1	3	0	37	7	1	0	1	121	4	12	10	16	14	35	3	10	38	6	2	23	1	3	2	NA
1	4	3	11868.42	44	1	1	0	1	40000	1	1	10	15	8	21	2	10	45	1	2	46	2	14	2	37.422222
1	1	3	12122.47	39	1	1	0	1	50000	1	1	4	4	22	51	10	4	40	1	2	54	2	14	2	21
1	1	3	11840.53	39	7	1	2	0	0	5	13	15	23	24	53	5	5	-4	1	2	18	2	15	1	8
1	4	3	12556.29	44	1	2	0	1	51000	1	1	10	15	8	21	2	10	35	1	2	32	2	14	2	28.028571
1	4	3	12901.46	44	1	1	0	1	49000	1	1	10	15	8	21	2	10	39	1	2	36	2	14	2	24.153846
1	1	3	10283.22	39	7	2	2	1	7200	2	7	10	15	8	21	2	11	-4	1	2	56	2	3	1	15
1	1	3	11818.94	39	1	1	2	1	50000	1	1	2	2	21	49	9	3	40	1	2	54	2	14	2	20.92
1	1	3	11760.94	42	1	2	2	1	25000	3	3	10	16	17	40	5	10	40	1	2	32	2	12	2	15
1	1	3	13010.36	43	7	1	0	0	0	5	13	15	23	24	53	5	10	40	1	2	27	2	15	2	12.5
1	1	3	0	40	7	1	0	1	22890	1	1	10	16	10	27	2	10	40	1	2	25	2	13	2	NA
1	1	3	12157.81	39	7	1	2	1	45000	1	1	6	8	22	51	10	6	60	1	2	33	2	13	2	18.333333
1	1	3	14020.97	35	7	2	2	1	10000	2	7	11	18	13	32	3	11	43	1	2	22	2	6	2	8.75
1	1	3	11201	37	7	2	2	0	0	5	13	15	23	24	53	4	11	20	1	1	20	2	7	1	8.25
1	1	3	14592.49	42	7	1	2	1	4000	2	7	6	8	17	40	5	6	19	1	2	20	2	8	1	8
1	2	3	0	42	1	2	0	1	900	4	12	13	21	17	40	5	13	39	1	2	31	2	12	2	NA
1	1	3	11701.96	42	1	1	2	1	38000	1	1	10	16	11	28	3	10	40	1	2	41	2	10	2	18
1	1	3	0	40	7	2	0	1	15945	1	1	12	20	6	17	2	12	40	2	2	34	2	7	2	NA
1	1	3	14020.97	39	7	2	2	1	24000	1	1	10	16	11	28	3	10	40	1	2	24	2	11	2	10.93
1	1	3	11993.02	36	7	2	2	1	11000	4	10	11	18	13	33	3	11	33	1	2	26	2	4	1	8
1	3	3	11589.77	46	7	2	2	1	43500	1	1	11	17	8	22	2	11	40	1	2	38	2	11	2	23.375
1	1	3	11502.22	39	1	1	2	1	67600	1	1	4	4	4	12	2	4	40	1	2	35	2	14	2	32.5
1	1	3	12090.12	44	7	1	2	1	921	1	1	10	16	6	17	2	10	10	1	2	56	2	6	1	26
1	1	3	0	39	7	1	0	1	1944	4	12	11	18	13	32	3	11	25	1	2	19	2	9	1	NA
1	4	3	0	40	1	2	0	1	29000	1	1	10	15	17	40	5	10	40	6	2	55	2	11	2	NA
1	1	3	10114.27	43	7	2	2	1	15000	2	7	11	18	13	33	3	11	25	1	2	27	1	6	1	2.65
1	2	3	12516.19	46	1	1	2	1	14238	2	7	13	21	3	9	2	13	20	1	2	59	2	7	1	14.3
1	1	3	0	43	1	1	0	1	70000	1	1	9	13	3	8	2	9	40	1	2	48	2	15	2	NA
1	1	3	0	39	7	1	0	1	7502	4	10	5	7	16	39	4	5	27	1	2	22	2	11	1	NA
1	1	3	11957.95	43	7	1	2	1	58000	1	1	5	7	20	48	8	5	62	1	2	53	2	12	2	17.983871
1	1	3	13237.79	39	1	2	2	1	40000	1	1	10	16	17	40	5	10	40	1	2	46	2	14	2	17.5
1	1	3	0	43	1	2	0	1	12000	2	7	12	20	6	17	2	12	20	1	2	39	2	13	1	NA
1	1	3	11824.53	40	7	2	2	1	16640	1	1	5	7	16	39	4	5	35	1	2	21	2	9	2	8.25
1	1	3	13076.88	43	1	1	2	1	80000	1	1	4	4	1	3	1	4	40	2	2	36	2	14	2	38.45
1	1	3	10168.18	39	1	2	2	1	15000	2	7	5	7	16	38	4	5	15	1	2	32	2	8	1	10
1	1	3	11441.58	36	7	1	2	1	48000	1	1	9	14	14	34	3	9	40	1	2	43	2	12	2	10.6
1	1	3	0	40	7	1	0	1	7800	4	11	10	16	11	28	3	10	24	1	2	20	2	11	1	NA
1	1	3	9168.49	40	7	2	2	1	28000	2	7	10	16	17	40	5	10	32	1	2	40	2	8	1	18
1	1	3	13237.79	43	5	2	2	1	70000	1	1	9	13	2	6	1	9	40	1	2	45	2	14	2	33.65
1	1	3	12720.87	39	7	1	2	1	17466	3	2	3	3	22	51	5	11	-4	1	2	26	2	16	1	9.95
1	1	3	9151.35	40	1	2	2	1	22000	1	1	8	12	17	40	5	8	40	1	2	42	2	14	2	14
1	1	3	11354.01	40	7	1	2	1	3000	4	11	9	13	3	8	2	9	32	1	2	19	2	15	1	9
1	1	3	13059.28	39	7	2	2	1	15000	1	1	5	7	17	40	5	5	-4	2	2	19	2	12	1	7.55
1	1	3	0	41	7	1	0	1	63000	1	1	4	4	2	6	1	4	40	1	2	44	2	13	2	NA
1	1	3	0	46	1	2	0	1	433605	1	1	10	16	10	24	2	10	60	1	2	61	2	16	2	NA
1	1	3	9774.19	44	1	2	0	1	28000	1	1	10	16	1	3	1	10	63	1	2	39	2	11	2	12.825397
1	1	3	11885.31	42	1	1	2	1	40000	1	1	9	13	3	8	2	9	40	1	2	38	2	14	2	19.225
1	1	3	9774.19	39	1	2	2	1	14500	1	1	11	17	16	39	4	11	50	1	2	36	2	11	2	10
1	1	3	15645.04	39	6	2	2	1	2000	4	10	11	18	16	39	4	11	21	2	2	25	2	1	1	7.4
1	4	3	9792.5	43	5	2	2	1	40000	1	1	13	21	1	1	1	13	40	1	2	39	2	12	2	20
1	1	3	13237.79	40	1	2	2	1	35000	1	1	8	11	17	40	5	8	40	1	2	46	2	9	2	15.25
1	1	3	9397.79	44	1	2	2	1	87000	1	1	8	11	3	8	2	8	40	4	2	45	2	15	2	41.825
1	1	3	16221.61	43	5	2	0	1	70000	1	1	10	16	10	25	2	10	40	2	2	49	2	4	2	14.4
1	1	3	0	43	1	1	0	1	188000	1	1	8	11	16	39	4	8	40	1	2	54	2	16	2	NA
1	1	3	11397.19	39	1	1	2	1	40000	1	1	6	8	22	51	10	6	40	1	2	48	2	14	2	19.56
1	1	3	10950.76	40	7	2	2	1	8500	2	7	12	20	16	39	4	12	15	1	2	19	2	14	1	7.4
1	1	3	11641.15	40	1	1	2	1	60000	2	7	11	18	13	32	3	11	-4	1	2	54	2	8	1	16.56
1	1	3	10702.52	43	1	2	2	1	52000	1	1	4	4	1	2	1	4	45	1	2	56	2	14	2	25.644444
1	1	3	12974.95	39	1	1	0	1	45000	1	1	4	5	21	49	9	4	42	1	1	41	2	9	2	18.8
1	1	3	11302.28	39	5	1	0	0	0	5	13	15	23	24	53	8	6	40	1	2	60	2	14	2	17.25
1	1	3	0	39	1	1	0	1	20000	3	3	6	8	22	51	10	6	40	1	2	47	2	14	2	NA
1	1	3	0	39	7	1	0	1	8750	2	7	11	18	13	32	3	11	16	1	2	18	2	13	1	NA
1	1	3	12768.96	43	1	1	2	1	45000	1	1	8	11	3	8	2	8	45	1	2	28	2	13	2	33
1	1	3	10152.51	42	1	2	2	1	32000	1	1	5	7	21	49	9	5	40	1	2	29	2	13	2	15
1	1	3	9185.95	43	1	2	2	1	70000	1	1	4	4	2	6	1	4	51	1	2	44	2	13	2	28.431373
1	1	3	0	44	1	1	0	1	56000	1	1	9	13	3	8	2	9	40	1	2	28	2	13	2	NA
1	1	3	10950.76	39	3	2	2	1	10000	3	4	11	18	13	33	3	11	-4	1	2	19	2	4	1	2.63
1	1	3	11641.15	39	1	1	0	1	68068	1	1	4	4	20	48	8	4	40	1	2	54	2	13	2	32.73
1	1	3	12045.46	43	1	2	2	1	56888	1	1	10	16	10	27	2	10	40	1	2	53	2	13	2	27.35
1	1	3	11810.5	40	7	1	2	1	13000	3	4	11	18	13	32	3	11	40	1	2	22	2	13	2	10
1	4	3	0	43	1	1	0	1	50000	1	1	13	21	12	30	3	13	40	1	2	28	2	15	2	NA
1	1	3	11157.04	39	1	2	2	1	32000	1	1	8	12	17	40	4	5	32	1	2	29	2	12	1	16.3
1	1	3	0	39	5	2	0	1	13720	2	7	5	7	16	39	4	5	-4	1	2	37	2	4	1	NA
1	1	3	9838.56	39	1	1	2	1	75000	1	1	5	7	5	14	2	5	55	1	2	38	2	12	2	15
1	1	3	10556.43	39	7	2	2	1	22000	1	1	11	18	13	33	3	11	40	1	1	22	2	10	2	11.4
1	1	3	9840.21	43	5	1	2	1	80000	1	1	5	7	3	8	2	5	40	1	2	32	2	11	2	20
1	1	3	9839.98	40	1	1	2	1	40000	1	1	6	8	2	5	1	6	40	1	2	30	2	13	2	19.225
1	2	3	7703.89	39	1	2	2	1	46000	1	1	13	21	17	40	5	13	40	1	2	51	2	13	2	26
1	1	3	0	40	1	1	0	1	52000	1	1	4	4	21	49	9	4	-4	1	2	31	2	13	1	NA
1	1	3	11296.4	43	7	2	2	1	3219	2	7	10	16	6	17	5	10	14	1	1	30	2	7	1	8.5
1	1	3	11810.5	42	7	1	2	1	46000	1	1	9	13	3	8	2	9	40	1	2	24	2	11	2	17.25
1	3	3	8044.65	46	1	1	2	1	45976	2	7	10	15	8	20	2	10	-4	1	2	65	2	16	1	NA
1	4	3	0	44	1	1	0	1	59000	1	1	10	16	6	17	2	10	40	4	2	46	2	9	2	NA
1	1	3	0	39	1	2	0	1	29000	1	1	8	11	2	5	1	8	40	4	2	46	2	9	2	NA
1	1	3	13043.63	40	7	2	2	1	12000	2	7	10	16	8	21	2	10	30	1	2	22	2	13	1	7.25
1	1	3	7978.22	40	1	2	2	1	15912	1	1	5	7	16	39	4	5	40	1	2	48	2	11	2	7.65
1	1	3	12541.59	35	1	2	2	1	14560	1	1	5	7	16	39	4	5	40	1	1	38	1	8	2	7.5
1	1	3	8270.28	43	1	1	2	1	60000	1	1	10	16	10	27	2	10	56	2	2	37	2	15	2	24
1	1	3	11658.41	43	1	1	2	1	45000	1	1	4	4	20	48	8	9	35	2	2	40	2	11	2	42
1	1	3	8007.88	43	1	1	2	1	43000	1	1	4	4	17	40	5	4	40	1	2	46	2	12	2	20
1	1	3	7882.58	43	1	2	2	1	65000	1	1	8	11	1	1	1	8	43	1	2	45	2	15	2	30.627907
1	1	3	9168.25	40	7	1	2	1	11000	1	1	10	16	11	28	3	10	35	1	2	19	2	12	2	11
1	1	3	9128.93	40	1	1	2	1	500	4	12	9	14	21	49	9	9	40	2	2	51	2	13	2	7
1	1	3	11369.71	42	7	2	2	1	36000	1	1	3	3	17	40	5	3	35	1	2	33	2	8	2	22
1	1	3	9845.92	39	7	1	0	1	82000	1	1	7	10	20	48	8	7	40	1	2	31	2	14	2	31.56
1	1	3	0	31	7	1	0	1	23400	1	1	11	18	13	33	3	11	48	1	1	40	2	12	2	NA
1	1	3	0	40	7	2	0	1	16758	1	1	11	18	16	38	4	5	40	1	2	25	2	9	2	NA
1	1	3	11025.67	43	1	2	2	1	14000	1	1	10	16	8	21	2	10	40	1	2	27	2	10	2	9.5
1	1	3	7613.18	39	1	2	2	1	14000	4	9	6	8	22	51	10	6	25	1	2	52	2	13	1	14
1	1	3	7806.96	44	1	1	2	1	88000	1	1	10	15	1	3	1	10	55	1	2	49	2	14	2	30.763636
1	3	3	8062.2	46	7	1	2	1	377332	2	7	13	21	10	24	2	13	30	1	2	56	2	16	1	99
1	1	3	8147.55	43	5	2	2	1	65000	1	1	10	16	10	25	2	10	56	1	2	62	2	14	2	22.321429
1	1	3	0	39	1	2	0	1	30000	1	1	5	7	13	33	3	5	40	1	2	62	2	13	2	NA
1	1	3	0	37	7	1	0	1	9000	2	7	5	7	21	49	9	5	20	1	2	18	2	16	1	NA
1	1	3	0	43	1	2	0	1	7000	2	7	10	16	11	28	3	10	36	1	2	55	2	12	2	NA
1	1	3	11218.92	39	1	2	2	1	15000	1	1	10	16	14	35	3	10	40	1	2	34	2	10	2	9
1	1	3	8160.01	40	1	1	2	1	24002	1	1	12	20	21	49	9	12	40	1	2	56	1	8	2	2.14
1	1	3	9279.48	37	7	1	2	1	3002	4	11	4	5	21	49	9	4	20	1	2	18	2	8	1	7.25
1	1	3	7901.67	43	1	1	2	1	170000	1	1	4	5	1	1	1	4	60	1	2	48	2	16	2	48.083333
1	4	3	7899.8	44	1	1	0	1	70000	1	1	10	15	5	15	2	10	50	1	2	48	2	13	2	26.92
1	1	3	7703.72	39	1	2	2	1	65000	1	1	7	10	17	40	5	7	52	1	2	53	2	12	2	24.038462
1	1	3	9976.21	39	1	2	2	1	14500	1	1	5	7	16	39	4	5	40	1	2	42	2	13	2	8.65
1	1	3	7842.77	39	1	1	0	1	70000	1	1	6	9	19	44	7	6	40	1	2	63	2	13	2	35
1	4	3	12671.1	37	7	2	2	0	0	5	13	15	23	24	53	5	10	20	1	2	18	2	13	1	6
1	1	3	7613.36	40	1	2	2	1	27000	1	1	10	16	17	40	5	10	40	1	2	54	2	15	2	15.27
1	4	3	0	40	1	1	0	1	63500	1	1	13	21	12	30	3	13	40	1	2	46	2	15	2	NA
1	1	3	10922.83	39	1	2	2	1	35000	1	1	10	16	17	40	5	10	40	1	2	39	2	11	2	16.72
1	1	3	9398.76	40	7	1	2	1	10000	4	11	11	18	13	33	3	11	20	1	2	18	2	8	1	7.25
1	1	3	0	43	1	2	0	1	48000	2	7	10	16	10	25	2	10	-4	1	2	36	2	15	1	NA
1	1	4	6963.8	39	5	1	2	1	17000	1	1	6	8	22	51	10	6	50	1	2	51	2	14	2	26.14
1	1	4	6194.28	37	1	2	2	1	12000	2	7	11	18	13	32	3	11	42	1	1	24	2	7	2	8.5
1	1	4	6761.09	39	1	1	0	1	36000	1	1	4	5	22	51	10	4	-4	1	2	60	2	12	1	15.2
1	1	4	6154.75	39	1	2	2	1	25000	1	1	10	16	17	40	5	10	52	1	2	57	2	12	2	12.9
1	1	4	7089.07	39	7	1	2	1	20000	1	1	3	3	19	43	7	3	40	1	2	22	2	15	2	11
1	1	4	0	43	1	1	0	1	165000	1	1	4	4	1	1	2	10	70	1	2	32	2	16	2	NA
1	1	4	6932.52	40	7	1	2	1	40000	1	1	5	7	16	39	4	5	45	1	2	61	2	12	2	17.088889
1	1	4	0	39	1	1	0	1	48000	1	1	5	7	16	38	4	5	45	1	2	45	2	11	2	NA
1	1	4	0	42	1	1	0	1	50000	3	2	4	4	21	49	9	4	45	1	2	31	2	10	2	NA
1	1	4	7089.07	39	1	1	2	1	40000	1	1	4	5	22	51	10	4	40	1	2	21	2	11	2	10.69
1	1	4	7753.76	41	1	2	2	1	25000	1	1	10	16	8	22	2	10	40	1	2	24	2	11	2	13.6
1	1	4	6667.63	43	1	2	2	1	33000	1	1	10	16	2	5	1	10	40	1	2	33	2	13	2	16.98
1	1	4	7753.76	40	7	2	2	1	9000	2	7	11	18	1	3	1	11	20	1	2	22	2	11	1	11
1	4	4	6000.86	45	1	2	0	1	55000	1	1	10	15	8	21	2	10	40	1	2	43	2	14	2	26.45
1	1	4	0	44	1	2	0	1	80000	1	1	10	16	1	3	1	10	40	1	2	45	2	15	2	NA
1	1	4	6050.83	40	1	1	0	1	65000	1	1	4	4	21	49	9	4	45	1	2	34	2	14	2	35
1	1	4	6050.83	40	7	1	2	1	42000	1	1	5	7	10	27	2	5	42	1	2	32	2	10	2	17.82
1	4	4	0	43	1	1	0	1	96000	1	1	13	21	12	29	3	13	40	1	2	40	2	16	2	NA
1	1	4	0	43	1	1	0	1	105000	1	1	9	13	1	3	1	9	40	1	2	46	2	8	2	NA
1	1	4	7347.67	39	1	2	2	1	40000	1	1	11	18	1	3	5	10	30	1	2	48	2	16	1	15
1	1	4	5118.8	40	7	1	2	1	9500	2	7	11	18	13	32	3	11	20	4	2	22	2	14	1	9.11
1	1	4	6305.36	43	1	2	2	1	2360	2	7	5	7	17	40	5	5	15	1	2	57	2	15	1	14
1	1	4	6767.67	43	7	1	2	1	32000	1	1	7	10	9	23	2	7	40	1	2	25	2	12	2	11.5
1	4	4	7096.55	43	1	2	0	1	50000	1	1	10	15	8	21	2	10	50	1	2	52	2	14	2	27.78
1	1	4	6794.57	43	7	2	2	1	29000	1	1	10	16	6	17	2	10	40	1	2	25	2	14	2	13.95
1	1	4	7262.54	40	7	1	2	0	0	5	13	15	23	24	53	10	10	-4	1	2	20	2	14	1	6
1	1	4	5730.01	37	4	2	2	1	12000	1	1	10	16	15	37	3	10	-4	2	2	56	2	1	1	10
1	1	4	7417.17	39	5	2	2	1	24000	3	4	8	11	17	40	5	8	40	1	2	51	1	8	2	15.39
1	1	4	7417.17	40	7	2	2	1	2500	3	3	7	10	15	37	3	7	-4	1	2	53	2	4	1	7.25
1	1	4	0	38	7	1	0	1	3700	4	12	11	18	13	33	3	11	30	1	1	23	2	4	1	NA
1	1	4	0	37	7	1	0	1	3000	2	7	5	7	17	40	5	5	10	1	2	18	2	14	1	NA
1	3	4	0	44	1	2	0	1	59000	1	1	10	15	8	21	2	10	48	1	2	47	2	13	2	NA
1	1	4	6862.76	41	7	2	0	1	31000	1	1	6	8	1	3	1	6	40	1	2	38	2	9	2	14.9
1	1	4	5949.3	36	5	1	2	1	8000	1	1	12	20	14	35	3	12	-4	2	2	60	2	4	1	NA
1	1	4	6984.69	39	1	1	2	1	45000	1	1	5	7	22	50	10	5	-4	1	2	48	2	14	1	22.5
1	1	4	7090.76	39	1	2	2	1	40000	1	1	5	6	16	39	4	5	40	1	1	45	2	14	2	18
1	1	4	8302.38	43	7	2	2	1	31000	1	1	10	16	6	17	2	10	40	1	2	24	2	11	2	10
1	1	4	8302.38	40	7	2	2	1	12000	2	7	5	7	16	39	4	5	24	1	2	21	2	15	1	8.5
1	1	4	0	39	1	2	0	1	40000	1	1	11	17	15	37	3	11	40	1	2	37	2	13	2	NA
1	1	4	0	39	1	1	0	1	32000	1	1	12	20	6	17	2	12	40	2	2	40	2	13	2	NA
1	1	4	6475.13	43	1	1	2	1	72000	3	3	5	6	16	39	4	5	40	1	2	28	2	14	2	35.575
1	1	4	7096.55	42	1	2	2	1	4000	2	7	9	13	1	3	1	9	10	1	2	53	2	14	1	7.7
1	1	4	0	43	1	1	0	1	146000	1	1	8	11	16	39	4	8	40	1	2	32	2	15	2	NA
1	1	4	0	42	1	2	0	1	27000	3	2	5	7	16	39	4	5	40	1	2	39	2	7	2	NA
1	1	4	0	39	7	2	0	1	23000	1	1	11	18	14	35	3	11	37	1	2	23	2	1	2	NA
1	1	4	0	40	7	2	0	1	26000	1	1	9	14	17	40	1	8	40	6	2	23	1	15	2	NA
1	4	4	0	39	1	1	0	1	72000	1	1	13	21	19	46	7	13	32	1	2	45	2	13	1	NA
1	1	4	7172.16	40	1	2	2	1	16560	2	7	12	20	17	40	5	12	15	1	2	48	2	14	1	20.333333
1	4	4	6154.75	42	1	2	2	1	43000	1	1	8	11	17	40	5	13	40	1	2	59	2	15	2	15
1	1	4	0	41	1	1	0	1	40000	1	1	9	13	21	49	9	9	40	1	2	46	2	9	2	NA
1	1	4	7134.2	40	7	1	0	1	80390	1	1	6	8	17	40	5	6	50	1	2	52	2	14	2	29.62
1	1	4	6147.7	44	7	2	2	1	60000	1	1	4	4	1	3	1	4	40	1	2	42	2	16	2	25
1	1	4	5684.84	43	1	1	2	1	35000	3	3	6	8	22	51	10	6	40	1	2	44	2	12	2	12.5
1	1	4	6963.8	40	1	1	0	1	26000	3	5	4	5	20	48	8	4	40	1	2	54	2	14	2	21
1	1	4	7753.76	43	1	2	2	1	35000	1	1	8	11	16	39	4	8	40	1	2	24	2	15	2	13
1	4	4	5624.63	41	1	2	2	1	40000	1	1	10	16	10	25	2	10	50	1	2	47	2	14	2	20
1	1	4	0	39	1	2	0	1	52000	1	1	4	5	1	3	1	4	40	1	2	48	2	14	2	NA
1	1	4	0	40	1	2	0	1	23662	2	7	5	7	17	40	5	5	32	1	2	49	2	14	1	NA
1	1	4	4304.79	34	1	1	2	1	22000	1	1	4	4	21	49	9	4	40	1	2	26	2	8	2	20
1	1	4	4210.94	42	7	2	2	1	64000	1	1	10	16	10	25	2	10	36	1	2	32	2	11	2	33
1	1	4	0	39	7	2	0	1	6000	4	10	5	7	16	39	4	5	-4	1	2	26	2	10	1	NA
1	1	4	5201.66	45	1	1	2	1	409068	1	1	9	13	7	18	2	9	-4	1	2	56	2	15	1	NA
1	4	4	5110.09	41	1	1	0	1	70000	1	1	13	21	4	12	2	13	40	1	2	59	2	16	2	28
1	1	4	6056.76	39	5	2	0	1	25696	3	3	5	6	22	51	10	5	40	2	2	56	2	10	2	16
1	1	4	4862.84	43	1	2	2	1	40000	1	1	8	11	2	7	1	4	30	1	2	56	2	16	1	12.3
1	1	4	4295.56	43	7	2	2	1	25000	1	1	10	16	17	40	5	10	40	1	2	28	2	13	2	29.8
1	1	4	4011.88	39	7	2	2	1	5500	2	7	9	14	14	35	10	5	45	1	1	23	2	7	2	10
1	2	4	5768.24	39	1	2	2	1	40000	3	3	6	9	2	5	5	13	40	1	2	48	2	15	2	18
1	1	4	5666.69	39	6	2	2	1	41000	1	1	8	11	2	5	1	8	40	1	2	47	2	11	2	18.69
1	1	4	5857.17	40	5	2	2	1	35000	1	1	8	11	17	40	5	8	40	1	2	60	1	13	2	17
1	1	4	5163.12	39	1	2	2	1	64020	1	1	8	11	20	48	8	8	40	1	1	53	2	14	2	30.775
1	1	4	4414.71	40	7	1	2	1	42000	1	1	5	7	3	8	2	5	45	1	2	25	2	13	2	23.511111
1	1	4	0	33	1	1	0	1	40000	1	1	4	5	21	49	9	4	40	1	1	42	2	14	2	NA
1	1	4	3842.24	40	7	1	2	1	8000	4	9	5	6	21	49	9	5	35	1	2	19	2	12	2	9
1	1	4	4828.28	42	1	1	2	1	20000	1	1	6	8	22	51	10	6	40	1	2	22	2	8	2	9.5
1	4	4	0	42	5	2	0	1	42000	1	1	13	21	10	25	2	13	48	1	2	35	2	11	2	NA
1	1	4	0	39	7	2	0	1	33000	1	1	4	5	21	49	9	4	40	1	1	26	2	4	2	NA
1	1	4	0	37	7	2	0	1	11000	3	3	11	18	16	39	4	5	16	1	2	18	2	15	1	NA
1	1	4	0	40	1	1	0	1	50000	1	1	4	4	20	48	8	4	40	1	2	47	2	14	2	NA
1	1	4	4870.67	40	1	1	0	1	55000	1	1	4	5	22	51	10	4	40	1	2	62	2	13	2	20
1	1	4	3789.84	39	1	2	2	1	30000	1	1	10	16	11	28	3	10	40	1	2	44	2	12	2	15
1	1	4	5072.15	40	5	1	2	1	9000	3	6	4	5	21	49	9	4	40	1	2	59	2	15	2	18.55
1	1	4	0	40	1	1	0	1	32000	1	1	1	1	22	51	10	6	50	1	2	35	2	10	2	NA
1	1	4	5768.24	43	1	2	2	1	75000	1	1	7	10	1	1	1	7	50	1	2	48	2	15	2	47.68
1	1	4	4951.56	39	7	1	2	1	16000	1	1	3	3	19	44	7	3	40	1	2	21	2	7	2	16
1	1	4	4933.5	42	7	2	2	1	26000	1	1	9	13	3	8	2	9	40	1	2	23	2	8	2	12.6
1	1	4	5016.71	36	1	1	2	1	7000	4	9	7	10	22	51	10	7	-4	1	2	52	2	5	1	7.5
1	1	4	4870.67	40	1	1	2	1	24954	1	1	4	5	14	35	3	4	40	1	2	62	2	14	2	11.34
1	1	4	4060.56	40	7	1	2	1	40000	1	1	5	7	16	39	4	5	15	1	2	31	2	2	1	9
1	1	4	3878.4	37	7	1	2	1	4000	2	7	11	18	13	33	3	11	20	1	2	18	2	16	1	7.25
1	1	4	0	40	5	2	0	1	14000	1	1	8	12	1	1	5	8	40	1	2	26	2	9	2	NA
1	1	4	0	39	1	1	0	1	28000	1	1	7	10	21	49	9	7	40	1	2	32	2	10	2	NA
1	1	4	5211.63	39	1	1	2	1	70000	1	1	5	7	22	51	10	5	35	1	2	59	2	14	2	8.5
1	1	4	5483.51	39	1	2	2	1	17472	1	1	3	3	17	40	5	3	40	1	2	52	2	11	2	8.4
1	3	4	5119.88	40	7	1	1	1	29000	1	1	10	15	14	35	3	10	40	1	2	55	2	8	2	13
1	1	4	4942.96	40	7	2	2	1	20000	2	7	11	18	13	33	3	11	30	1	2	23	2	7	1	10.5
1	1	4	4228.02	40	1	2	2	1	900	4	12	5	7	16	39	4	5	20	1	2	27	2	8	1	8.02
1	1	4	4957.79	43	1	1	2	1	60000	1	1	9	13	9	23	2	9	40	1	2	47	2	13	2	28.85
1	1	4	0	44	1	1	0	1	86000	1	1	4	4	4	12	2	4	60	1	2	34	2	15	2	NA
1	1	4	0	34	5	1	0	1	12000	2	7	12	20	22	51	10	12	25	1	2	49	2	7	1	NA
1	1	4	9688.19	43	1	1	2	1	43000	1	1	10	16	10	25	2	10	40	1	2	48	2	14	2	23
1	1	4	10152.93	39	7	1	2	1	10200	3	5	5	6	20	48	8	5	40	1	2	22	2	9	2	10.5
1	1	4	10185.08	40	3	1	2	1	75000	1	1	9	13	3	8	2	9	45	1	2	45	2	13	2	32.044444
1	1	4	10474.17	43	1	1	2	1	60000	1	1	5	7	16	38	4	5	72	1	2	60	1	16	2	15.277778
1	1	4	8559.38	39	1	1	2	1	36000	1	1	10	15	1	3	1	10	40	1	2	41	2	12	2	17.3
1	1	4	8559.38	40	1	1	2	1	18000	1	1	5	7	21	49	9	5	35	1	2	41	2	12	2	12.25
1	2	4	8559.38	40	1	1	0	1	60500	1	1	13	21	12	30	3	13	40	1	2	41	2	13	2	28
1	1	4	0	43	1	1	0	1	90000	1	1	9	13	3	8	2	9	40	1	2	41	2	14	2	NA
1	1	4	0	40	1	2	0	1	28000	1	1	11	18	13	33	3	11	40	1	2	26	2	13	2	NA
1	1	4	10518.08	39	1	1	2	1	22880	1	1	5	7	16	39	4	5	40	1	2	58	2	6	2	11
1	1	4	8891.33	34	1	1	2	1	25000	1	1	5	7	16	38	4	5	40	1	2	36	2	15	2	13
1	2	4	13695.23	40	7	2	2	1	20000	1	1	5	7	17	40	5	13	40	1	2	22	2	11	2	10.5
1	1	4	11033.87	39	5	2	2	1	35000	1	1	10	16	1	3	1	10	40	1	2	64	2	10	2	16.825
1	1	4	0	41	1	1	0	1	72000	1	1	4	4	19	46	7	4	40	1	2	53	2	15	2	NA
1	1	4	10900.2	40	7	1	2	1	9500	2	7	11	18	13	32	3	11	20	1	2	24	2	2	1	8.65
1	1	4	12571.21	39	1	2	2	1	52000	1	1	10	16	1	3	1	10	50	1	2	61	2	14	2	25
1	1	4	9081.57	41	1	1	0	1	56160	1	1	3	3	19	46	7	3	40	1	2	32	2	12	2	27
1	1	4	0	39	1	1	0	1	18720	1	1	4	5	17	40	5	4	40	1	2	24	2	1	2	NA
1	1	4	10403.1	44	5	1	2	1	86616	1	1	4	4	4	12	2	4	45	1	2	53	2	14	2	41.39
1	1	4	9222.01	43	1	1	2	1	52000	1	1	5	7	17	40	5	5	40	1	2	34	2	11	2	10
1	1	4	0	42	1	2	0	1	48000	3	2	10	16	10	27	2	10	40	1	2	31	2	15	2	NA
1	1	4	0	39	1	1	0	1	76000	1	1	12	20	20	48	8	12	40	1	2	30	2	15	2	NA
1	1	4	10403.1	41	7	1	2	1	44000	1	1	12	20	20	48	8	12	40	1	2	54	2	11	2	19.2
1	1	4	11356.44	43	1	2	2	1	75000	1	1	5	6	17	40	5	5	40	1	2	32	2	14	2	17.93
1	1	4	15364.03	40	1	2	2	1	31616	1	1	10	16	11	28	3	10	38	2	2	49	2	12	2	16
1	2	4	15127.39	36	6	2	2	1	2000	3	6	10	16	11	28	3	10	40	2	2	24	2	1	2	8.5
1	4	4	9179.57	44	7	2	2	1	59000	1	1	10	15	8	21	2	10	45	2	2	39	2	12	2	25.222222
1	1	4	9179.57	44	5	2	2	1	103000	1	1	4	4	16	39	4	4	50	2	2	37	2	15	2	39.62
1	1	4	0	43	1	2	0	1	42000	1	1	9	13	2	6	1	9	35	1	2	31	2	14	2	NA
1	3	4	0	39	7	1	0	1	26000	1	1	10	16	11	28	3	10	40	2	2	35	2	9	2	NA
1	4	4	8458.97	41	1	1	2	1	43000	1	1	10	16	10	27	2	10	68	1	2	31	2	14	2	13.014706
1	4	4	10830.35	43	1	2	0	1	29299	3	3	10	15	17	40	5	10	37	1	2	35	2	12	2	18.11
1	1	4	8822.71	40	1	1	2	1	25000	1	1	5	7	17	40	5	5	40	1	2	30	2	12	2	11
1	1	4	0	39	1	1	0	1	41000	1	1	6	8	22	50	10	6	40	1	2	65	2	13	2	NA
1	1	4	1848.18	39	1	2	2	1	26000	1	1	10	16	14	34	3	10	32	1	2	61	2	12	1	16
1	1	4	2144.96	36	7	1	2	1	4000	3	5	2	2	19	47	7	3	-4	3	2	19	2	13	1	10
1	1	4	1726.97	39	1	2	2	1	45000	1	1	6	8	17	40	5	6	45	1	2	53	2	13	2	17.777778
1	1	4	0	41	1	1	0	1	45000	1	1	3	3	19	44	7	3	45	1	2	47	2	11	2	NA
1	3	4	1726.97	43	7	2	2	1	34000	1	1	13	21	6	17	2	13	40	1	2	54	2	10	2	18.45
1	1	4	1503.75	46	1	1	2	1	75000	1	1	9	13	7	18	2	9	40	1	2	43	2	16	2	0.025
1	1	4	1788.08	42	1	2	2	1	55000	1	1	10	16	10	25	2	10	40	1	2	49	2	15	2	18
1	1	4	0	40	1	1	0	1	29000	1	1	6	8	22	51	10	6	48	1	2	48	2	13	2	NA
1	1	4	0	41	1	1	0	1	52500	1	1	5	7	16	39	4	5	50	1	2	32	2	11	2	NA
1	4	4	0	40	1	2	0	1	14500	1	1	10	15	6	17	2	10	54	1	2	45	2	13	2	NA
1	1	4	1756.08	43	1	1	2	1	170000	1	1	6	8	2	6	10	6	20	1	2	58	2	16	1	11.55
1	1	4	1572.13	41	7	2	2	1	24000	3	2	12	20	20	48	8	12	40	1	2	21	2	8	2	12.5
1	1	4	1671.88	40	5	1	2	1	35000	1	1	4	4	21	49	9	4	48	1	2	27	2	8	2	18.2
1	1	4	1545.39	40	7	2	2	1	30000	1	1	2	2	22	51	10	3	40	1	2	19	2	11	2	16
1	1	4	1322.5	42	1	2	2	1	14000	3	5	9	14	17	40	5	9	40	1	2	37	2	15	2	5.475
1	1	4	1634.42	40	1	2	2	1	90000	1	1	10	16	1	3	1	10	50	1	2	57	2	16	2	26
1	1	4	1663.86	41	1	1	2	1	45000	1	1	3	3	19	43	7	3	40	1	2	35	2	14	2	25
1	1	4	1646.09	40	7	2	2	1	1000	4	9	11	18	13	33	3	11	20	1	2	18	2	14	1	7.5
1	1	4	1599.93	41	1	2	2	1	7000	2	7	12	20	15	37	2	10	25	1	2	20	2	6	1	8
1	1	4	0	41	1	1	0	1	42000	1	1	12	20	16	39	4	12	40	1	2	55	2	13	2	NA
1	3	4	0	43	1	2	0	1	32000	1	1	10	15	15	37	3	10	40	1	2	37	2	15	2	NA
1	1	4	0	41	1	2	0	1	23000	1	1	8	11	17	40	5	8	46	1	2	37	2	10	2	NA
1	1	4	1375.81	43	1	2	2	1	11600	3	5	10	15	8	21	4	5	8	1	2	32	2	10	1	9.375
1	1	4	1701.45	40	7	1	2	1	42000	1	1	10	16	15	37	3	10	40	1	2	26	2	13	2	5
1	1	4	1599.93	39	7	2	2	1	24400	1	1	10	16	10	27	2	10	40	1	2	24	2	13	2	11.74
1	1	4	1297.65	39	1	2	2	1	19000	1	1	5	7	16	39	4	5	35	1	2	37	2	13	2	10.3
1	1	4	1589.69	43	1	1	0	1	60000	1	1	6	8	17	40	5	6	45	1	2	39	2	15	2	28.5
1	1	4	1875.19	40	7	1	0	1	40000	1	1	3	3	21	49	9	3	60	1	2	24	2	12	2	25.6
1	4	4	1875.31	43	7	1	2	1	25000	1	1	10	15	8	21	2	10	46	1	2	24	2	8	2	12.543478
1	1	4	1727.08	40	6	2	2	1	19000	1	1	5	7	13	32	3	5	40	1	2	50	2	7	2	11.75
1	1	4	1788.19	40	6	2	2	1	20000	1	1	11	18	17	40	5	11	40	1	2	45	2	6	2	10.29
1	4	4	0	41	7	1	0	1	14000	1	1	9	14	22	51	10	9	40	1	2	30	2	11	2	NA
1	1	4	0	43	5	2	0	1	7600	2	7	10	16	11	28	3	10	12	3	2	55	2	10	1	NA
1	4	4	1179.14	43	1	2	2	1	22768	1	1	13	21	1	3	1	13	43	1	2	42	2	11	2	10.8
1	1	4	0	43	1	1	0	1	34000	1	1	4	5	21	49	9	4	42	1	2	53	2	12	2	NA
1	3	4	1661.11	39	7	1	2	0	0	5	13	15	23	24	53	10	11	40	3	2	19	2	2	2	7.25
1	1	4	1440.24	39	1	1	2	1	21590	1	1	3	3	19	45	7	3	49	1	2	60	1	14	2	12
1	1	4	2610.79	39	7	1	2	1	25000	1	1	12	20	17	40	5	12	40	3	2	32	2	8	2	10
1	1	4	1874.28	40	1	2	2	1	24540	2	7	10	15	1	3	1	8	42	1	2	52	2	13	2	21.97619
1	1	4	0	43	1	2	0	1	31000	1	1	8	11	1	1	1	8	37	1	2	38	2	14	2	NA
1	1	4	0	43	1	1	0	1	18750	3	5	9	13	5	14	4	5	50	1	2	46	2	14	2	NA
1	1	4	1385.38	39	1	2	2	1	27000	1	1	10	16	17	40	5	10	40	1	2	51	2	12	2	12
1	1	4	1456.96	44	1	1	2	1	60000	3	4	6	8	1	1	1	6	60	1	1	48	2	14	2	27.25
1	1	4	1458.29	40	7	1	2	1	21000	1	1	8	11	17	40	5	8	40	6	2	24	2	7	2	11.27
1	4	4	1218.64	43	1	1	2	1	36000	3	3	10	15	8	21	2	10	50	1	2	30	2	13	2	9.7
1	4	4	0	44	1	1	0	1	62000	1	1	10	15	1	3	1	10	50	1	2	44	2	15	2	NA
1	1	4	1775.77	39	5	2	2	1	19000	1	1	5	7	22	51	10	5	39	1	2	25	2	6	2	9.37
1	4	4	1891.63	43	3	2	2	1	36000	1	1	10	15	8	21	2	10	52	1	2	50	2	10	2	12.942308
1	4	4	1952.37	42	1	1	0	1	22500	3	4	10	15	8	21	2	10	40	1	2	61	2	13	2	13.275
1	1	4	0	37	1	1	0	1	25009	1	1	8	11	16	38	4	8	40	1	2	21	2	6	2	NA
1	3	4	0	43	2	2	0	1	32000	1	1	10	15	17	40	5	10	40	1	2	48	2	14	2	NA
1	1	4	2028.95	43	4	1	2	1	111915	1	1	8	11	1	1	1	8	40	1	2	55	2	15	2	47.475
1	1	4	0	43	1	2	0	1	33000	3	3	9	13	9	23	2	9	40	1	2	31	1	15	2	NA
1	1	4	1968.76	43	5	1	2	1	80000	1	1	8	11	16	39	4	8	40	1	2	45	2	13	2	38.45
1	1	4	1648.29	41	1	1	2	1	42000	1	1	3	3	19	45	7	4	50	1	2	37	2	15	2	19.6
1	1	4	1751.22	39	1	1	2	1	9817	1	1	4	4	21	49	9	4	48	2	2	34	2	13	2	11.7
1	4	4	0	40	1	2	0	1	55000	1	1	4	4	21	49	9	10	40	1	2	50	2	16	2	NA
1	1	4	0	44	1	2	0	1	100000	1	1	6	9	2	6	1	6	40	1	2	31	2	15	2	NA
1	1	4	0	39	1	2	0	1	40000	1	1	8	11	17	40	5	8	40	1	2	32	2	14	2	NA
1	1	4	1499.82	39	1	1	2	1	34000	1	1	4	4	21	49	9	4	40	1	2	56	2	11	2	15.35
1	1	4	1294.75	39	1	1	2	1	70000	1	1	4	4	21	49	9	4	48	1	2	28	2	13	2	18.2
1	1	4	1429.87	43	1	1	2	1	68000	1	1	12	20	6	17	2	12	-4	1	2	60	2	14	1	NA
1	1	4	1303.21	41	1	2	2	1	28000	1	1	10	16	10	27	2	10	36	1	2	25	2	13	2	15.15
1	1	4	1303.21	43	1	2	2	1	1350	4	10	5	7	16	39	4	5	25	1	2	25	2	6	1	7.5
1	1	4	1497.67	39	7	2	2	1	450	3	6	12	19	15	37	3	12	28	1	2	19	2	6	1	3
1	1	4	1164.5	41	1	1	2	1	50000	1	1	5	7	17	40	5	5	40	1	2	40	2	8	2	10.25
1	1	4	0	37	7	1	0	1	10000	1	1	5	7	22	51	10	5	-4	1	2	30	2	8	1	NA
1	1	4	1441.93	39	1	1	2	1	37000	1	1	3	3	20	48	8	3	35	1	2	45	2	13	2	15
1	3	4	1230.93	43	5	2	2	1	36684	1	1	10	16	6	17	2	10	40	1	2	56	2	8	2	17.12
1	1	4	3354.56	46	1	2	2	1	80000	1	1	10	16	10	24	2	10	40	1	2	32	2	16	2	38.45
1	1	4	3111.43	42	1	2	2	1	6000	4	8	11	18	13	33	3	11	10	1	2	54	2	14	1	10
1	4	4	3932.32	39	1	2	0	1	40000	1	1	10	15	8	22	2	10	40	3	2	30	2	4	2	18.38
1	1	4	3457.34	40	7	1	2	1	19000	1	1	10	16	11	28	3	10	35	1	2	33	2	10	2	9.83
1	4	4	2764.54	44	1	2	0	1	50000	1	1	10	15	8	21	2	10	50	1	2	55	2	15	2	19.24
1	1	4	3457.34	39	1	1	2	1	7500	2	7	5	7	13	32	3	5	25	1	2	30	1	2	1	6
1	1	4	0	41	5	2	0	1	36400	1	1	5	7	16	39	4	5	50	1	2	32	2	8	2	NA
1	1	4	0	39	1	2	0	1	30000	1	1	4	5	22	51	10	4	40	1	2	49	2	15	2	NA
1	4	4	0	42	5	2	0	0	0	5	13	15	23	24	53	5	13	8	1	2	31	2	1	1	NA
1	1	4	3802	39	1	2	2	0	0	5	13	15	23	24	53	2	9	-4	4	2	30	2	12	1	NA
1	1	4	3554.85	43	1	1	2	1	45000	1	1	10	15	12	31	3	5	35	1	2	32	2	13	2	13.742857
1	1	4	2997.16	39	1	1	2	1	60000	1	1	6	8	22	51	10	6	40	1	2	54	2	13	2	23
1	1	4	2934.35	39	5	1	2	1	75000	1	1	4	4	22	51	10	6	65	1	2	49	2	11	2	17.753846
1	1	4	0	39	5	2	0	1	2200	4	12	10	16	15	37	3	10	25	1	1	55	2	1	1	NA
1	1	4	0	43	1	2	0	1	64000	1	1	10	16	11	28	3	10	40	1	2	44	2	16	2	NA
1	1	4	0	40	7	1	0	1	8700	1	1	5	7	16	39	4	5	40	2	2	25	2	3	2	NA
1	1	4	0	43	1	1	0	1	22000	3	4	4	4	2	6	1	4	50	1	2	32	2	9	2	NA
1	4	4	0	43	2	2	0	1	32000	1	1	10	15	8	21	2	10	45	1	2	29	2	12	2	NA
1	1	4	3430.88	39	1	2	2	1	10000	1	1	9	14	17	40	5	9	40	1	2	26	2	8	2	11.8
1	1	4	4042.75	44	1	1	2	1	60000	1	1	9	13	3	8	2	8	-4	4	2	31	2	12	1	NA
1	1	4	3449.16	43	1	2	2	1	50000	1	1	5	7	9	23	2	9	40	1	2	32	2	8	2	38.45
1	1	4	4285.23	40	7	2	2	1	67000	1	1	8	11	2	5	1	8	40	2	2	53	2	14	2	32.2
1	4	4	6155.08	44	7	2	1	1	15900	1	1	10	15	8	21	2	10	-4	2	2	24	2	7	1	NA
1	3	4	3199.18	43	1	2	2	1	51000	1	1	13	21	17	40	5	13	40	1	2	54	2	14	2	24.525
1	4	4	3423.73	44	1	2	2	1	60000	1	1	10	15	8	21	2	10	45	1	2	60	2	16	2	26.488889
1	1	4	0	39	7	2	0	1	30000	1	1	10	16	15	37	3	10	40	1	2	27	2	11	2	NA
1	1	4	0	42	1	2	0	1	55380	1	1	10	16	6	17	2	10	40	1	2	55	2	14	2	NA
1	1	4	3499.34	42	5	2	2	1	29000	1	1	10	16	17	40	5	10	40	1	2	61	2	16	2	13.68
1	1	4	0	40	5	2	0	1	38000	1	1	8	11	17	40	5	8	40	1	2	49	2	10	2	NA
1	1	4	4099.6	43	1	2	2	1	52000	1	1	10	16	10	25	2	10	36	1	2	24	2	12	2	41.25
1	1	4	3738.99	39	7	1	2	1	30000	1	1	6	8	22	51	10	6	40	1	1	20	2	14	2	13
1	1	4	0	41	1	1	0	1	65000	1	1	3	3	19	42	7	3	40	1	2	46	2	13	2	NA
1	4	4	0	39	5	2	0	1	20000	1	1	13	21	17	40	5	13	40	1	2	37	2	7	2	NA
1	3	4	5371.02	43	1	2	0	1	36000	1	1	10	15	8	21	2	10	55	1	2	33	2	9	2	15.109091
1	4	4	4833.91	40	1	2	2	1	9000	2	7	10	15	8	21	2	10	35	1	2	44	2	9	2	8.571429
1	1	4	4974.39	40	1	2	2	2	1300	2	7	12	20	15	37	3	12	-4	1	2	41	2	13	1	NA
1	1	4	6529.75	39	4	2	2	1	24151	1	1	5	7	16	38	4	5	40	1	2	62	2	8	2	11.71
1	1	4	5379.57	43	5	2	2	1	50000	1	1	10	16	10	27	2	10	40	1	2	55	2	12	2	26
1	1	4	5510.56	45	1	1	2	1	80000	1	1	8	11	1	1	1	8	45	1	2	42	2	15	2	34.177778
1	4	4	4936.15	42	1	2	2	1	60000	1	1	1	1	2	6	5	10	32	1	2	44	2	15	1	9.59375
1	1	4	6871.74	43	1	1	2	1	8000	2	7	12	20	6	17	2	12	44	1	2	24	2	11	2	13
1	1	4	5484.62	44	1	2	2	1	17500	1	1	12	20	17	40	1	10	36	1	2	30	2	11	2	16.027778
1	1	4	4113.66	39	1	2	2	1	30000	1	1	10	16	17	40	5	10	40	6	2	46	1	13	2	15.25
1	1	4	5725.23	43	5	1	2	1	68000	1	1	8	11	2	6	1	8	45	1	2	33	2	13	2	32.044444
1	1	4	0	40	1	1	0	1	70000	1	1	4	5	20	48	8	4	40	1	2	47	2	10	2	NA
1	1	4	0	43	3	1	0	1	50000	1	1	3	3	16	39	4	3	40	4	2	38	2	12	2	NA
1	1	4	0	41	1	2	0	1	12000	2	7	5	7	17	40	5	5	25	2	2	48	2	13	1	NA
1	1	4	0	40	1	1	0	1	55000	1	1	7	10	3	8	2	7	60	1	2	37	2	13	2	NA
1	3	4	5086.23	40	1	2	2	1	28000	1	1	10	16	10	27	2	10	60	2	2	50	2	12	2	11.5
1	1	4	7379.32	39	5	1	0	1	75000	1	1	4	4	22	51	10	4	60	1	2	50	2	15	2	24.033333
1	1	4	6655.28	39	7	2	2	1	1200	4	12	5	7	16	39	3	11	25	1	2	22	1	8	1	2.18
1	1	4	0	40	1	2	0	1	32000	1	1	10	16	11	28	3	10	35	2	2	28	2	11	2	NA
1	1	4	6361.79	43	1	2	2	1	28000	1	1	7	10	9	23	2	7	40	1	1	36	2	13	2	14.52
1	1	4	5904.49	40	7	2	2	1	5850	2	7	11	18	13	32	3	11	20	6	2	20	1	4	1	5.15
1	4	4	6427.45	43	7	2	0	1	28300	1	1	10	15	8	21	2	10	40	1	2	23	2	10	2	13.6
1	3	4	6045.83	43	7	1	2	1	16000	2	7	10	15	8	20	2	10	20	4	2	29	2	6	1	16.5
1	3	4	6245.94	40	7	2	2	1	8000	2	7	10	15	17	40	5	10	20	1	2	23	2	7	1	7.95
1	3	4	5606.65	43	1	1	2	1	36000	1	1	10	15	8	21	2	10	-4	1	2	30	2	14	1	NA
1	1	4	0	34	1	1	0	1	20000	3	3	4	4	21	49	9	4	60	1	2	37	2	13	2	NA
1	1	4	8201.97	39	7	1	2	0	0	5	13	15	23	24	53	3	11	32	6	2	19	2	7	1	7.5
1	3	4	5272.11	39	1	2	2	1	25000	1	1	13	21	17	40	5	13	40	1	2	37	2	14	2	12.98
1	1	4	0	43	1	1	0	1	37000	1	1	10	16	12	31	3	10	40	2	2	60	1	12	2	NA
1	1	4	0	40	1	1	0	1	60000	1	1	7	10	20	48	8	7	40	1	2	35	2	13	2	NA
1	1	4	5613.4	39	1	2	2	1	35000	1	1	8	11	1	1	1	5	65	1	2	58	2	13	2	15.26
1	4	5	2895.83	39	1	1	2	1	31608	2	7	10	15	22	51	10	10	-4	6	2	31	2	13	1	17
1	1	5	1747.38	39	1	1	2	1	26000	4	8	6	8	22	51	10	9	40	1	2	44	2	9	2	10.75
1	3	5	1580.05	39	1	2	2	1	24600	1	1	10	15	17	40	5	10	-4	1	2	44	2	9	1	NA
1	1	5	2179	38	7	1	2	0	0	5	13	15	23	24	53	10	11	35	1	2	20	2	14	2	11
1	1	5	0	39	5	1	0	1	20002	1	1	12	20	20	48	8	12	40	2	2	49	2	2	2	NA
1	4	5	1813.18	39	7	1	0	1	17010	4	9	10	15	22	51	10	10	30	2	2	56	2	10	1	13.5
1	1	5	2496.75	39	7	1	0	1	37000	1	1	6	8	22	51	10	6	43	2	2	30	2	13	2	18
1	1	5	1576.61	40	1	1	2	1	48000	1	1	9	14	14	34	3	9	35	1	2	48	2	14	2	9
1	1	5	1610.36	37	7	1	2	1	14000	1	1	3	3	22	51	10	3	40	1	2	50	2	14	2	17
1	1	5	1233.32	42	1	2	2	1	30000	1	1	10	16	10	27	2	10	35	4	2	47	2	14	2	35
1	1	5	1490.82	39	1	2	2	1	65000	1	1	4	5	21	49	9	4	36	1	2	55	2	10	2	11.87
1	1	5	1628.16	44	1	1	2	1	83500	1	1	9	13	3	8	2	9	50	1	2	55	2	15	2	33.84
1	1	5	1552.06	42	4	2	2	1	1202	4	12	3	3	17	40	5	3	24	1	2	64	2	14	1	14.416667
1	1	5	2349.39	39	7	1	2	1	40000	1	1	7	10	20	48	8	7	40	2	2	42	2	15	2	15.05
1	1	5	1628.16	37	1	1	0	1	50000	1	1	8	12	20	48	8	12	40	1	2	57	2	14	2	14.4
1	1	5	1677.88	40	4	2	2	1	35000	1	1	8	11	1	1	1	8	40	1	2	54	2	11	2	19.225
1	4	5	1755.62	43	1	2	2	1	38000	1	1	13	21	17	40	5	13	38	1	2	31	2	14	2	19.5
1	1	5	1610.36	40	1	1	2	1	29000	1	1	4	5	21	49	9	4	48	1	2	53	2	13	2	35
1	1	5	2041.6	40	7	2	2	1	25000	1	1	9	13	17	40	5	9	40	1	2	23	2	15	2	7.5
1	1	5	2041.6	39	7	2	2	2	13000	3	3	10	16	15	37	2	10	40	1	2	22	2	13	2	8
1	1	5	0	39	7	1	0	1	55000	1	1	10	16	20	48	8	10	-4	1	1	51	2	14	1	NA
1	1	5	0	40	7	2	0	1	6000	2	7	10	16	11	28	3	10	15	2	2	18	2	13	1	NA
1	1	5	0	40	5	2	0	1	31000	1	1	10	16	11	28	3	10	40	2	2	56	2	14	2	NA
1	1	5	0	40	7	2	0	1	30000	1	1	10	16	2	5	1	9	40	2	2	36	2	14	2	NA
1	1	5	0	43	1	2	0	1	49000	1	1	10	15	8	21	2	10	40	1	2	28	2	16	2	NA
1	2	5	0	39	1	1	0	1	125000	1	1	6	8	22	50	10	6	-4	1	2	42	2	14	1	NA
1	1	5	0	40	7	2	0	1	36000	1	1	8	11	1	1	1	8	40	2	2	63	2	12	2	NA
1	1	5	1671.42	40	1	1	2	1	43000	1	1	5	6	16	38	4	5	40	1	2	41	2	12	2	21
1	3	5	1858.3	42	1	1	2	1	11000	3	5	13	21	3	8	2	13	37	1	2	27	2	13	2	11
1	1	5	0	43	1	2	0	1	80000	1	1	9	13	17	40	5	9	16	1	2	36	2	14	1	NA
1	1	5	0	39	1	1	0	1	42906	1	1	10	15	14	35	3	11	40	2	2	53	2	6	2	NA
1	1	5	0	39	7	1	0	1	4000	3	5	12	20	22	51	10	12	35	1	1	20	1	12	2	NA
1	1	5	0	43	7	2	0	1	30000	1	1	12	20	17	40	5	12	40	2	2	31	2	12	2	NA
1	3	5	0	43	1	2	0	1	75000	1	1	13	21	12	30	3	13	40	1	2	48	2	15	2	NA
1	1	5	6201.24	45	1	2	2	1	11000	2	7	9	13	7	18	2	9	5	1	2	42	2	15	1	42.4
1	1	5	0	39	6	2	0	1	30000	3	4	10	16	11	28	3	10	40	2	2	19	2	13	2	NA
1	1	5	6332.97	40	1	1	2	1	70000	1	1	3	3	2	5	1	3	50	1	2	56	1	16	2	33.46
1	1	5	7770.43	43	1	1	2	1	38000	1	1	11	17	9	23	2	11	35	1	2	34	2	16	2	20.885714
1	1	5	0	41	4	1	0	1	104000	1	1	9	13	4	12	2	4	40	6	2	51	2	8	2	NA
1	2	5	8142.25	43	7	2	0	1	39000	3	4	13	21	17	40	5	13	40	2	2	43	2	8	2	20
1	1	5	9974.79	39	1	1	2	0	0	5	13	15	23	24	53	10	6	40	2	2	48	2	8	2	10.25
1	1	5	10014.99	39	1	2	2	1	566972	1	1	10	16	1	3	1	10	40	2	2	61	2	14	2	7.55
1	1	5	9580.28	39	7	1	2	1	15000	1	1	5	7	17	40	5	5	40	2	2	44	2	8	2	16.5
1	1	5	6689.77	39	1	1	2	1	55000	1	1	3	3	19	42	7	3	45	1	2	56	2	12	2	16.43
1	2	5	9851.96	40	7	1	0	1	25000	1	1	13	21	12	30	3	13	42	1	2	24	2	11	2	22.880952
1	1	5	0	40	1	2	0	1	29000	1	1	8	11	1	1	1	8	40	1	2	29	2	13	2	NA
1	4	5	7769.13	43	1	1	2	1	75000	1	1	13	21	12	29	3	13	40	1	2	39	2	14	2	36.05
1	4	5	6201.24	40	1	2	2	1	87000	1	1	4	4	3	8	2	13	50	1	2	40	2	16	2	28.84
1	1	5	6573.26	44	1	2	2	1	60000	1	1	10	16	1	3	1	10	60	1	2	49	2	16	2	19.233333
1	1	5	6332.97	40	1	1	2	1	78002	1	1	9	13	2	7	1	9	40	1	2	55	2	15	2	15.5
1	1	5	0	42	1	2	0	1	15000	4	9	10	15	22	51	10	10	27	1	2	36	2	12	1	NA
1	1	5	0	39	1	1	0	1	42000	1	1	3	3	20	48	8	3	40	1	2	37	2	12	2	NA
1	1	5	6433.62	40	1	1	2	1	75000	1	1	7	10	12	29	3	7	40	2	2	39	2	15	2	20
1	1	5	8315.32	39	1	1	0	1	36000	1	1	3	3	21	49	9	3	40	1	2	29	2	13	2	15
1	2	5	9442.78	43	1	1	2	1	121000	1	1	6	8	2	5	1	6	40	2	2	49	2	15	2	28
1	2	5	0	40	3	2	0	1	75000	1	1	13	21	1	1	1	13	40	2	2	53	1	14	2	NA
1	1	5	6890.27	40	1	2	2	1	9000	4	9	11	18	13	33	3	11	18	1	2	31	2	12	1	8
1	1	5	8315.32	40	1	1	2	1	32500	3	3	4	4	19	44	7	4	50	1	2	29	2	12	2	14.76
1	1	5	6332.97	43	1	1	2	1	65000	1	1	9	14	3	8	2	9	60	1	2	57	2	15	2	22.433333
1	1	5	5043.13	39	1	2	2	1	24000	1	1	5	6	20	48	8	5	40	4	2	47	2	15	2	12.025
1	1	5	7770.43	42	1	1	2	1	70000	1	1	5	7	20	48	8	3	40	1	2	32	2	14	2	12.75
1	4	5	6689.77	44	1	1	0	1	80000	3	3	10	15	8	21	2	10	60	1	2	59	2	14	2	28.983333
1	1	5	0	40	1	2	0	1	39000	1	1	12	20	17	40	5	10	40	1	2	41	2	15	2	NA
1	2	5	0	43	1	2	0	1	106000	1	1	13	21	1	1	1	13	48	3	2	43	2	16	2	NA
1	1	5	6096.07	43	1	2	2	1	56000	1	1	10	16	17	40	5	10	40	1	2	58	2	15	2	26.925
1	1	5	6616.64	43	7	1	2	1	77500	1	1	9	13	3	8	2	9	40	1	2	54	2	14	2	51.325
1	1	5	5848.5	41	1	1	2	1	46000	1	1	12	20	20	48	8	12	40	1	1	53	2	16	2	23.25
1	1	5	6545.73	35	3	1	2	1	45000	1	1	5	7	19	46	7	5	45	1	1	45	2	11	2	19.222222
1	1	5	0	43	1	2	0	1	30000	2	7	12	19	17	40	5	12	20	1	2	38	2	16	1	NA
1	1	5	0	40	1	1	0	1	34000	1	1	11	18	21	49	9	11	40	1	1	36	2	10	2	NA
1	1	5	0	42	7	2	0	1	3000	4	11	5	7	16	39	4	5	25	2	2	26	2	15	1	NA
1	1	5	0	44	1	1	0	1	109000	1	1	9	13	3	8	2	9	40	2	2	30	2	15	2	NA
1	1	5	0	39	1	2	0	1	36000	1	1	9	13	5	16	2	9	40	4	2	51	2	14	2	NA
1	1	5	0	43	1	2	0	1	42000	1	1	7	10	20	48	8	7	60	1	2	31	2	16	2	NA
1	1	5	0	44	7	2	0	1	87000	4	11	9	14	10	25	2	9	-4	2	2	36	2	10	1	NA
1	1	5	9069.31	43	1	1	2	1	29000	1	1	8	11	17	40	5	8	40	2	2	41	2	12	2	13.45
1	1	5	0	31	3	1	0	1	30000	1	1	3	3	19	44	7	3	40	1	1	32	2	6	2	NA
1	1	5	0	40	7	2	0	1	8450	2	7	7	10	1	3	1	7	25	2	2	22	2	7	1	NA
1	1	5	0	43	7	1	0	1	80000	1	1	8	11	1	1	1	8	60	2	2	37	2	15	2	NA
1	1	5	0	42	1	2	0	1	60000	1	1	10	16	17	40	5	10	40	2	2	54	2	14	2	NA
1	1	5	1194.29	45	7	1	2	1	30000	3	3	9	13	7	18	2	9	40	1	1	26	2	11	2	20
1	1	5	1202.37	43	7	1	2	1	3600	4	10	10	15	17	40	5	10	-4	1	2	26	2	6	1	10
1	1	5	3004.5	40	1	1	2	0	0	5	13	15	23	24	53	7	3	40	2	1	55	2	8	2	16.35
1	1	5	1180.94	45	5	2	2	1	130000	1	1	9	13	7	18	2	9	45	1	2	29	2	16	2	44.866667
1	1	5	1081.45	44	7	1	2	1	80000	1	1	12	20	1	3	1	12	40	1	2	42	2	14	2	39.9
1	1	5	1245.82	44	5	1	2	1	115000	1	1	8	11	3	8	2	8	50	1	1	36	2	16	2	46.16
1	1	5	1039.62	36	7	2	2	1	36000	1	1	11	18	13	33	3	11	40	2	2	44	2	3	2	14.2
1	1	5	1141.23	43	7	2	2	1	60000	1	1	7	10	17	40	5	7	40	1	2	35	2	12	2	25
1	1	5	1180.94	44	7	2	2	1	140000	1	1	8	11	1	1	1	8	-4	1	2	28	2	16	1	NA
1	1	5	1202.37	43	7	1	2	1	25000	3	3	11	18	13	33	3	11	40	1	2	28	2	14	2	25
1	2	5	1856.5	46	1	1	0	1	409068	1	1	13	21	17	40	5	13	50	1	2	56	2	16	2	57.7
1	1	5	1365.14	39	1	1	2	1	12500	4	12	9	14	14	35	7	3	30	2	2	34	2	13	1	12.97
1	1	5	1068.51	43	1	2	2	0	0	5	13	15	23	24	53	2	10	-4	1	2	44	2	15	1	20
1	1	5	1081.45	45	1	1	2	1	180000	1	1	4	4	7	18	2	4	50	1	2	40	2	16	2	57.7
1	1	5	1081.45	44	1	1	2	1	100000	1	1	9	13	2	5	1	9	45	1	2	42	2	15	2	42.733333
1	1	5	1187.24	44	1	2	2	1	90500	1	1	9	13	2	5	1	9	45	1	2	30	2	16	2	38.466667
1	1	5	1123.39	43	7	1	2	1	45000	1	1	11	17	1	3	1	11	40	1	2	35	2	15	2	21.625
1	1	5	1141.23	43	1	2	2	1	50000	3	4	11	18	16	39	4	11	10	1	2	35	2	16	1	11.5
1	1	5	1081.45	43	5	1	2	1	92950	1	1	12	20	1	1	1	12	45	1	2	44	2	12	2	39.733333
1	1	5	1068.51	43	7	2	2	1	71000	1	1	9	13	2	5	1	9	70	1	2	43	2	14	2	19.5
1	1	5	1512.09	39	7	1	2	1	45000	1	1	3	3	19	46	7	3	40	1	1	32	2	12	2	30
1	2	5	1380.63	43	7	2	2	0	0	5	13	15	23	24	53	2	11	40	1	2	22	2	9	2	14.425
1	1	5	1068.51	44	7	2	2	1	20000	1	1	12	20	6	17	2	12	50	1	2	44	2	9	2	11.54
1	1	5	0	35	7	1	0	1	12000	1	1	11	18	13	32	3	11	40	1	1	22	2	6	2	NA
1	1	5	0	35	3	2	0	1	7800	1	1	11	18	16	39	4	11	35	1	1	30	2	2	2	NA
1	1	5	0	39	7	1	0	1	42000	1	1	3	3	17	40	5	3	40	2	2	54	2	10	2	NA
1	2	5	0	35	7	1	0	1	10000	4	9	10	16	14	35	3	10	20	2	2	41	2	5	1	NA
1	1	5	0	43	1	1	0	1	125000	1	1	12	20	1	1	1	12	37	2	2	64	2	15	2	NA
1	2	5	0	40	1	2	0	1	55000	1	1	13	21	1	2	1	13	40	2	2	35	2	15	2	NA
1	1	5	0	43	7	1	0	1	23000	1	1	9	14	14	35	3	9	40	2	2	28	2	9	2	NA
1	2	5	0	40	7	2	0	1	46000	1	1	6	8	17	40	5	6	40	2	2	46	2	11	2	NA
1	1	5	0	39	4	2	0	1	8000	2	7	12	20	17	40	5	12	20	2	2	62	2	6	1	NA
1	2	5	0	44	1	1	0	1	95000	1	1	13	21	1	3	1	13	50	3	2	47	2	11	2	NA
1	2	5	0	44	1	1	0	1	70000	1	1	13	21	1	3	1	13	45	1	2	46	2	15	2	NA
1	1	5	0	38	7	2	0	1	5000	4	11	11	18	13	32	3	11	30	2	1	18	2	11	1	NA
1	1	5	0	39	1	1	0	1	83000	1	1	10	16	20	48	8	10	40	1	2	45	2	14	2	NA
1	1	5	0	39	5	2	0	1	20000	1	1	9	13	17	40	5	9	40	1	2	47	2	11	2	NA
1	1	5	14321.48	40	1	1	2	1	9000	1	1	4	4	21	49	9	4	40	1	2	26	2	7	2	21.82
1	1	5	0	39	7	2	0	1	30000	1	1	10	16	11	28	3	10	40	2	2	21	2	7	2	NA
1	1	5	11577.62	40	1	2	2	1	14000	2	7	10	16	17	40	5	10	23	1	2	48	2	14	1	9.5
1	4	5	14075.08	43	5	2	2	1	35000	1	1	10	16	15	37	3	10	40	1	2	32	2	14	2	14.4
1	3	5	12667.56	40	1	2	2	1	50000	1	1	13	21	1	2	1	13	40	1	2	42	2	15	2	24.05
1	1	5	12667.56	42	7	2	2	1	85000	1	1	10	16	10	25	2	10	40	1	2	42	2	14	2	15.28
1	3	5	0	42	1	2	0	1	38000	1	1	13	21	2	5	1	13	40	1	2	55	2	15	2	NA
1	1	5	0	40	1	1	0	1	30000	1	1	3	3	19	45	7	10	40	1	2	43	2	14	2	NA
1	1	5	0	39	1	2	0	1	18000	3	3	10	15	8	22	2	10	40	1	2	43	2	14	2	NA
1	3	5	0	40	7	2	0	1	6000	2	7	10	15	6	17	2	10	20	4	2	19	2	5	1	NA
1	2	5	0	43	1	2	0	1	15000	1	1	13	21	2	5	1	13	48	1	2	45	2	10	2	NA
1	1	5	9759.72	39	1	2	2	1	36000	1	1	10	16	17	40	5	10	40	1	2	59	2	11	2	17.5
1	3	5	0	43	1	2	0	1	60000	1	1	9	13	2	5	1	9	40	1	2	37	2	15	2	NA
1	3	5	0	39	5	2	0	1	40000	1	1	13	21	12	29	3	13	40	1	2	53	2	14	2	NA
1	1	5	13520.24	39	7	1	2	1	30000	1	1	7	10	17	40	5	7	-4	1	2	33	2	11	1	9.95
1	1	5	0	39	7	1	0	1	25000	2	7	9	14	20	48	8	9	-4	2	2	26	2	13	1	NA
1	1	5	0	39	1	1	0	1	40000	1	1	6	8	22	51	10	6	40	2	2	45	2	14	2	NA
1	1	5	0	40	7	1	0	1	18000	3	4	9	14	17	40	5	9	40	2	2	29	2	4	2	NA
1	1	5	19560.79	40	7	1	2	1	2250	4	12	3	3	19	44	7	3	-4	2	2	20	2	11	1	7.5
1	1	5	12443.72	39	7	2	2	1	27800	1	1	5	7	17	40	5	5	40	1	2	41	2	8	2	14.9
1	1	5	18195.27	39	7	1	2	1	25000	1	1	3	3	19	44	7	3	40	1	1	30	2	13	2	11
1	1	5	9393.98	44	1	1	2	1	115000	1	1	6	8	1	1	1	6	55	1	2	47	2	14	2	40.218182
1	4	5	10984.35	44	5	2	2	1	59000	1	1	10	15	8	21	2	10	40	1	2	51	2	12	2	28.375
1	1	5	12443.72	43	1	2	2	1	42000	1	1	10	16	10	25	2	10	40	1	2	44	2	16	2	28.35
1	3	5	0	43	2	2	0	1	37000	1	1	10	15	8	21	2	10	40	1	2	27	2	14	2	NA
1	1	5	9467.59	45	1	1	2	1	409068	1	1	10	16	10	24	2	10	40	1	2	47	2	15	2	72.125
1	1	5	13394.78	45	1	2	2	1	433605	1	1	9	13	7	18	2	9	50	1	2	36	2	16	2	57.7
1	1	5	18302.38	33	1	1	2	1	19000	1	1	3	3	19	44	7	3	35	1	1	31	2	10	2	10
1	1	5	9758.21	44	1	1	2	1	132417	1	1	9	13	1	3	1	9	50	1	2	56	2	16	2	50.92
1	1	5	0	40	1	1	0	1	20000	1	1	12	20	16	39	4	12	40	2	2	50	2	10	2	NA
1	2	5	0	43	1	1	0	1	79000	1	1	13	21	7	19	2	13	40	4	2	40	2	14	2	NA
1	1	5	16173.28	40	1	2	2	1	38000	1	1	6	8	1	2	1	6	40	1	2	24	2	15	2	18.275
1	1	5	16173.28	41	7	2	2	0	0	5	13	15	23	24	53	3	10	30	1	2	20	2	15	1	12.5
1	1	5	0	40	1	1	0	1	40000	1	1	6	8	17	40	5	6	40	2	2	45	2	14	2	NA
1	2	5	9836.19	46	1	2	2	1	145000	1	1	13	21	7	18	2	13	40	1	2	55	2	16	2	72
1	2	5	10240.25	46	7	2	2	1	122984	1	1	13	21	1	3	1	13	50	1	2	63	2	16	2	47.3
1	1	5	0	39	1	2	0	1	8000	1	1	12	19	14	35	3	12	40	1	1	48	2	8	2	NA
1	1	5	11704.52	39	4	2	2	1	35000	1	1	10	16	14	35	3	10	40	1	2	60	2	7	2	16.57
1	2	5	4271.56	40	1	2	2	1	41700	1	1	13	21	17	40	5	13	-4	1	2	58	2	13	1	21.31
1	4	5	5424.56	39	7	1	2	1	2400	3	6	6	9	22	51	10	6	40	1	2	19	2	14	2	7.5
1	3	5	0	40	1	1	0	1	25000	1	1	13	21	12	30	3	13	40	1	2	32	2	8	2	NA
1	1	5	5144.98	39	5	2	2	1	12000	1	1	10	16	17	40	5	10	35	1	2	61	2	8	2	8.24
1	1	5	0	39	1	1	0	1	120000	1	1	2	2	19	47	10	3	40	1	2	29	2	2	2	NA
1	1	5	5693.26	39	1	1	2	1	16800	1	1	10	15	14	35	3	10	41	1	2	35	2	9	2	15.756098
1	1	5	4993.34	44	1	2	0	1	56000	1	1	10	15	8	21	2	10	40	1	2	53	2	15	2	26.925
1	1	5	5483.49	44	1	2	2	1	65000	3	4	10	16	10	25	2	10	20	1	2	36	2	15	1	45
1	4	5	0	39	1	1	0	1	70000	1	1	13	21	12	30	3	13	40	1	2	52	2	13	2	NA
1	3	5	0	39	1	2	0	1	35000	1	1	13	21	17	40	5	13	40	2	2	53	2	10	2	NA
1	1	5	5147.14	39	1	2	2	1	26000	1	1	5	7	17	40	5	5	40	1	2	42	2	14	2	20.75
1	2	5	4542.04	40	1	1	2	1	60000	1	1	13	21	21	49	9	13	40	1	2	60	2	15	2	28.75
1	1	5	5704.53	43	5	2	2	1	39000	1	1	10	16	10	25	2	10	-4	1	2	33	2	12	1	NA
1	3	5	0	40	1	2	0	1	33000	1	1	13	21	6	17	2	13	40	1	2	53	2	3	2	NA
1	4	5	4820.14	41	4	2	0	1	18000	3	4	10	15	2	5	1	10	35	1	2	54	2	3	2	12.142857
1	4	5	4741.97	44	1	1	0	1	52000	1	1	10	15	8	21	2	10	36	1	2	56	2	15	2	27.777778
1	4	5	16517.73	43	1	2	2	1	32000	1	1	13	21	2	6	1	13	40	1	2	26	2	13	2	15.375
1	1	5	11713.86	40	1	1	2	1	50000	1	1	12	20	16	39	4	12	50	1	2	54	2	15	2	19.24
1	1	5	0	39	1	1	0	1	25000	1	1	4	4	19	43	7	4	40	1	1	29	2	3	2	NA
1	1	5	12296.01	43	1	1	2	1	120000	1	1	4	4	1	1	1	4	45	1	2	59	2	15	2	43.6
1	1	5	12296.01	43	1	1	2	1	100000	1	1	13	21	5	14	2	6	50	1	2	58	2	5	2	19.22
1	1	5	16452.85	44	5	2	2	1	25000	2	7	9	13	1	3	1	9	40	1	2	34	2	12	2	17.3
1	1	5	0	35	1	1	0	1	18000	1	1	9	14	14	35	3	9	40	1	1	31	2	15	2	NA
1	1	5	0	39	7	1	0	1	14000	4	10	6	8	22	51	10	6	25	2	2	25	2	8	1	NA
1	3	5	0	43	5	2	0	1	26000	1	1	10	16	6	17	2	10	40	2	2	48	2	8	2	NA
1	1	5	0	39	7	1	0	1	12000	1	1	11	18	13	32	3	11	35	2	2	44	2	10	2	NA
1	4	5	0	43	1	1	0	1	50000	1	1	13	21	12	30	3	13	40	1	2	41	2	16	2	NA
1	1	5	28833.99	43	1	1	2	1	38000	1	1	9	13	16	39	1	9	45	3	1	35	2	13	2	22.222222
1	3	5	16822.56	39	7	2	2	1	27500	1	1	13	21	17	40	5	13	40	1	2	32	2	8	2	13.225
1	1	5	12327.89	40	7	2	2	1	8820	3	4	5	7	16	39	4	5	35	2	2	35	2	7	2	6.95
1	1	5	15118.49	39	1	1	2	1	13000	3	4	4	4	21	49	9	4	40	2	2	46	2	2	2	11.5
1	1	5	20154.68	40	7	2	2	1	5000	2	7	5	7	16	39	4	5	15	6	1	22	2	10	1	7
1	1	5	13421.59	39	1	2	2	1	46000	1	1	8	11	16	39	4	8	40	1	2	46	2	13	2	20
1	1	5	0	42	1	2	0	1	15400	1	1	10	16	11	28	3	10	40	2	2	41	2	13	2	NA
1	1	5	0	39	1	2	0	1	15000	1	1	10	16	14	35	3	10	40	2	2	60	2	13	2	NA
1	1	5	11468.37	43	1	1	2	1	150000	1	1	4	5	1	3	1	4	40	1	2	49	2	15	2	36.05
1	1	5	18989.22	39	7	2	2	1	10000	4	11	12	20	15	37	3	12	30	1	2	20	2	15	1	10
1	1	5	11517.66	39	1	2	2	1	10000	4	9	9	13	17	40	5	9	30	1	2	58	2	14	1	7.766667
1	4	5	0	43	2	2	0	1	45000	1	1	10	15	8	21	2	10	40	1	2	38	2	13	2	NA
1	1	5	13807.12	39	1	2	2	1	20000	1	1	4	5	21	49	9	4	40	1	2	61	2	12	2	10.7
1	3	5	0	44	1	2	0	1	58000	1	1	10	15	8	21	2	10	40	3	2	51	2	15	2	NA
1	3	5	0	43	1	1	0	1	25500	4	9	10	15	8	21	2	10	25	3	2	59	2	11	1	NA
1	1	5	19188.9	40	7	1	2	1	36000	1	1	3	3	21	49	9	3	40	2	2	26	2	13	2	15
1	1	5	11906.73	39	1	1	2	1	24024	1	1	9	14	14	35	3	9	40	1	2	46	2	2	2	11.55
1	3	5	13426.32	44	7	2	2	1	43000	1	1	13	21	17	40	5	13	40	2	2	32	2	11	2	20.675
1	3	5	11894.32	44	1	1	2	1	70000	1	1	10	15	21	49	9	10	50	1	2	51	2	15	2	23.08
1	1	5	11645.05	43	1	1	2	1	30000	1	1	5	6	17	40	5	5	40	1	2	47	2	16	2	57.7
1	3	5	16365.62	43	7	2	2	1	42000	1	1	10	15	8	21	2	10	40	1	2	28	2	15	2	10
1	1	5	0	37	1	1	0	1	12000	1	1	9	14	14	35	3	9	-4	1	1	39	2	6	1	NA
1	1	5	0	39	5	2	0	1	14500	1	1	4	5	21	49	9	4	40	2	2	48	2	11	2	NA
1	1	5	0	40	7	2	0	1	2000	4	10	5	7	16	39	4	5	15	2	2	48	2	6	1	NA
1	1	5	16684.69	43	1	2	2	1	13000	2	7	8	12	17	40	5	8	8	1	2	32	2	10	1	31.25
1	1	5	11753.29	43	5	1	2	1	120000	1	1	6	9	1	3	1	6	50	1	2	49	2	15	2	48.08
1	1	5	0	43	1	2	0	1	26000	1	1	8	11	17	40	5	8	40	1	2	42	1	14	2	NA
1	4	5	15189.91	43	7	2	2	1	28000	1	1	10	16	6	17	2	10	40	2	2	29	2	9	2	13.41
1	4	5	0	43	7	2	0	1	42000	1	1	10	16	10	25	2	10	40	2	2	34	2	11	2	NA
1	1	5	0	42	1	2	0	1	30000	2	7	12	20	10	25	2	12	40	1	2	33	2	12	2	NA
1	4	5	8909.84	39	5	1	2	1	8000	4	9	10	15	22	51	10	10	25	2	2	63	2	5	1	11.27
1	1	5	8931.84	39	7	1	2	1	18720	1	1	12	20	20	48	8	12	40	1	2	46	2	16	2	9
1	1	5	0	39	1	1	0	1	70000	1	1	12	20	20	48	8	12	50	1	2	33	2	8	2	NA
1	1	5	8545.29	41	1	1	2	1	28000	1	1	5	7	20	48	8	5	50	1	2	30	2	14	2	16
1	4	5	11505.63	43	1	1	0	1	40000	1	1	13	21	6	17	2	13	40	2	2	44	2	14	2	16
1	4	5	13500.79	43	7	1	0	0	0	5	13	15	23	24	53	2	10	40	2	2	22	2	14	2	12
1	1	5	8224.88	39	1	1	2	1	35000	1	1	6	8	22	51	10	6	40	1	2	44	2	10	2	19
1	1	5	8578.53	39	5	2	2	1	50000	1	1	10	16	1	3	1	9	-4	1	2	65	2	10	1	6.25
1	1	5	10143.65	39	7	2	2	1	24200	1	1	4	4	17	40	5	4	40	1	2	21	2	7	2	11.27
1	1	5	0	39	7	2	0	1	40000	1	1	5	7	17	40	5	5	40	1	2	32	2	14	2	NA
1	1	5	0	40	3	1	0	1	53500	1	1	4	4	17	40	5	4	40	1	2	57	2	14	2	NA
1	1	5	0	40	3	2	0	1	20000	2	7	5	7	16	39	4	5	20	1	2	55	2	14	1	NA
1	1	5	8722.75	44	2	2	2	1	30000	2	7	10	16	10	25	2	10	30	1	2	33	2	13	1	18
1	4	5	8676.51	43	2	2	2	1	35000	1	1	10	15	8	21	2	10	40	1	2	26	2	13	2	16.825
1	2	5	8931.84	43	3	1	2	1	52000	1	1	3	3	19	43	7	3	40	1	2	46	2	13	2	25
1	1	5	9464.87	39	1	1	2	1	90000	1	1	4	5	1	1	1	4	40	1	2	56	2	15	2	35
1	1	5	0	43	1	1	0	1	110000	1	1	8	11	9	23	2	8	45	1	2	35	2	14	2	NA
1	1	5	0	41	1	2	0	1	26000	2	7	10	16	11	28	3	10	32	1	2	44	2	15	1	NA
1	1	5	9553.83	44	7	2	2	1	60000	2	7	9	13	7	19	2	9	40	2	2	34	2	9	2	21.625
1	1	5	8772.21	39	5	2	2	1	50000	1	1	4	4	21	49	9	4	50	2	2	39	2	14	2	14
1	1	5	0	41	5	2	0	1	53500	1	1	10	16	10	25	2	10	40	2	2	38	2	11	2	NA
1	1	5	9680.14	40	1	1	2	1	29000	1	1	12	20	20	48	8	12	40	1	2	60	2	11	2	17
1	1	5	10218.01	43	1	2	2	1	50000	1	1	10	16	10	25	2	10	36	1	2	46	2	16	2	22
1	1	5	0	41	1	2	0	1	14000	1	1	11	18	13	33	3	11	35	1	1	48	2	10	2	NA
1	1	5	10218.01	42	1	2	2	1	22000	1	1	10	16	17	40	5	8	58	1	2	46	2	15	2	21.551724
1	1	5	8759.91	39	1	1	0	1	20400	3	3	4	5	22	51	10	4	40	1	2	42	2	9	2	12.15
1	1	5	9144.51	43	1	1	2	1	39000	1	1	8	12	16	39	4	8	40	1	2	28	2	14	2	17
1	1	5	0	40	1	1	0	1	50000	1	1	4	5	21	49	9	4	-4	1	2	45	2	13	1	NA
1	1	5	10129.69	39	6	2	2	1	8000	3	2	11	18	13	33	3	11	22	2	2	33	2	4	1	7.4
1	1	5	10163.86	39	7	2	2	1	8000	2	7	5	7	16	39	4	5	25	2	2	41	2	3	1	8
1	1	5	11668.02	39	1	2	2	1	23400	3	4	8	12	16	39	4	8	40	1	2	39	2	13	2	15
1	3	5	0	37	1	1	0	1	31000	1	1	3	3	19	42	7	3	40	1	2	35	2	13	2	NA
1	1	5	10264.01	40	1	2	2	1	33000	1	1	10	16	2	6	1	10	40	2	2	42	2	12	2	15.875
1	3	5	12681.18	46	1	2	2	1	60000	3	3	13	21	7	18	2	13	40	2	2	45	2	14	2	12.5
1	3	5	12518.14	43	5	2	2	1	40000	1	1	10	15	4	12	2	10	40	1	2	55	2	15	2	19.225
1	1	5	0	45	1	2	0	1	433605	1	1	10	16	10	24	2	10	60	4	2	48	2	16	2	NA
1	1	5	14962.03	40	1	2	2	1	30000	1	1	11	18	17	40	5	11	60	1	2	47	2	10	2	9.616667
1	1	5	0	39	7	2	0	1	10000	2	7	11	18	14	35	3	11	20	2	2	59	2	8	1	NA
1	1	5	13725.93	43	7	1	2	1	50000	1	1	12	20	3	8	2	12	40	1	2	27	2	12	2	27.65
1	1	5	0	44	1	2	0	1	115000	1	1	7	10	1	1	1	7	40	2	2	41	2	14	2	NA
1	1	5	12157.14	43	1	2	2	1	28000	1	1	12	20	6	17	2	12	40	1	2	26	2	9	2	14.425
1	1	5	14367.27	39	4	2	2	1	33000	1	1	10	16	14	35	3	10	40	2	2	52	2	8	2	15.875
1	1	5	12149.22	40	1	2	2	1	40000	1	1	9	13	7	19	2	9	40	2	2	33	2	14	2	19.225
1	1	5	0	39	7	2	0	1	22500	1	1	5	7	16	39	4	5	40	2	2	23	1	7	2	NA
1	1	5	0	40	1	1	0	1	38000	3	2	6	8	17	40	5	6	40	2	2	34	2	7	2	NA
1	1	5	0	41	5	1	0	1	60000	1	1	10	15	1	3	1	10	40	2	2	60	2	15	2	NA
1	1	5	0	40	7	2	0	1	39000	1	1	9	14	16	39	4	9	40	2	2	37	2	10	2	NA
1	1	5	11929.34	33	3	1	2	1	8000	4	9	3	3	19	44	7	3	-4	1	1	50	2	7	1	8
1	1	5	11809.34	39	1	1	2	1	110000	1	1	5	6	22	51	10	5	40	2	2	39	2	1	2	10.25
1	1	5	12639.38	44	1	1	2	1	80000	1	1	7	10	1	3	1	7	50	1	1	45	2	16	2	30.76
1	1	5	12870.26	45	1	1	2	1	100000	1	1	9	13	7	18	2	9	50	1	2	43	2	16	2	27.7
1	1	5	0	44	7	2	0	1	25000	3	4	11	18	2	5	1	11	40	2	2	28	2	9	2	NA
1	1	5	0	42	7	2	0	1	18000	1	1	11	18	16	39	4	11	40	2	2	23	2	15	2	NA
1	2	5	17531.25	39	1	2	2	1	33280	1	1	6	8	22	51	10	6	40	2	2	55	2	14	2	16
1	2	5	11809.34	38	7	1	2	1	20000	1	1	9	14	14	35	3	9	40	2	2	35	2	14	2	10
1	1	5	15249.86	39	1	1	2	1	21000	3	3	11	18	13	32	3	11	40	2	2	56	2	7	2	9.5
1	4	5	14450.68	43	5	2	2	1	35000	1	1	10	15	8	21	2	10	40	1	2	53	2	14	2	16.825
1	1	5	0	44	1	1	0	1	117000	1	1	4	4	1	1	1	4	50	1	2	45	2	15	2	NA
1	1	5	0	40	1	2	0	1	78000	1	1	8	11	1	1	1	8	40	1	2	30	2	15	2	NA
1	3	5	0	43	1	1	0	1	50000	1	1	10	15	8	21	2	10	45	1	2	34	2	8	2	NA
1	1	5	0	40	1	1	0	1	120000	1	1	3	3	1	1	1	3	40	1	2	40	2	9	2	NA
1	1	5	15967.72	36	1	1	2	1	7000	1	1	3	3	19	44	7	3	40	2	2	46	2	9	2	7
1	1	5	15690.71	39	7	1	2	1	14500	1	1	11	18	13	33	3	11	40	1	2	23	2	7	2	3.5
1	1	5	14485.77	40	7	2	2	1	14500	1	1	11	18	13	33	3	11	40	1	2	21	2	7	2	10
1	1	5	12456.64	39	1	2	2	1	46000	1	1	5	7	16	38	4	5	40	1	2	34	2	12	2	14.65
1	1	5	0	40	5	2	0	1	20000	1	1	5	7	16	39	4	5	37	1	2	43	2	11	2	NA
1	1	5	0	39	2	2	0	1	2660	4	12	12	20	15	37	3	12	6	1	1	35	2	13	1	NA
1	1	5	13989.51	41	3	1	2	1	57000	1	1	6	8	22	51	10	6	45	1	2	29	2	12	2	25.333333
1	4	5	14493.26	44	1	2	2	1	72000	1	1	13	21	12	30	3	13	40	1	2	50	2	15	2	10
1	4	5	10264.01	37	7	2	2	1	3000	4	12	6	8	22	51	10	6	30	2	2	43	2	7	1	10
1	1	5	11573.18	40	7	2	2	1	2308	3	3	11	18	14	35	3	11	30	2	2	27	2	7	1	7.5
1	1	5	0	39	5	1	0	1	4320	3	5	3	3	19	43	7	3	40	1	2	35	2	4	2	NA
1	3	5	14351.84	42	1	2	0	1	40000	1	1	10	15	17	40	5	13	40	1	2	53	2	13	2	11.525
1	3	5	14233.66	46	5	2	2	1	493804	1	1	10	15	1	1	1	10	60	1	1	62	2	16	2	48.083333
1	1	5	10724.35	40	1	2	2	1	25000	1	1	9	13	17	40	5	9	40	1	2	41	2	14	2	11.55
1	4	5	13382.3	43	1	1	2	1	48000	1	1	10	16	11	28	3	10	40	1	2	29	2	14	2	21.625
1	1	5	13382.3	43	1	1	2	1	58000	1	1	12	20	1	3	1	12	50	1	2	28	2	15	2	23.08
1	1	5	17923	39	7	2	2	1	11000	2	7	10	16	15	37	3	10	40	2	2	20	2	6	2	10.25
1	1	5	9300.96	40	7	2	2	1	43000	1	1	10	16	11	28	3	10	40	2	2	35	2	11	2	8.125
1	4	5	13504.05	43	1	1	2	1	35000	1	1	10	15	8	21	2	10	50	1	2	36	2	13	2	15.38
1	4	5	13892.91	40	1	2	0	1	25000	1	1	10	15	17	40	5	10	36	1	1	45	2	11	2	16.5
1	4	5	12754.86	44	5	2	0	1	30000	4	11	10	15	8	21	2	10	35	1	2	38	2	11	2	16.457143
1	1	5	0	39	1	1	0	1	38000	1	1	9	14	14	35	3	9	40	1	2	49	2	9	2	NA
1	1	5	0	40	1	1	0	1	62000	1	1	9	14	1	3	1	9	40	1	2	50	2	8	2	NA
1	1	5	13775.94	41	5	2	2	1	13000	1	1	5	7	16	39	4	5	-4	1	2	51	2	1	1	7.25
1	1	5	12836.18	39	5	1	2	1	20000	1	1	4	4	22	51	10	4	40	1	2	40	2	6	2	7.25
1	1	5	0	40	1	2	0	1	40000	1	1	10	16	17	40	5	10	40	1	2	37	2	14	2	NA
1	3	5	17360.16	42	6	1	0	1	38000	1	1	10	15	8	21	2	10	40	2	2	32	2	7	2	18.275
1	4	5	13657.68	40	3	1	2	1	29000	1	1	10	15	14	35	3	10	40	4	2	63	2	12	2	14
1	1	5	15952.06	43	1	2	2	1	36000	1	1	10	16	6	17	2	10	40	2	2	29	2	13	2	17.3
1	1	5	0	43	1	1	0	1	110000	1	1	10	16	3	8	2	10	40	4	2	50	2	15	2	NA
1	1	5	0	39	1	2	0	1	9500	2	7	5	7	16	39	4	9	20	1	2	42	2	11	1	NA
1	1	5	0	42	1	1	0	1	60000	1	1	6	9	20	48	8	4	40	1	2	38	2	11	2	NA
1	1	5	0	43	1	2	0	1	42000	1	1	5	7	16	38	4	5	40	1	2	33	2	12	2	NA
1	1	5	12598.96	39	1	1	2	1	42000	1	1	3	3	19	44	7	3	40	1	2	50	2	12	2	25
1	3	5	15850.1	39	1	2	2	1	36000	1	1	10	16	6	17	2	10	40	2	2	46	2	11	2	18.75
1	1	5	19786.7	39	1	1	2	1	31000	1	1	4	5	21	49	9	5	22	2	2	24	2	7	1	8
1	1	5	15288.06	39	6	2	2	1	20800	1	1	11	17	15	36	3	11	40	1	2	23	2	14	2	10
1	3	5	0	43	1	2	0	1	30002	1	1	10	15	8	21	2	10	40	1	1	43	2	10	2	NA
1	1	5	0	33	1	2	0	1	4152	1	1	9	14	22	51	10	9	40	1	1	40	2	11	2	NA
1	1	5	14376.09	42	7	2	2	1	72000	1	1	10	16	1	3	1	10	40	1	2	46	2	13	2	34.625
1	1	5	12970.48	43	1	2	2	1	50000	1	1	4	5	17	40	5	9	40	4	2	25	2	13	2	14.425
1	1	5	12799.78	43	7	1	2	1	38000	1	1	5	7	16	38	4	5	40	1	2	50	2	14	2	11.5
1	1	5	12314.66	37	5	2	2	1	14216	2	7	11	18	17	40	5	11	30	1	2	59	2	5	1	8.47
1	2	5	14616.12	42	1	2	2	1	8000	3	4	11	17	15	37	1	13	39	1	1	36	2	14	2	15.5
1	1	5	0	39	6	2	0	0	0	5	13	15	23	24	53	2	10	40	1	2	40	2	7	2	NA
1	1	5	0	39	1	1	0	1	45000	3	2	6	8	20	48	8	6	40	2	1	34	2	7	2	NA
1	1	5	0	45	7	1	0	1	19500	3	4	9	14	17	40	5	9	40	2	2	50	2	12	2	NA
1	1	5	9277.78	44	1	2	2	1	72000	1	1	7	10	9	23	2	7	40	1	1	43	2	16	2	34.625
1	1	5	12736.98	43	7	2	2	1	33000	1	1	8	12	17	40	5	8	40	1	2	35	2	9	2	15.875
1	1	5	12427.19	40	1	1	2	1	85000	1	1	11	17	22	51	10	11	40	1	2	62	2	15	2	40.875
1	1	5	13321.17	40	7	2	2	1	38000	1	1	5	7	16	39	4	8	40	1	1	31	2	15	2	13.98
1	4	5	13015.45	43	5	2	2	1	75000	1	1	10	16	10	25	2	10	40	2	2	35	2	15	2	38.9
1	1	5	12314.66	42	2	2	2	1	18000	2	7	10	16	10	25	2	10	16	1	2	55	2	14	1	40
1	1	5	0	43	1	2	0	1	30000	1	1	5	6	16	38	4	5	40	1	1	37	2	13	2	NA
1	1	5	0	36	5	2	0	1	27000	1	1	5	7	17	40	5	5	32	2	1	39	2	5	1	NA
1	1	5	0	39	1	2	0	1	25000	1	1	10	16	11	28	3	10	40	1	1	52	2	15	2	NA
1	1	5	0	41	1	2	0	1	15000	3	4	10	16	8	22	2	10	40	1	1	52	2	6	2	NA
1	1	5	0	40	7	1	0	1	25000	1	1	8	11	17	40	5	6	-4	2	2	21	2	5	1	NA
1	1	5	12964.48	39	1	1	0	1	27000	1	1	4	4	19	46	7	4	40	1	2	58	2	11	2	15
1	1	5	11923.89	40	1	1	2	1	30000	1	1	12	20	6	17	2	12	75	2	2	39	2	4	2	NA
1	1	5	0	40	1	2	0	1	22500	1	1	12	20	17	40	5	12	40	1	1	45	2	5	2	NA
1	1	5	0	39	6	2	0	1	6000	4	10	9	14	14	35	3	9	-4	1	1	55	2	2	1	NA
1	1	5	0	42	5	2	0	1	39000	2	7	12	20	11	28	3	12	32	1	1	41	2	8	1	NA
1	1	5	0	43	1	2	0	1	54000	1	1	10	15	5	15	2	10	-4	1	1	28	2	12	1	NA
1	1	5	0	41	5	1	0	1	13000	3	3	3	3	19	45	3	10	40	1	2	36	2	7	2	NA
1	1	5	0	38	1	1	0	1	25000	1	1	11	18	13	32	3	11	35	1	2	41	2	4	2	NA
1	1	5	0	41	1	2	0	1	10000	4	11	11	18	13	33	3	11	24	1	2	31	2	4	1	NA
1	1	5	12351.92	33	1	1	2	1	34000	1	1	12	20	6	17	2	12	60	1	2	60	2	12	2	10.9
1	1	5	13345.84	43	1	1	2	1	37000	3	5	7	10	3	8	2	4	40	1	2	34	2	15	2	25
1	1	5	13343.61	39	7	1	2	1	18720	1	1	12	20	20	48	8	12	40	1	2	38	2	13	2	10
1	1	5	12708.76	42	1	2	2	1	75000	1	1	5	7	17	40	5	5	50	1	2	63	2	12	2	28.84
1	1	5	13427.28	41	1	2	2	1	28000	1	1	12	20	15	37	3	12	35	1	1	27	2	16	2	15.371429
1	1	5	14703.39	43	1	2	2	1	125000	1	1	8	11	2	7	1	8	40	1	2	62	2	14	2	60.1
1	1	5	13786.41	43	5	2	2	1	44000	1	1	10	15	8	21	2	10	40	1	2	54	2	12	2	21.15
1	4	5	0	43	7	2	0	1	43000	1	1	10	15	8	21	2	10	40	2	2	28	2	12	2	NA
1	1	5	12552.21	39	1	1	2	1	29000	1	1	7	10	2	6	1	7	40	1	1	48	2	13	2	12
1	1	5	11851.66	42	1	1	2	1	92000	1	1	9	13	3	8	2	9	40	2	2	37	2	16	2	32.5
1	1	5	12862.87	43	1	1	2	1	90000	1	1	4	4	1	3	1	4	40	1	2	59	2	15	2	43.275
1	4	5	0	40	5	2	0	1	30000	1	1	13	21	17	40	5	13	40	1	2	34	2	14	2	NA
1	4	5	13333.99	45	7	1	0	1	21000	1	1	10	15	22	51	10	10	40	1	2	39	2	7	2	13.5
1	1	5	24087	44	1	1	2	1	39000	1	1	9	14	2	5	1	9	40	1	2	33	2	14	2	18.75
1	1	5	26432.86	38	1	1	2	1	80000	3	5	6	8	19	44	7	3	40	2	2	63	2	13	2	14
1	1	5	15288.06	39	7	2	2	1	25000	1	1	5	7	16	39	4	5	40	1	2	22	2	13	2	13
1	1	5	0	39	1	1	0	1	18202	1	1	5	7	16	39	10	5	39	1	2	23	2	7	2	NA
1	1	5	0	39	1	1	0	1	36000	1	1	5	6	16	39	4	5	40	6	2	54	2	13	2	NA
1	1	5	0	39	7	1	0	1	6500	4	9	11	17	17	40	5	11	20	2	2	18	2	9	1	NA
1	1	5	0	39	1	2	0	1	25000	1	1	10	16	17	40	5	10	40	2	2	33	2	10	2	NA
1	2	5	0	44	1	1	0	1	55000	1	1	13	21	1	2	1	13	40	2	2	52	2	14	2	NA
1	1	5	14263.41	39	1	2	2	1	35000	1	1	10	16	1	3	1	10	45	1	2	48	2	13	2	21.355556
1	1	5	14453.41	42	7	1	2	1	13000	1	1	11	18	13	32	3	11	40	1	2	27	2	6	2	8.5
1	4	5	17815.37	44	5	2	2	1	63600	1	1	10	15	8	21	2	10	40	2	2	58	2	13	2	41
1	2	5	12862.87	39	1	1	0	1	53000	1	1	6	8	17	40	5	6	80	1	2	57	2	14	2	8.9375
1	1	5	0	44	1	1	0	1	70000	1	1	8	11	21	49	9	8	40	4	2	37	2	13	2	NA
1	4	5	18022.46	42	7	1	0	1	50000	1	1	13	21	12	30	3	13	61	1	1	30	2	12	2	20
1	1	5	11840.87	40	1	2	2	1	12500	1	1	10	16	17	40	5	10	40	1	2	43	2	11	2	12
1	1	5	14098.96	43	1	2	2	1	42000	3	3	10	16	6	17	2	12	35	2	2	42	2	9	2	28.342857
1	1	5	15461.74	40	1	1	2	1	18000	1	1	5	6	21	49	9	5	30	1	1	42	2	11	1	11
1	3	5	12422.3	46	6	1	2	1	72000	3	3	10	15	8	20	2	10	50	1	2	49	2	14	2	37.9
1	1	5	0	43	6	2	0	1	35000	1	1	10	16	2	6	1	10	40	1	1	37	2	9	2	NA
1	4	5	0	43	1	2	0	1	38000	1	1	10	15	8	21	2	10	48	1	2	48	2	14	2	NA
1	1	5	0	42	7	2	0	0	0	5	13	15	23	24	53	4	5	20	1	1	28	2	2	1	NA
1	1	5	11819.93	39	7	1	2	1	25000	1	1	9	13	10	27	2	9	40	2	2	38	2	11	2	11
1	2	5	18058.98	36	7	1	2	1	38000	1	1	10	16	20	48	8	10	40	2	2	47	2	10	2	18.275
1	1	5	12872.64	39	1	1	2	1	49000	1	1	3	3	1	2	1	3	40	1	2	56	2	15	2	24.025
1	1	5	0	42	1	1	0	1	33000	1	1	9	13	4	12	2	9	40	1	1	56	2	8	2	NA
1	1	5	0	43	1	1	0	1	36000	1	1	10	16	10	25	2	10	40	4	2	31	2	15	2	NA
1	1	5	0	39	7	2	0	1	35000	1	1	5	7	16	38	4	5	-4	2	2	42	2	11	1	NA
1	1	6	8067.82	42	1	1	2	1	32000	1	1	5	7	20	48	8	5	40	1	2	56	2	9	2	17.25
1	1	6	7812.4	39	1	1	2	1	40000	1	1	6	8	22	51	10	6	-4	1	2	49	2	13	1	NA
1	1	6	6762.65	39	1	2	2	1	31000	1	1	4	4	17	40	5	4	40	1	2	37	2	12	2	10
1	1	6	7890.37	40	1	1	2	1	66000	1	1	5	7	16	38	4	5	40	1	2	57	2	12	2	18.5
1	1	6	7979.62	41	1	1	2	1	103500	1	1	3	3	1	2	1	3	60	1	2	52	2	11	2	17
1	1	6	8900.4	42	1	2	2	1	25000	1	1	4	5	21	49	9	4	40	1	2	52	2	14	2	13.65
1	1	6	6249.83	41	7	2	2	1	27000	1	1	4	5	17	40	5	4	41	1	2	42	2	6	2	11
1	1	6	7035.25	41	1	2	2	1	50000	1	1	4	4	20	48	8	4	40	1	2	33	2	10	2	17
1	3	6	0	43	1	2	0	1	21842	1	1	10	15	8	22	2	10	35	1	2	44	2	11	2	NA
1	1	6	0	37	1	1	0	1	26000	1	1	4	4	22	51	10	4	40	1	2	32	2	9	2	NA
1	1	6	7679.11	40	7	1	2	1	30000	2	7	11	17	15	37	3	11	30	1	2	27	2	9	1	5
1	1	6	0	39	1	1	0	1	21600	1	1	5	6	22	51	10	5	40	1	2	52	2	12	2	NA
1	1	6	7963.48	44	1	1	2	1	175000	1	1	8	11	1	1	1	8	55	1	2	56	2	15	2	52.454545
1	1	6	7812.4	39	5	1	0	1	42000	1	1	4	5	22	51	10	4	40	1	2	45	2	8	2	20
1	1	6	0	39	1	1	0	1	30000	3	2	3	3	19	46	7	3	40	1	2	32	2	11	2	NA
1	1	6	0	39	1	2	0	1	30000	1	1	10	16	11	28	3	10	40	1	2	29	2	11	2	NA
1	1	6	8439.19	35	1	1	2	1	14560	1	1	11	18	13	32	3	11	-4	1	1	48	2	6	1	7
1	1	6	0	40	7	2	0	1	41000	1	1	5	7	16	38	4	5	40	1	2	39	2	11	2	NA
1	1	6	0	42	1	2	0	1	56000	1	1	10	16	10	25	2	10	36	1	2	31	2	14	2	NA
1	1	6	6906.85	42	5	1	2	1	55000	1	1	9	13	20	48	8	9	-4	1	2	40	2	12	1	NA
1	1	6	8785.3	39	1	2	2	1	40000	1	1	8	11	16	39	4	8	40	1	2	54	2	12	2	19.225
1	1	6	7963.48	39	1	1	2	1	77753	1	1	4	4	17	40	5	5	48	1	2	57	2	10	2	26.041667
1	1	6	6907.45	40	7	2	2	1	21000	1	1	12	20	15	37	3	12	-4	1	2	27	2	12	1	NA
1	1	6	7679.11	40	7	1	2	1	30000	1	1	4	4	1	3	1	4	40	1	2	27	2	12	2	16
1	3	6	7876.43	44	1	1	2	1	38500	1	1	10	15	6	17	2	10	37	1	2	53	2	16	2	20.783784
1	1	6	7175.92	40	1	1	2	1	20000	4	9	3	3	19	43	7	3	-4	1	2	34	2	11	1	NA
1	1	6	0	40	1	1	0	1	40000	1	1	5	6	17	40	5	5	40	1	2	39	2	14	2	NA
1	4	6	0	39	1	1	0	1	98800	1	1	13	21	12	30	3	13	-4	1	2	38	2	14	1	NA
1	1	6	6907.45	43	1	2	2	1	90000	1	1	5	6	16	39	4	5	50	1	2	29	2	16	2	18.46
1	1	6	0	40	1	2	0	1	32000	1	1	8	11	16	39	4	8	40	1	2	44	2	9	2	NA
1	1	6	16199.65	39	1	1	2	1	40002	1	1	4	4	21	49	9	4	40	1	2	43	2	14	2	19.35
1	1	6	11990.77	40	1	1	2	1	35000	1	1	4	4	21	49	9	4	40	1	2	54	2	10	2	19.35
1	1	6	13809.49	44	7	2	2	1	80000	1	1	8	11	3	8	2	8	40	1	2	51	2	12	2	16.825
1	4	6	0	39	1	2	0	1	23000	1	1	13	21	17	40	5	13	40	1	2	45	2	15	2	NA
1	1	6	12124.15	39	1	2	2	1	10000	4	10	10	16	11	28	3	10	36	1	2	55	1	13	2	12
1	1	6	9959.62	39	1	2	2	1	34000	1	1	10	15	17	40	5	9	40	1	2	42	2	14	2	16.95
1	1	6	0	41	7	2	0	1	144000	1	1	9	13	17	40	5	9	40	1	2	38	2	15	2	NA
1	1	6	0	39	1	2	0	1	9860	4	9	11	18	17	40	5	11	40	1	2	35	2	6	2	NA
1	1	6	10816.54	39	7	2	2	1	34000	1	1	10	16	11	28	3	10	40	1	2	25	2	10	2	16.35
1	1	6	12114.34	43	5	2	2	1	29640	1	1	5	7	16	39	4	5	38	1	2	55	2	15	2	15
1	4	6	12772.68	39	1	1	0	1	50000	1	1	13	21	19	43	7	13	40	1	2	58	2	15	2	21.625
1	1	6	0	40	5	2	0	1	28000	1	1	10	16	10	27	2	10	40	2	2	38	2	11	2	NA
1	1	6	17077.61	43	1	1	2	1	160002	1	1	7	10	3	8	2	7	-4	1	2	42	2	15	1	NA
1	1	6	12693.22	40	5	1	2	1	11000	4	10	12	20	20	48	8	12	40	1	2	53	2	4	2	17.3
1	1	6	14116.89	43	5	2	2	1	100000	1	1	8	12	16	39	4	8	50	1	2	48	2	14	2	38.46
1	4	6	0	39	5	2	0	1	28000	1	1	10	15	17	40	5	10	40	2	2	51	2	8	2	NA
1	1	6	12772.68	39	1	1	2	1	30000	1	1	5	7	16	38	4	5	40	1	2	59	2	12	2	14.425
1	1	6	0	43	1	1	0	1	50000	1	1	6	8	22	50	10	6	-4	1	2	54	2	16	1	NA
1	1	6	0	41	1	2	0	1	9600	3	4	6	8	17	40	10	5	-4	1	2	39	2	7	1	NA
1	1	6	17729.3	41	7	1	2	1	1400	4	12	11	18	13	33	3	11	28	1	2	35	2	7	1	2.15
1	1	6	10452.84	43	1	2	2	1	53000	1	1	10	16	8	21	2	10	70	1	2	37	2	13	2	15.714286
1	1	6	0	39	1	1	0	1	52000	1	1	4	4	19	46	7	4	40	1	2	45	2	13	2	NA
1	1	6	10294.94	39	7	1	2	1	21000	1	1	4	4	21	49	9	4	40	1	2	42	2	5	2	9.7
1	1	6	9785.75	40	1	2	2	1	5000	4	10	10	16	1	3	1	10	30	1	2	37	2	9	1	7.5
1	1	6	10917.87	39	5	1	2	1	21268	1	1	3	3	19	44	7	3	40	1	2	28	2	7	2	10.24
1	1	6	16704.45	40	7	1	2	0	0	5	13	15	23	24	53	3	11	-4	2	2	22	2	7	1	7.25
1	3	6	8739.41	40	1	2	2	1	29952	1	1	10	16	17	40	5	10	40	1	2	43	2	13	2	14.4
1	1	6	11629.34	43	7	2	2	1	20400	3	4	10	16	10	25	2	10	36	1	2	23	2	14	2	24.1
1	1	6	10200.74	39	1	1	2	1	95000	1	1	6	8	1	2	1	6	40	1	2	35	2	14	2	45.675
1	1	6	11324.26	39	1	1	2	1	13000	1	1	3	3	19	46	10	5	40	1	2	26	2	10	2	9.25
1	1	6	9162.18	39	1	2	2	1	3000	4	10	12	20	15	37	3	12	-4	1	2	40	2	12	1	10.7
1	1	6	9380	39	1	2	2	1	36000	1	1	8	12	1	1	1	8	40	1	2	43	2	15	2	17.3
1	1	6	14422.67	39	1	1	2	1	20000	1	1	5	7	22	51	10	5	40	4	2	56	2	11	2	23
1	1	6	12376.26	38	5	1	2	1	23400	1	1	11	18	13	33	3	11	40	4	2	30	2	11	2	8
1	1	6	14127.49	39	1	2	2	1	14252	2	7	9	14	2	5	1	9	-4	1	2	50	2	14	1	NA
1	1	6	13061.91	42	7	1	2	1	24960	1	1	5	7	21	49	9	5	40	1	2	22	2	14	2	12
1	1	6	12066.61	39	1	1	2	1	30000	3	2	3	3	19	43	7	3	40	1	1	39	2	10	2	15
1	1	6	0	43	1	1	0	1	20000	1	1	9	13	1	1	1	9	60	1	2	30	2	16	2	NA
1	1	6	0	43	1	2	0	1	35000	1	1	4	5	5	13	2	4	45	1	2	29	2	16	2	NA
1	4	6	0	43	5	2	0	1	41000	1	1	13	21	12	30	3	13	40	1	2	34	2	9	2	NA
1	4	6	0	39	1	2	0	1	70000	1	1	13	21	12	30	3	13	15	2	2	48	2	8	1	NA
1	2	6	13823.22	43	1	1	2	1	85000	1	1	3	3	2	7	1	3	40	2	2	41	2	15	2	43
1	1	6	9805.97	43	5	1	2	1	52000	1	1	6	9	1	1	1	6	49	2	2	38	2	13	2	21.591837
1	3	6	11516.35	43	1	2	0	1	44000	1	1	10	15	8	21	2	10	40	2	2	55	2	13	2	21.15
1	4	6	8739.41	39	1	2	0	1	6000	4	10	10	15	8	21	2	10	40	1	2	43	2	15	2	23.075
1	1	6	17828.46	40	7	1	2	1	10000	2	7	11	18	13	32	3	11	30	2	2	19	2	12	1	8.25
1	1	6	15023.69	36	1	1	2	1	16600	3	3	4	4	21	49	9	12	40	2	2	53	2	6	2	21
1	1	6	0	39	7	2	0	1	35000	1	1	11	18	13	32	3	11	50	2	2	34	2	10	2	NA
1	1	6	12022.04	40	1	1	2	1	56000	1	1	3	3	22	51	10	3	60	1	2	35	2	11	2	11
1	1	6	0	39	7	1	0	1	25000	1	1	4	5	21	49	9	4	40	2	2	24	2	8	2	NA
1	1	6	0	39	1	1	0	1	80000	1	1	6	9	20	48	8	6	40	1	2	41	2	15	2	NA
1	1	6	0	41	7	1	0	1	45000	1	1	12	20	20	48	8	12	40	1	2	22	2	16	2	NA
1	1	6	9306.01	39	1	2	2	1	35360	1	1	10	16	17	40	5	10	40	2	2	40	2	9	2	17
1	1	6	0	39	1	2	0	1	12000	1	1	10	16	17	40	5	10	40	1	2	33	2	14	2	NA
1	1	6	11333.26	40	1	1	2	1	19800	1	1	8	12	14	35	3	8	40	2	2	54	2	12	2	11
1	1	6	0	39	7	1	0	1	4500	4	9	10	16	22	51	10	10	25	2	2	50	2	13	1	NA
1	1	6	13340.92	40	1	2	2	1	3500	4	8	11	18	13	33	3	11	16	1	2	20	2	13	1	7.5
1	1	6	10830.21	39	7	2	2	1	19000	1	1	9	14	14	35	3	9	40	2	2	50	2	8	2	6
1	3	6	6716.91	44	1	2	2	1	55000	1	1	10	16	1	3	1	10	40	1	2	55	2	15	2	26.45
1	1	6	12816.97	43	7	1	2	1	32000	1	1	11	18	1	1	1	11	40	1	2	28	2	13	2	15.375
1	1	6	7778.81	40	1	1	2	1	70000	1	1	4	5	9	23	2	4	40	1	2	48	2	15	2	19.75
1	1	6	8140.06	39	1	2	2	1	20904	1	1	10	15	8	22	2	10	40	1	2	46	2	15	2	10.05
1	2	6	8022.84	43	1	1	0	1	90000	1	1	13	21	2	6	1	13	40	1	2	55	2	16	2	30
1	1	6	7851.79	42	1	2	2	1	30000	1	1	3	3	17	40	5	4	40	1	2	52	2	14	2	12.025
1	1	6	0	39	1	2	0	1	30000	1	1	8	11	17	40	5	8	40	1	2	31	2	14	2	NA
1	1	7	0	41	1	2	0	1	60000	1	1	10	16	1	3	1	10	40	1	2	28	2	15	2	NA
1	3	7	0	43	1	2	0	1	36566	1	1	10	15	8	21	2	10	40	1	2	37	2	7	2	NA
1	1	7	9005.82	41	1	1	2	1	16400	1	1	7	10	20	48	8	7	40	1	2	49	2	9	2	23.5
1	1	7	10545.27	36	1	2	2	1	12000	2	7	10	16	11	28	3	10	32	2	2	45	2	1	1	10.76
1	4	7	8393.73	38	1	1	2	1	25000	1	1	3	3	19	46	7	3	40	1	2	34	2	2	2	14.5
1	1	7	9300.26	39	5	1	2	1	1200	4	12	5	7	16	39	2	10	72	1	2	59	2	7	2	7.208333
1	1	7	0	39	1	1	0	1	40000	1	1	6	8	22	51	10	6	-4	1	2	28	2	9	1	NA
1	1	7	8402.39	39	5	2	2	1	12000	3	5	6	8	13	32	3	6	84	1	2	56	2	4	2	13.5
1	1	7	0	44	7	1	0	1	70002	1	1	5	7	2	5	1	5	40	2	2	30	2	11	2	NA
1	1	7	0	39	4	2	0	1	26000	1	1	9	14	17	40	5	9	45	1	2	36	2	13	2	NA
1	1	7	0	39	7	1	0	1	19000	1	1	12	20	14	35	3	12	30	2	2	20	2	12	1	NA
1	1	7	0	39	7	2	0	1	26100	1	1	9	14	17	40	5	9	40	1	2	24	2	13	2	NA
1	1	7	0	40	7	2	0	1	8500	4	9	9	14	14	35	8	12	36	1	2	19	2	12	2	NA
1	3	7	0	40	1	1	0	1	33000	3	2	3	3	4	12	2	3	40	2	2	47	2	13	2	NA
1	1	7	9871.45	40	2	2	2	1	12000	4	8	11	18	13	33	3	11	25	1	2	30	2	7	1	2.35
1	1	7	17881.15	39	1	1	2	1	23000	1	1	5	7	16	38	4	5	40	2	2	42	2	12	2	13.08
1	1	7	13950.69	39	1	1	0	1	93000	1	1	4	5	21	49	9	4	40	1	2	42	2	15	2	32
1	1	7	13273.47	43	5	1	2	1	105000	1	1	4	5	16	39	4	4	40	1	2	54	2	14	2	24.225
1	1	7	13018.54	39	1	2	2	1	35000	1	1	10	16	11	28	3	10	40	1	2	60	2	14	2	16
1	1	7	0	39	7	2	0	1	17000	1	1	10	16	11	28	3	10	36	1	2	26	2	6	2	NA
1	4	7	13820.69	39	1	2	2	1	11000	2	7	6	8	15	37	3	6	30	2	2	61	2	8	1	11.666667
1	1	7	15648.93	43	7	2	2	1	45000	1	1	8	11	17	40	5	8	40	2	2	27	2	8	2	13.75
1	1	7	0	34	1	1	0	1	28000	1	1	3	3	22	51	10	3	40	2	2	60	2	11	2	NA
1	1	7	20562.37	39	7	2	2	1	2000	4	11	11	18	13	33	3	5	-4	2	2	23	2	7	1	8
1	1	7	15405.57	43	7	1	2	0	0	5	13	15	23	24	53	3	11	-4	1	2	27	2	16	1	12.5
1	1	7	15788.55	39	7	1	2	1	43000	1	1	3	3	19	46	7	3	50	1	2	27	2	12	2	18
1	1	7	0	39	7	2	0	1	21000	1	1	12	20	15	37	3	12	45	4	2	29	2	11	2	NA
1	3	7	15788.55	43	1	1	2	1	29000	1	1	10	15	8	20	2	10	60	1	2	25	2	11	2	25
1	1	7	14746.88	40	6	2	2	1	31200	1	1	4	5	17	40	5	4	40	1	2	41	2	13	2	16.25
1	1	7	0	40	7	2	0	1	20000	2	7	6	8	22	50	10	6	27	2	2	40	2	11	1	NA
1	1	7	0	39	1	1	0	1	100000	1	1	5	7	16	39	4	5	65	2	2	33	2	10	2	NA
1	4	7	7404.6	39	4	2	2	1	25200	1	1	13	21	1	1	1	13	40	1	2	57	2	11	2	12.125
1	1	7	0	42	6	2	0	1	38000	1	1	5	7	16	38	4	5	50	3	2	31	2	9	2	NA
1	1	7	11829.08	42	1	2	2	1	25000	1	1	10	16	10	24	2	10	40	1	2	29	2	15	2	12
1	1	7	10622.43	39	1	1	2	1	45000	1	1	3	3	19	43	7	3	40	1	2	27	2	6	2	15
1	3	7	0	43	1	2	0	1	40000	1	1	11	17	15	36	3	11	40	6	2	40	2	11	2	NA
1	1	7	7404.6	39	1	2	2	1	39000	1	1	6	8	17	40	5	6	43	1	2	55	2	11	2	28.75
1	1	7	7429.28	39	1	2	2	1	17000	1	1	8	11	2	5	1	8	50	6	2	53	2	11	2	15.38
1	3	7	11773.36	43	1	2	2	1	42000	1	1	13	21	6	17	2	13	40	3	1	50	2	15	2	19.3
1	1	7	0	37	1	2	0	1	18000	1	1	10	16	15	37	3	10	36	1	2	33	2	8	2	NA
1	1	7	0	39	1	2	0	1	24000	1	1	10	16	1	2	1	10	50	1	2	56	2	14	2	NA
1	1	7	0	43	1	2	0	1	20000	1	1	11	18	13	33	3	11	40	4	2	27	2	13	2	NA
1	1	7	8475.75	40	5	1	2	1	25000	1	1	11	17	14	35	3	11	-4	1	2	54	2	6	1	9
1	1	7	0	42	1	2	0	1	35000	1	1	10	16	10	25	2	10	20	1	2	56	2	14	1	NA
1	1	7	7192.17	39	1	2	2	1	28500	1	1	10	16	17	40	5	10	40	1	2	56	2	12	2	13.7
1	1	7	13432.55	40	7	2	2	1	10000	2	7	10	16	15	37	3	10	40	1	2	21	2	12	2	4.8
1	1	7	9592.95	40	1	1	2	1	40000	1	1	8	12	17	40	5	8	50	1	2	44	2	14	2	15.38
1	1	7	11877.08	39	1	2	2	1	28000	1	1	11	18	1	3	1	11	40	1	2	27	2	6	2	13.45
1	1	7	0	40	1	1	0	1	37000	1	1	7	10	20	48	8	7	40	1	2	28	2	13	2	NA
1	1	7	8662.79	41	1	2	2	1	17000	2	7	10	16	17	40	5	10	24	1	2	60	2	9	1	9.666667
1	1	7	15379.29	43	1	1	2	1	76000	1	1	6	8	4	12	2	6	40	6	2	32	2	15	2	36.55
1	1	7	9280.07	45	1	1	2	1	175000	1	1	2	2	1	1	1	3	40	1	2	40	2	15	2	62.5
1	1	7	13204.09	39	7	2	2	1	12000	2	7	11	18	1	3	1	11	40	1	2	19	2	5	2	8.5
1	1	7	12228.1	39	1	2	2	1	30000	1	1	8	11	1	1	1	8	40	1	1	27	2	13	2	12.25
1	1	7	0	40	1	2	0	1	5000	1	1	10	16	11	28	3	10	10	1	1	29	2	10	1	NA
1	1	7	0	42	7	2	0	1	25000	1	1	9	14	17	40	5	9	40	6	2	25	2	11	2	NA
1	1	7	0	42	1	2	0	1	22000	1	1	8	11	17	40	5	8	40	1	2	21	2	6	2	NA
1	4	7	0	43	5	2	0	1	58000	1	1	7	10	9	23	2	7	40	1	2	45	2	12	2	NA
1	1	7	11489.71	39	5	2	2	1	23920	1	1	5	6	17	40	5	5	40	1	2	27	2	11	2	11.5
1	1	7	0	41	1	2	0	1	12000	1	1	12	20	15	37	3	12	40	1	1	45	2	8	2	NA
1	2	7	12375.57	42	1	2	2	1	49000	1	1	6	8	1	3	1	6	41	1	2	63	2	12	2	24
1	1	7	13840.21	35	1	2	2	1	9048	1	1	12	20	21	49	9	12	44	1	2	43	2	11	2	7.25
1	3	7	0	43	1	2	0	1	17500	1	1	10	15	17	40	5	13	40	1	2	54	2	15	2	NA
1	1	7	12587.31	44	5	1	2	1	120000	1	1	12	20	16	39	4	12	50	1	2	61	2	15	2	36.92
1	1	7	14052.81	40	1	2	2	1	18000	2	7	12	20	11	28	3	12	25	1	2	41	2	16	1	13.84
1	2	7	13731.01	43	1	1	2	1	66500	1	1	13	21	9	23	2	13	40	1	2	59	2	14	2	31.975
1	1	7	16356.39	39	1	2	2	1	41000	1	1	5	7	17	40	5	5	40	1	2	33	2	12	2	18
1	1	7	14230.04	44	1	2	2	1	58000	1	1	10	15	8	22	2	10	40	1	2	54	2	15	2	28.85
1	1	7	13584.38	41	1	2	2	1	37000	1	1	5	7	17	40	5	5	40	1	2	53	2	10	2	23.075
1	1	7	12657.13	43	1	1	2	1	65000	1	1	3	3	1	2	1	3	40	1	2	48	2	14	2	57.675
1	1	7	14712.71	46	1	1	2	1	111000	1	1	8	11	7	18	2	8	45	1	2	35	2	15	2	44.866667
1	1	7	14997.03	44	5	1	2	1	67000	1	1	4	4	2	6	2	4	40	4	2	38	2	13	2	28.85
1	1	7	12429.74	44	1	1	2	1	90000	1	1	4	5	1	3	1	4	40	4	2	48	2	15	2	43.275
1	1	7	0	39	6	2	0	1	31200	1	1	9	13	17	40	5	9	40	1	1	42	2	15	2	NA
1	1	7	0	43	1	2	0	1	10000	2	7	5	7	16	39	4	5	15	1	2	40	2	16	1	NA
1	1	7	0	40	1	2	0	1	32995	1	1	10	16	17	40	5	10	40	1	2	30	2	11	2	NA
1	1	7	19303.86	40	7	1	2	1	20000	1	1	11	18	13	33	3	11	20	2	2	25	2	6	1	19.25
1	2	7	12095.51	43	1	2	2	1	95000	1	1	13	21	10	25	2	13	40	1	2	58	2	14	2	37.5
1	1	7	15747.94	41	7	1	2	1	22400	3	3	11	18	3	8	5	8	42	1	2	27	2	12	2	11
1	1	7	16025.39	39	1	2	2	1	1350	4	12	11	18	16	39	4	11	30	1	1	29	2	5	1	7.5
1	1	7	16024.47	39	1	2	2	1	10000	1	1	9	14	22	51	10	9	40	1	1	28	2	14	2	8
1	1	7	0	36	5	2	0	1	21000	3	3	9	14	14	35	3	9	32	1	1	33	2	7	1	NA
1	1	7	0	34	1	1	0	1	30000	1	1	4	4	19	43	7	4	40	1	1	27	2	9	2	NA
1	1	7	0	39	1	1	0	0	0	5	13	15	23	24	53	7	3	40	1	1	49	2	6	2	NA
1	2	7	0	45	1	1	0	1	80000	1	1	8	12	1	3	1	8	40	2	2	51	2	15	2	NA
1	1	7	0	43	7	2	0	1	60000	1	1	4	4	2	5	1	5	60	2	2	40	2	14	2	NA
1	1	7	17662.98	40	7	1	0	1	25000	1	1	5	7	21	49	9	5	50	1	2	24	2	12	2	12.5
1	2	7	15565.36	43	7	2	2	1	50000	1	1	4	4	3	8	2	13	40	1	2	29	2	12	2	28
1	1	7	14712.71	43	1	1	2	1	125000	1	1	5	6	16	39	4	5	50	1	2	37	2	15	2	48.08
1	1	7	0	43	7	1	0	1	75000	1	1	9	14	14	34	3	9	70	1	1	34	2	12	2	NA
1	1	7	12095.51	39	1	2	2	1	50000	2	7	10	16	17	40	5	10	32	1	2	58	2	15	1	17
1	1	7	14094.96	43	1	2	2	1	24000	3	5	10	16	17	40	5	9	40	1	2	46	2	13	2	15.625
1	1	7	15647.43	43	1	2	2	1	29120	2	7	9	13	10	25	2	10	25	1	2	32	2	16	1	18
1	1	7	12412.62	39	4	1	2	1	34000	1	1	4	5	21	49	9	4	40	2	2	55	2	13	2	17
1	1	7	11024.09	39	1	1	2	1	30000	1	1	4	5	21	49	9	4	40	2	2	60	2	14	2	13.75
1	1	7	12130.96	39	5	1	2	1	48000	1	1	3	3	19	43	7	3	40	1	1	51	2	7	2	25
1	1	7	18197.38	39	7	2	2	1	27040	1	1	4	5	17	40	5	4	40	1	2	21	2	14	2	13
1	1	7	0	43	1	1	0	1	48000	1	1	7	10	17	40	5	7	40	1	1	34	2	12	2	NA
1	1	7	0	37	7	2	0	1	13200	1	1	5	7	16	39	4	5	40	1	1	18	2	13	2	NA
1	1	7	0	38	1	1	0	1	12000	1	1	3	3	19	44	7	3	50	1	1	26	2	12	2	NA
1	1	7	0	40	7	2	0	1	59000	1	1	4	4	21	49	9	4	40	2	2	53	2	12	2	NA
1	1	7	0	44	1	2	0	1	30000	1	1	10	15	8	21	2	10	40	1	2	50	2	15	2	NA
1	1	7	0	44	1	1	0	1	96000	1	1	3	3	2	5	1	5	40	1	2	52	2	15	2	NA
1	1	7	17670.08	39	1	2	2	1	22002	1	1	8	11	17	40	5	10	40	1	1	39	2	15	2	16.75
1	1	7	13158.22	39	1	1	2	1	30000	1	1	6	8	22	51	10	6	40	1	1	49	2	15	2	20
1	1	7	0	43	1	1	0	1	51000	1	1	10	15	8	21	2	10	50	2	2	47	2	15	2	NA
1	1	7	13545.95	39	7	1	2	1	34000	1	1	12	20	20	48	8	12	40	1	2	46	2	7	2	17
1	2	7	15529.45	44	1	2	2	1	75000	1	1	3	3	1	2	1	3	40	1	2	61	2	15	2	29.76
1	1	7	0	44	1	2	0	1	48000	1	1	10	16	10	25	2	10	28	1	2	34	2	12	1	NA
1	1	7	16136.44	34	1	1	2	1	50000	1	1	3	3	19	43	7	3	40	1	1	24	2	7	2	24.05
1	1	7	14499.1	37	1	2	2	1	12000	1	1	10	16	14	35	3	10	40	1	1	45	2	9	2	9
1	1	7	15514.76	39	4	2	2	1	41568	1	1	10	16	2	6	1	10	40	1	2	61	2	11	2	21.65
1	1	7	16121.18	39	7	1	2	1	9600	2	7	11	18	13	32	3	11	-4	1	1	22	2	1	1	7.65
1	1	7	15097.41	41	5	1	2	1	60000	1	1	3	3	20	48	8	3	40	1	2	41	2	12	2	30
1	1	7	0	33	3	1	0	1	55000	1	1	12	20	22	51	10	12	40	1	1	61	2	7	2	NA
1	1	7	0	39	1	1	0	1	26000	1	1	3	3	19	43	7	3	40	1	1	36	2	8	2	NA
1	1	7	0	40	1	1	0	1	22000	3	4	5	7	17	40	5	5	40	1	1	25	2	13	2	NA
1	1	7	0	39	7	1	0	1	25000	1	1	10	15	13	33	3	11	35	2	2	31	2	7	2	NA
1	4	7	0	43	1	2	0	1	22000	1	1	10	15	8	22	2	10	40	1	2	29	2	12	2	NA
1	1	7	0	40	5	1	0	1	38000	1	1	12	20	16	38	4	12	53	4	2	25	2	11	2	NA
1	4	7	0	44	7	2	0	1	45000	1	1	10	15	8	21	2	10	40	2	2	31	2	15	2	NA
1	2	7	12816.69	44	2	2	2	1	32000	1	1	8	12	17	40	4	5	32	2	2	31	2	9	1	8.15
1	1	7	15764.99	40	1	1	2	1	93600	1	1	9	14	1	1	1	9	40	1	1	35	2	14	2	45
1	1	7	0	39	7	1	0	1	30002	1	1	6	8	22	51	10	6	40	1	2	19	2	15	2	NA
1	1	7	18897.2	32	4	2	2	1	13000	1	1	9	14	14	35	3	11	30	3	1	61	2	4	1	8
1	1	7	0	40	7	1	0	1	48000	1	1	5	7	16	38	4	5	40	1	1	40	2	13	2	NA
1	4	7	0	43	1	1	0	1	45000	1	1	10	15	8	21	2	10	40	1	1	33	2	15	2	NA
1	1	7	0	34	1	2	0	1	9600	2	7	10	16	15	37	3	10	20	1	1	60	2	12	1	NA
1	1	7	0	44	1	2	0	1	8000	2	7	9	13	9	23	2	9	15	1	1	28	2	12	1	NA
1	3	7	16169.54	44	7	1	2	1	60000	1	1	10	16	10	27	2	10	40	4	2	34	2	6	2	26.425
1	1	7	0	39	1	2	0	1	20000	1	1	5	7	22	51	10	5	36	1	2	33	2	11	2	NA
1	1	7	12991.96	35	7	2	2	1	11000	1	1	10	16	11	28	3	10	35	1	1	56	2	4	2	7.25
1	1	7	12535	39	1	2	2	1	150	4	12	10	15	13	33	3	10	4	2	2	42	2	7	1	9.37
1	3	7	19761.39	39	7	1	2	1	30000	1	1	10	15	20	48	8	10	40	1	1	30	2	12	2	26
1	1	7	17223.62	41	1	2	2	1	36000	1	1	10	16	17	40	5	10	40	1	1	36	2	13	2	21.925
1	3	7	0	43	7	1	0	1	43000	1	1	13	21	8	21	2	13	55	1	1	27	2	12	2	NA
1	1	7	0	40	1	2	0	1	24000	3	4	9	14	16	39	4	9	40	1	1	35	2	14	2	NA
1	1	7	0	43	1	1	0	1	16000	3	3	5	6	16	38	4	5	40	1	2	27	2	14	2	NA
1	1	7	0	38	1	1	0	1	19000	1	1	5	7	22	51	10	5	40	1	1	53	2	8	2	NA
1	1	7	0	43	1	2	0	1	150000	1	1	8	11	2	7	1	8	50	1	2	45	2	16	2	NA
1	1	7	13580.91	39	1	1	2	1	35360	1	1	3	3	21	49	9	3	45	1	2	50	2	12	2	16
1	4	7	0	40	1	1	0	1	30000	1	1	13	21	12	30	3	13	60	1	1	21	2	11	2	NA
1	1	7	15669.24	39	1	1	2	1	20800	1	1	5	7	16	38	4	5	32	1	2	26	2	6	1	10
1	1	7	15112.82	39	1	1	2	1	50000	1	1	2	2	19	47	7	3	40	1	1	27	2	10	2	17
1	1	8	2385.68	41	1	2	2	1	27500	1	1	9	13	17	40	5	9	-4	1	2	55	2	16	1	9
1	4	8	2685.03	39	1	2	2	0	0	5	13	15	23	24	53	2	10	-4	1	2	53	2	5	1	NA
1	3	8	2385.79	43	5	2	0	1	50872	1	1	10	15	8	21	2	10	50	1	2	59	2	12	2	20.34
1	1	8	2459	45	1	1	2	1	80000	1	1	12	20	5	13	2	12	55	1	2	48	2	14	2	16.781818
1	1	8	3194.29	39	5	1	2	1	27000	3	4	3	3	19	46	7	3	-4	1	2	44	2	9	1	NA
1	1	8	3551.44	43	7	1	2	1	22000	3	4	9	13	4	12	2	9	40	1	2	26	2	7	2	18.75
1	1	8	0	39	1	1	0	1	24000	1	1	3	3	22	51	10	3	40	1	2	54	1	9	2	NA
1	1	8	2320.68	44	1	2	0	1	53000	1	1	10	15	8	21	2	10	50	1	2	58	2	13	2	16.16
1	1	8	2486.06	40	7	1	2	1	73000	1	1	10	16	10	27	2	4	40	1	2	55	2	3	2	14
1	1	8	2780.17	43	3	2	0	1	60000	1	1	10	16	10	24	2	10	53	1	2	47	2	16	2	13.056604
1	1	8	3930.78	39	1	2	2	1	20000	1	1	10	16	2	6	1	10	40	1	2	37	2	12	2	9.375
1	1	8	3983.32	39	7	1	2	1	4684	2	7	11	18	13	33	3	11	18	1	2	23	2	14	1	10
1	1	8	0	39	7	1	0	1	6000	3	6	3	3	19	44	7	3	40	3	2	18	2	13	2	NA
1	3	8	2795.2	40	1	2	2	1	26000	1	1	13	21	12	30	3	13	40	1	2	61	2	14	2	12.5
1	1	8	3956.55	40	7	2	2	1	8000	2	7	11	18	13	33	3	11	20	1	2	25	2	11	1	8
1	1	8	2458.88	39	1	1	2	1	47000	1	1	5	7	4	12	2	5	40	1	2	50	2	14	2	50.475
1	1	8	3476.85	43	1	1	2	1	23000	1	1	7	10	17	40	5	7	40	1	2	26	2	8	2	10.33
1	1	8	3520.72	39	1	1	2	1	15000	1	1	5	7	17	40	5	5	40	1	2	49	2	8	2	7
1	1	8	0	43	5	1	0	1	22560	1	1	7	10	9	23	2	7	46	2	2	45	2	12	2	NA
1	1	8	0	40	1	2	0	1	26000	1	1	5	7	16	38	4	5	50	1	2	29	2	14	2	NA
1	3	8	0	43	1	2	0	1	34000	1	1	10	15	8	21	2	10	49	1	2	36	2	13	2	NA
1	1	8	3494.64	44	1	1	2	1	94000	1	1	8	11	1	1	1	8	40	1	2	62	2	13	2	46.625
1	1	8	3658.32	40	6	1	2	1	17000	3	4	4	4	20	48	8	4	40	1	2	45	2	6	2	13
1	1	8	0	39	7	1	0	1	7000	4	8	11	18	13	33	3	11	10	1	1	22	2	6	1	NA
1	1	8	4215.23	39	1	2	2	1	27000	1	1	9	13	7	19	2	9	40	1	2	30	2	13	2	13.6
1	4	8	3297.32	39	1	2	2	1	16000	1	1	10	15	17	40	5	10	40	1	2	55	2	11	2	10
1	1	8	3250.37	40	7	2	2	1	22275	1	1	10	16	11	28	3	10	36	3	2	55	2	8	2	10.04
1	1	8	4670.1	39	1	1	2	1	95000	1	1	5	6	16	39	4	5	60	1	2	36	2	13	2	30.45
1	1	8	3852.54	42	4	2	2	1	32000	1	1	10	16	17	40	5	10	41	1	2	53	2	13	2	19.75
1	1	8	0	40	1	1	0	1	75000	1	1	9	14	22	50	10	9	48	1	2	37	2	13	2	NA
1	1	8	0	40	1	2	0	1	63000	1	1	5	7	16	38	4	5	55	1	2	45	2	14	2	NA
1	4	8	1149.51	43	1	1	2	1	72000	1	1	13	21	12	30	3	13	50	1	2	46	2	13	2	20
1	1	8	1174.11	39	5	1	0	1	72000	1	1	4	4	19	46	7	6	40	1	2	51	2	12	2	24.1
1	1	8	1141.16	36	1	2	2	1	13000	1	1	5	7	16	38	4	5	35	1	2	44	2	13	2	7.25
1	1	8	0	39	1	1	0	1	150000	1	1	9	14	1	3	1	9	75	4	2	57	2	15	2	NA
1	1	8	1139.93	40	1	1	2	1	89000	1	1	2	2	19	47	7	3	40	1	2	60	2	15	2	37.19
1	1	8	1220.67	40	1	1	2	1	76000	1	1	2	2	19	47	7	3	-4	1	2	40	2	13	1	29.35
1	3	8	1171.89	40	7	2	2	1	41640	1	1	13	21	2	7	1	13	46	1	2	39	2	13	2	17.391304
1	1	8	1393.6	37	7	2	2	1	3000	2	7	10	16	17	40	5	10	10	1	2	18	2	16	1	5.8
1	1	8	1059.47	42	5	2	2	1	16000	1	1	10	16	17	40	5	10	50	1	2	54	2	5	2	16.5
1	4	8	0	39	1	2	0	1	17500	1	1	10	15	8	22	2	10	40	1	2	48	2	13	2	NA
1	1	8	0	43	1	1	0	1	36000	1	1	13	21	7	19	4	8	-4	1	2	30	2	12	1	NA
1	1	8	1268.22	39	1	1	0	1	60000	1	1	2	2	19	44	7	3	48	1	2	33	2	13	2	22
1	3	8	0	39	7	1	0	1	6650	2	7	11	17	8	22	2	11	16	6	2	27	1	11	1	NA
1	1	8	0	41	1	1	0	1	56000	1	1	2	2	20	48	8	3	42	1	2	52	2	14	2	NA
1	4	8	1179.47	39	1	2	2	1	39000	3	3	10	15	13	32	3	10	40	1	2	62	2	15	2	22.5
1	1	8	1268.23	42	5	1	2	1	16000	3	4	5	7	16	39	4	5	60	1	2	39	2	10	2	10
1	4	8	1102.11	44	1	2	2	1	60000	1	1	10	15	8	20	2	10	40	1	2	52	2	15	2	28.85
1	1	8	1268.23	39	1	1	2	1	157000	1	1	2	2	1	3	1	3	60	1	2	36	2	16	2	22.5
1	1	8	0	40	1	2	0	1	35000	1	1	12	20	20	48	8	3	40	1	2	27	2	14	2	NA
1	1	8	6106.08	39	6	2	2	1	40000	1	1	10	16	11	28	3	10	38	1	2	30	2	11	2	15
1	1	8	6380.38	43	1	2	2	1	30000	1	1	9	13	9	23	2	9	-4	1	2	34	2	13	1	14.5
1	2	8	6406.86	45	5	2	2	1	10500	3	6	10	15	21	49	9	10	40	1	2	51	2	11	2	43.75
1	1	8	6578.67	43	5	1	2	1	34000	1	1	4	4	21	49	9	4	50	1	2	50	2	15	2	24.6
1	1	8	0	40	7	2	0	1	10000	1	1	10	16	1	3	5	10	10	1	1	22	2	3	1	NA
1	1	8	6487.13	43	1	1	2	1	44000	1	1	11	17	9	23	2	10	50	2	2	39	2	15	2	18
1	1	8	5869.49	43	1	2	2	1	34000	1	1	10	16	3	8	2	10	-4	1	2	37	2	15	1	NA
1	1	8	6073.71	39	4	2	2	1	17000	2	7	11	18	14	35	3	11	-4	1	2	29	2	8	1	7.85
1	1	8	5529.59	45	1	2	2	1	65000	2	7	10	16	10	24	2	10	-4	4	2	44	2	16	1	NA
1	2	8	0	43	1	2	0	1	20002	1	1	13	21	16	39	4	13	35	1	1	48	2	13	2	NA
1	4	8	6133.15	44	1	2	0	1	74000	1	1	10	15	8	21	2	10	40	1	2	39	2	14	2	35.575
1	1	8	0	40	1	2	0	1	28000	1	1	10	16	17	40	5	10	40	1	1	29	2	14	2	NA
1	4	8	0	40	7	2	0	1	45000	1	1	13	21	12	30	3	13	40	6	2	31	2	12	2	NA
1	1	8	0	37	7	1	0	1	7500	2	7	5	7	20	48	8	5	15	1	2	18	2	14	1	NA
1	2	8	0	43	5	1	0	1	42000	1	1	13	21	17	40	5	13	40	1	2	59	2	15	2	NA
1	1	8	0	39	1	2	0	1	17280	3	2	4	5	17	40	3	8	40	6	2	32	2	14	2	NA
1	1	8	6651.38	43	1	1	2	1	30000	3	4	12	20	20	48	8	12	40	1	2	57	2	11	2	13
1	1	8	6346.55	40	7	2	0	1	38000	1	1	10	16	17	40	5	10	40	1	2	28	2	11	2	18.275
1	1	8	6387.17	43	1	1	0	1	85265	1	1	12	20	8	21	2	12	40	1	2	60	2	16	2	41
1	1	8	6578.67	37	1	1	2	1	15000	1	1	3	3	19	44	7	3	40	1	2	51	2	14	2	31.27
1	1	8	7046.12	40	1	1	2	1	20000	1	1	5	7	22	51	10	5	40	1	1	22	2	11	2	12.025
1	1	8	0	40	7	1	0	1	20800	1	1	4	5	21	49	9	4	40	4	2	40	2	13	2	NA
1	1	8	0	39	1	1	0	1	70000	1	1	3	3	1	1	1	3	40	1	2	45	1	15	2	NA
1	1	8	6253.21	40	7	2	2	1	6500	3	6	4	4	17	40	5	8	40	1	1	26	2	12	2	18.25
1	1	8	0	35	1	1	0	1	25000	1	1	5	7	21	49	9	5	40	1	1	40	2	7	2	NA
1	1	8	6633.57	43	1	2	2	1	8500	2	7	7	10	17	40	5	7	16	1	2	45	2	16	1	9.95
1	1	8	0	43	1	1	0	1	51000	1	1	7	10	20	48	8	7	40	1	2	46	2	11	2	NA
1	1	8	0	46	1	1	0	1	78600	1	1	10	15	1	3	1	10	45	1	2	36	2	14	2	NA
1	1	8	6601.8	43	5	2	2	1	15000	4	11	11	17	9	23	2	11	-4	1	2	45	2	13	1	15
1	1	8	5495.47	40	1	2	2	1	10585	4	8	5	7	17	40	5	5	35	1	2	43	2	12	2	10
1	1	8	6163.91	40	1	1	2	1	26345	3	4	9	13	3	8	2	9	48	1	2	48	2	12	2	30.91
1	1	8	0	39	1	1	0	1	32000	1	1	5	6	22	51	10	5	40	1	1	37	2	11	2	NA
1	1	8	5456.88	39	4	2	2	1	40000	1	1	10	16	17	40	5	10	40	1	2	58	2	10	2	20
1	1	8	0	45	1	1	0	1	125000	1	1	10	16	10	24	2	10	40	1	2	38	2	15	2	NA
1	1	8	7121.44	43	1	1	2	1	52000	1	1	11	18	13	33	3	11	35	1	2	38	2	14	2	25
1	1	8	10248.24	42	5	2	2	1	12980	2	7	10	16	15	37	3	10	30	1	1	65	2	9	1	8
1	1	8	7797.49	40	1	2	2	1	30000	1	1	9	14	17	40	5	10	40	2	1	27	2	11	2	16.25
1	3	8	8347.31	43	1	1	2	1	75000	1	1	10	16	10	25	2	10	-4	1	2	25	2	4	1	22
1	1	8	8631.85	37	7	2	2	1	32000	1	1	10	16	8	21	2	10	40	2	1	22	2	5	2	8
1	1	8	7275.13	43	1	2	2	1	70000	1	1	10	16	2	5	1	10	40	1	2	34	2	12	2	33.65
1	1	8	7327.12	38	7	2	2	1	15000	1	1	4	5	21	49	9	4	40	1	1	31	2	4	2	7
1	3	8	6993.24	44	1	2	0	1	30000	3	4	10	15	8	21	2	10	40	1	2	36	2	14	2	14.425
1	2	8	0	40	5	1	0	1	60000	1	1	13	21	3	8	2	13	40	2	2	58	2	15	2	NA
1	1	8	0	43	1	2	0	1	58000	1	1	8	11	2	5	1	8	40	1	2	27	2	15	2	NA
1	1	8	0	40	7	2	0	1	37500	1	1	4	4	3	8	2	7	40	1	2	27	2	10	2	NA
1	4	8	0	43	1	2	0	1	10000	4	9	10	15	8	21	2	10	24	1	2	44	2	13	1	NA
1	3	8	7344.14	43	5	2	2	1	46600	1	1	10	15	1	2	1	10	40	1	1	32	2	11	2	22.4
1	2	8	0	39	1	2	0	1	33280	1	1	13	21	1	1	1	13	40	1	1	52	2	15	2	NA
1	1	8	5512.15	39	1	2	2	1	40000	1	1	5	7	22	51	10	5	40	1	2	59	2	14	2	11.8
1	1	8	10477.94	39	1	1	2	1	35000	1	1	2	2	19	47	7	3	40	1	1	56	2	11	2	16.75
1	1	8	0	39	5	2	0	1	21000	1	1	5	7	17	40	5	5	40	2	2	50	2	8	2	NA
1	1	8	6774.36	40	7	2	2	1	5000	4	9	10	16	11	28	3	10	10	1	1	24	2	2	1	12
1	1	8	0	41	7	2	0	1	7200	3	6	4	4	4	12	2	4	40	1	1	24	2	2	2	NA
1	1	8	17985.24	44	1	2	2	1	52000	1	1	10	16	10	25	2	10	40	1	2	34	2	15	2	25
1	1	8	13976.08	39	6	1	2	1	22000	1	1	5	7	22	51	10	5	48	2	2	58	2	9	2	21
1	1	8	19642.03	38	1	1	2	1	14500	1	1	11	17	14	35	3	4	50	1	1	28	2	11	2	12.55
1	1	8	16009.51	40	7	2	2	1	11100	2	7	11	18	11	28	3	11	32	1	2	48	2	6	1	6.65625
1	1	8	12615.67	42	1	2	2	1	72000	1	1	10	16	10	27	2	10	40	1	1	41	2	16	2	35
1	1	8	13956.01	41	1	2	2	1	60000	2	7	10	16	10	25	2	10	36	1	2	56	2	13	2	45
1	3	8	14788.03	43	1	2	2	1	24000	1	1	9	14	1	3	1	9	40	1	1	56	2	16	2	13
1	1	8	0	39	1	1	0	1	65000	1	1	3	3	2	5	1	3	40	1	1	39	2	13	2	NA
1	1	8	0	35	1	2	0	1	15000	1	1	10	16	14	35	3	10	40	1	1	61	2	13	2	NA
1	1	8	0	42	1	2	0	1	35000	1	1	4	5	17	40	5	8	40	1	1	30	2	16	2	NA
1	1	8	0	39	1	1	0	1	12000	3	4	3	3	19	44	7	3	40	1	1	35	2	6	2	NA
1	1	8	0	43	1	2	0	1	85000	1	1	10	16	10	25	2	10	40	4	2	45	2	14	2	NA
1	4	8	0	44	1	2	0	1	69000	3	2	10	15	8	21	2	10	61	1	2	50	2	15	2	NA
1	1	8	0	43	1	1	0	1	100000	1	1	3	3	1	1	1	3	60	1	2	35	2	16	2	NA
1	1	8	17005.39	38	1	1	2	1	66000	1	1	4	5	21	49	9	4	-4	1	2	47	2	11	1	20.67
1	3	8	10688.76	43	7	2	2	1	42000	1	1	10	15	17	40	5	10	40	3	2	46	2	12	2	20.2
1	4	8	0	40	1	2	0	1	7002	4	10	10	15	14	35	3	10	35	1	2	37	2	8	2	NA
1	1	8	28764.17	43	1	1	2	1	42000	1	1	5	6	16	38	4	5	-4	3	2	33	2	3	1	NA
1	1	8	0	41	6	2	0	1	8000	1	1	10	16	11	28	3	10	40	1	1	36	2	8	2	NA
1	1	8	13406.63	43	1	2	2	1	27000	1	1	11	18	13	32	3	11	60	1	2	55	2	14	2	7.5
1	1	8	17356.67	43	1	1	2	1	120000	1	1	9	13	1	3	1	9	60	1	2	40	2	15	2	35.25
1	1	8	17277.26	43	7	2	2	1	52000	1	1	10	15	8	21	2	10	-4	1	2	34	2	11	1	NA
1	1	8	0	43	5	2	0	1	21000	1	1	10	16	8	21	2	10	-4	1	2	45	2	15	1	NA
1	1	8	6702.84	43	1	1	2	1	180000	1	1	8	11	1	1	1	9	50	1	2	50	2	15	2	55.76
1	2	8	0	39	1	1	0	1	50000	1	1	4	4	21	49	9	13	40	1	1	53	2	8	2	NA
1	4	8	0	43	1	2	0	1	58000	3	4	10	15	8	22	2	10	40	1	2	51	2	8	2	NA
1	1	8	0	40	1	2	0	1	12000	3	5	12	20	11	28	3	10	36	1	2	29	2	15	2	NA
1	3	8	6570.1	43	1	1	2	1	45000	1	1	10	15	9	23	2	10	40	1	2	48	2	15	2	21.625
1	1	8	7960.48	43	1	1	2	1	67000	1	1	3	3	19	45	7	3	40	1	2	34	2	11	2	NA
1	1	8	6620.82	39	3	1	2	1	6500	4	11	3	3	19	44	7	3	40	1	1	45	2	11	2	13.75
1	4	8	0	40	1	1	0	1	60000	1	1	10	15	14	34	3	10	40	1	2	56	2	15	2	NA
1	1	8	6592.32	43	1	1	2	0	0	5	13	15	23	24	53	9	4	30	1	2	63	2	12	1	10
1	1	8	0	40	5	1	0	1	52000	1	1	5	7	19	43	7	5	40	1	2	57	2	13	2	NA
1	1	8	0	39	5	1	0	1	15000	1	1	5	7	14	35	3	5	40	1	2	63	2	13	2	NA
1	1	8	12039.93	43	7	1	2	1	3500	2	7	12	20	9	23	2	12	4	3	2	25	2	15	1	31.25
1	1	8	6758.67	40	1	2	2	1	35000	1	1	10	16	17	40	5	10	36	1	2	44	2	14	2	18.694444
1	1	8	7449.58	41	7	2	2	1	20000	1	1	12	20	15	36	3	12	40	1	2	28	2	10	2	16.825
1	1	8	0	39	1	2	0	1	30000	1	1	7	10	17	40	5	7	40	1	2	33	2	13	2	NA
1	1	8	0	43	1	2	0	1	6568	4	10	9	14	14	35	3	9	35	1	2	39	2	6	2	NA
1	1	8	5000.75	39	5	2	2	1	34000	2	7	5	7	16	38	4	5	32	1	1	52	2	9	1	20.83
1	1	8	5080.6	40	7	2	2	1	15000	4	9	5	7	16	39	4	5	48	4	2	22	2	15	2	13
1	1	8	5321.5	39	7	1	2	1	19240	1	1	3	3	19	46	7	3	35	1	2	28	2	15	2	22
1	1	8	3052.19	39	6	2	2	1	25000	1	1	11	17	16	39	4	11	40	1	1	40	2	6	2	12
1	1	8	4971.96	39	7	1	2	1	169000	1	1	12	20	15	37	3	11	35	1	2	38	2	15	2	15.371429
1	1	8	4182.69	40	5	2	2	1	27000	1	1	5	7	16	39	4	5	20	1	2	38	2	11	1	16.88
1	1	8	5968.62	39	7	1	2	1	40000	1	1	12	20	20	48	8	12	40	1	2	24	2	12	2	19.225
1	1	8	5439.69	39	1	1	2	1	50000	1	1	8	11	17	40	5	8	40	1	1	48	2	13	2	24.05
1	1	8	0	40	5	1	0	1	26000	2	7	11	18	15	37	3	11	32	1	1	60	1	7	1	NA
1	1	8	0	39	7	2	0	1	53790	1	1	9	14	17	40	5	9	40	1	1	26	2	10	2	NA
1	1	8	0	39	7	1	0	1	19000	1	1	5	6	17	40	5	5	40	1	1	20	2	7	2	NA
1	1	8	0	39	1	2	0	1	49000	1	1	11	18	17	40	5	11	40	1	2	39	2	15	2	NA
1	1	8	0	39	4	2	0	1	38480	1	1	11	18	13	32	3	11	40	4	2	53	2	11	2	NA
1	1	8	0	39	1	2	0	1	20000	1	1	11	17	16	39	4	11	40	1	2	48	2	12	2	NA
1	1	8	0	41	1	1	0	1	29000	1	1	11	17	4	12	2	11	40	4	2	28	2	7	2	NA
1	1	8	0	39	1	1	0	1	45000	1	1	10	16	6	17	2	10	40	1	2	39	2	12	2	NA
1	1	8	5460.81	37	7	1	2	1	63000	3	3	3	3	19	44	7	3	-4	1	2	27	2	13	1	16.74
1	1	8	6867.48	39	1	1	2	1	35000	1	1	4	4	21	49	9	4	40	1	1	30	2	14	2	17
1	1	8	5632.17	41	5	2	2	0	0	5	13	15	23	24	53	4	5	40	1	2	48	2	8	2	9.09
1	4	8	4833.22	40	1	2	2	1	40000	1	1	10	16	17	40	5	10	46	1	2	57	2	15	2	16.717391
1	3	8	4441.53	39	1	2	2	1	35000	1	1	13	21	6	17	2	13	40	1	2	26	2	14	2	18.38
1	1	9	0	39	1	2	0	1	33000	1	1	10	16	17	40	5	10	40	1	2	45	1	13	2	NA
1	1	9	12885.98	40	7	1	2	1	11000	3	4	5	7	22	51	10	6	50	1	2	38	2	11	2	35.38
1	1	9	15469.05	37	7	1	2	1	13000	2	7	11	18	13	33	3	11	30	1	2	20	2	9	1	8.55
1	4	9	11640.92	42	1	2	0	1	32000	1	1	13	21	2	7	1	13	40	1	2	37	2	14	2	18
1	1	9	11854.33	39	7	2	2	1	10260	2	7	11	17	15	37	3	11	12	4	2	27	2	7	1	8.55
1	3	9	10774.14	43	1	2	2	1	81860	1	1	13	21	1	1	1	13	40	1	2	48	2	16	2	39.35
1	1	9	9027.65	39	5	2	2	1	21000	1	1	10	16	14	35	3	8	-4	1	2	57	1	2	1	8
1	4	9	0	39	1	2	0	1	10000	4	9	10	15	17	40	5	10	12	1	2	47	2	14	1	NA
1	1	9	13529.09	43	1	2	2	1	35000	3	5	5	7	17	40	5	5	40	4	2	32	2	15	2	33.65
1	1	9	14189.87	37	7	2	2	1	9000	4	9	11	18	13	33	3	11	18	1	2	18	2	16	1	8.75
1	4	9	10583.85	40	1	1	0	1	76000	1	1	3	3	19	43	7	3	40	1	2	62	2	14	2	32.5
1	1	9	14137.13	43	1	1	2	1	95000	1	1	3	3	2	5	1	3	50	1	2	28	2	15	2	36.54
1	1	9	11147.17	44	1	1	2	1	52000	1	1	9	13	2	6	1	9	40	1	2	58	2	11	2	25
1	1	9	10704.37	40	1	1	0	1	67000	1	1	7	10	17	40	5	7	40	1	2	61	2	13	2	25.04
1	1	9	13059.82	43	5	1	2	1	35000	1	1	4	5	17	40	5	4	40	1	2	38	2	14	2	16.825
1	1	9	9141.66	39	5	2	2	1	35000	1	1	11	17	17	40	5	11	40	1	2	56	2	10	2	17
1	1	9	0	39	3	1	0	0	0	5	13	15	23	24	53	3	8	40	6	1	29	2	6	2	NA
1	4	9	0	39	1	1	0	1	30000	1	1	10	15	14	35	3	10	40	4	2	52	2	8	2	NA
1	2	9	0	40	1	2	0	1	12000	4	9	13	21	17	40	5	13	15	1	2	48	2	13	1	NA
1	1	9	0	40	1	2	0	1	48000	1	1	10	16	10	27	2	5	35	2	2	31	2	5	2	NA
1	1	9	0	39	1	1	0	1	36000	1	1	5	7	16	39	4	5	40	1	2	35	2	13	2	NA
1	1	9	0	43	1	1	0	1	35000	3	3	11	18	1	3	3	11	45	1	2	47	2	11	2	NA
1	4	9	10151.79	44	1	2	1	1	98000	1	1	10	15	1	3	1	10	50	1	2	54	1	16	2	39.62
1	1	9	13791.89	43	7	1	2	1	47000	1	1	4	5	21	49	9	4	40	1	2	29	2	16	2	23.075
1	4	9	0	45	1	2	0	1	44000	2	7	13	21	7	18	2	13	28	1	2	41	2	15	1	NA
1	1	9	13080.2	36	7	1	2	1	8750	4	8	11	18	13	32	3	11	16	1	2	37	2	13	1	10
1	1	9	15469.05	39	7	1	2	1	13300	3	3	5	7	17	40	5	5	35	1	2	20	2	5	2	9.5
1	4	9	0	39	7	1	0	1	6000	4	10	10	15	19	44	3	10	23	1	2	27	2	13	1	NA
1	1	9	7256.17	40	5	2	2	1	30000	1	1	8	12	16	39	4	8	50	1	2	58	2	9	2	11.54
1	2	9	8166.67	40	1	2	0	1	30000	1	1	6	8	17	40	5	6	-4	1	2	54	2	13	1	NA
1	1	9	6290.69	40	7	1	2	1	8000	3	5	5	7	16	39	4	5	40	4	2	19	2	2	2	9
1	1	9	7983.46	37	7	2	2	1	13000	1	1	11	18	13	32	3	11	18	1	2	23	2	10	1	8.4
1	1	9	6774.2	40	7	1	2	1	15000	1	1	11	18	13	33	3	3	40	1	2	32	2	13	2	9.225
1	1	9	7134.44	39	1	1	2	1	34031	3	3	4	4	21	49	9	4	40	1	2	56	1	14	2	19
1	1	9	6773.07	42	1	1	2	1	58000	1	1	8	11	1	1	1	8	40	1	2	37	2	15	2	24.025
1	1	9	0	39	1	1	0	0	0	5	13	15	23	24	53	10	9	40	1	2	43	1	6	2	NA
1	1	9	0	40	7	2	0	1	30159	1	1	9	14	1	3	1	9	50	1	2	36	2	10	2	NA
1	1	9	6898.56	39	1	2	2	1	15350	2	7	10	16	17	40	5	10	32	1	2	27	2	12	1	12.5
1	1	9	6843.45	40	7	1	0	1	24000	2	7	5	7	16	39	4	5	30	1	2	32	2	14	1	15
1	1	9	10916	42	1	1	2	1	89000	1	1	8	11	3	8	2	8	40	3	2	34	2	15	2	34.625
1	1	9	7554.24	43	5	2	2	1	35200	3	2	3	3	1	1	1	10	40	1	2	63	2	12	2	27.775
1	1	9	8110.3	39	5	2	2	1	4000	4	10	11	18	13	33	3	11	4	1	2	23	2	4	1	8.5
1	4	9	7364.4	37	1	1	0	1	17400	3	3	10	15	19	46	7	10	40	1	2	26	2	7	2	10.88
1	1	9	0	43	1	2	0	1	75000	1	1	8	11	9	23	2	8	40	1	2	29	2	15	2	NA
1	1	9	8259.95	39	7	1	0	1	12000	2	7	5	7	22	51	10	5	30	1	2	20	2	7	1	8.6
1	3	9	6276.79	44	1	2	0	1	70000	1	1	13	21	2	5	1	13	40	1	2	42	2	15	2	21.64
1	1	9	0	46	1	1	0	1	93000	1	1	10	15	8	20	2	10	60	1	2	45	2	14	2	NA
1	1	9	7018.32	39	1	1	0	1	25000	3	2	4	5	21	49	9	4	48	1	2	49	2	14	2	17.4
1	1	9	8542.09	43	1	2	2	1	99000	1	1	8	11	2	5	1	12	32	1	2	45	2	16	1	20.8
1	3	9	6979.19	43	1	1	2	1	96000	1	1	13	21	12	29	3	13	50	1	2	46	2	16	2	53.84
1	1	9	6898.56	34	1	2	2	1	22500	2	7	10	16	14	35	3	10	32	1	2	29	2	9	1	13
1	4	9	7207.37	40	1	1	0	1	20500	2	7	7	10	17	40	5	7	20	1	2	56	2	14	1	15.2
1	1	9	6935.33	40	1	2	2	1	23000	1	1	10	16	13	33	3	10	40	1	2	30	2	12	2	12.35
1	1	9	6241.79	40	1	2	2	1	93000	1	1	8	11	7	19	2	8	45	1	2	40	2	15	2	34.65
1	1	9	7782.73	43	7	1	2	1	8000	2	7	10	15	1	3	1	10	-4	4	2	30	2	6	1	9
1	1	9	6898.56	43	7	2	2	1	15400	4	9	11	17	8	21	2	11	30	1	2	25	2	6	1	11.26
1	4	9	0	39	7	1	0	1	10000	3	4	4	5	21	49	3	6	40	1	1	25	2	9	2	NA
1	1	9	6315.77	40	1	2	2	1	8000	2	7	4	4	21	49	9	4	-4	4	2	44	2	8	1	12
1	1	9	7371.45	41	1	2	2	1	80000	1	1	10	16	10	25	2	10	40	1	2	58	2	15	2	48.5
1	1	9	6880.68	43	1	1	2	1	85000	1	1	6	8	1	2	1	6	50	1	2	39	2	13	2	30.76
1	1	9	0	33	1	2	0	1	12500	3	3	11	18	13	32	3	11	37	3	1	29	2	6	2	NA
1	1	9	0	39	5	2	0	1	9500	3	5	9	14	9	23	2	9	40	1	2	47	2	13	2	NA
1	2	9	6276.79	43	1	2	0	1	54000	1	1	6	8	17	40	5	6	40	1	2	43	2	14	2	26.43
1	1	9	6935.33	43	1	2	2	1	34859	2	7	5	7	17	40	5	5	30	1	2	33	2	14	1	22.333333
1	1	9	7168.55	43	1	1	2	1	409068	1	1	4	4	1	1	1	4	40	1	2	51	2	16	2	72.125
1	1	9	0	39	1	1	0	1	17000	1	1	11	18	13	32	3	11	40	1	1	37	2	8	2	NA
1	1	9	16401.77	40	1	1	2	1	54000	1	1	9	14	14	35	3	9	40	1	1	32	2	10	2	31.25
1	1	9	12537.9	40	7	1	2	1	33000	3	3	4	4	21	49	9	4	40	1	1	23	2	14	2	14.5
1	1	9	9512.44	40	7	1	2	1	2560	4	11	10	15	17	40	5	10	20	1	1	19	2	7	1	8
1	1	9	10931.18	33	5	1	2	1	21000	1	1	11	18	13	33	3	11	40	1	1	46	2	4	2	10
1	1	9	10685.24	44	1	1	2	1	70002	1	1	9	13	1	1	1	9	60	1	2	49	2	14	2	22.433333
1	1	9	10596.26	39	1	1	0	1	70000	1	1	6	9	19	44	7	3	-4	1	2	61	1	14	1	15
1	1	9	0	40	1	2	0	1	34000	2	7	5	7	20	48	8	5	32	6	2	47	1	10	1	NA
1	1	9	10891.85	39	7	2	2	0	0	5	13	15	23	24	53	5	10	15	1	1	19	2	14	1	8
1	1	9	8284.23	40	5	2	2	1	21000	1	1	11	18	16	38	4	5	50	3	2	45	2	12	2	NA
1	1	9	13550.54	43	1	2	2	1	70000	1	1	9	13	2	6	1	9	40	1	2	28	2	15	2	33.65
1	1	9	12679.15	41	5	2	2	1	26000	2	7	10	16	10	27	2	10	25	1	1	31	2	12	1	30
1	1	9	11497.51	38	1	1	2	1	40000	2	7	3	3	19	46	7	3	20	1	1	39	2	8	1	20
1	1	9	15019.36	40	1	1	2	1	80000	1	1	11	17	15	37	3	7	40	3	1	21	2	12	2	7.25
1	4	9	11121.91	43	7	1	2	1	9600	2	7	3	3	19	44	7	3	58	1	2	25	2	5	2	11.37
1	1	9	11445.98	43	1	1	2	1	150000	1	1	8	12	1	1	1	8	40	1	2	45	2	16	2	72.125
1	1	9	11374.54	34	1	2	2	0	0	5	13	15	23	24	53	3	12	42	4	2	41	2	13	2	7
1	1	9	11181.61	43	7	1	2	1	8000	3	5	4	4	16	39	4	4	40	4	2	25	2	1	2	11.55
1	1	9	10393.12	39	1	1	2	1	24000	2	7	11	17	9	23	2	11	28	1	2	30	2	14	1	24.714286
1	1	9	8093.33	40	7	1	2	1	11000	2	7	5	7	17	40	5	5	20	1	1	19	2	10	1	8
1	1	9	8810.44	40	7	2	2	1	45000	1	1	11	17	9	23	2	11	40	1	1	40	2	15	2	15
1	1	9	10691.88	37	7	1	2	0	0	5	13	15	23	24	53	7	3	-4	1	1	21	2	8	1	10.5
1	1	9	11047.14	40	7	1	2	1	12000	2	7	10	16	11	28	3	10	-4	1	1	26	2	6	1	10
1	1	9	11523.86	35	1	1	2	1	44200	1	1	12	20	20	48	8	12	48	1	1	37	2	10	2	17.708333
1	1	9	12541.24	44	1	1	2	1	45000	1	1	11	17	19	45	7	11	40	4	2	51	2	14	2	19.75
1	1	9	12541.24	40	7	1	2	1	41600	1	1	6	8	17	40	5	6	60	4	2	54	2	5	2	14.166667
1	1	9	12101.39	44	7	2	2	1	30000	3	5	5	6	9	23	2	5	40	1	2	36	2	16	2	40
1	1	9	11472.21	43	1	1	2	1	50000	1	1	9	13	4	10	2	9	50	1	2	47	2	15	2	23.08
1	4	9	12418.65	39	1	2	0	1	31000	1	1	10	15	1	3	1	10	40	1	2	45	2	14	2	12.25
1	1	9	10717.02	43	6	2	2	1	75000	1	1	11	17	1	1	1	11	45	6	2	50	2	15	2	32.044444
1	1	9	12679.15	37	1	2	2	1	12000	1	1	11	18	14	35	3	11	40	1	1	32	2	10	2	8.25
1	1	9	11471.15	39	1	2	2	1	45000	1	1	9	13	2	5	1	9	40	1	1	58	2	13	2	21
1	1	9	12013.35	44	1	1	2	1	100000	1	1	4	4	4	12	2	4	40	1	1	44	2	9	2	47.5
1	1	9	11096.48	39	3	1	0	1	45000	1	1	8	11	2	7	1	8	40	1	2	27	2	12	2	27.125
1	1	9	11847.28	43	5	1	2	1	40000	1	1	4	4	3	8	2	4	40	1	2	57	2	10	2	17.3
1	1	9	13153.2	45	1	1	2	1	200000	1	1	10	16	10	24	2	10	40	4	2	59	2	8	2	52.875
1	1	9	12569.78	45	1	1	2	1	409068	1	1	10	15	8	20	2	10	40	4	2	62	2	10	2	17.3
1	1	9	8111.88	38	7	1	2	1	18000	1	1	4	4	22	51	10	4	40	1	1	19	2	5	2	10.45
1	1	9	11047.14	38	5	1	2	1	30000	1	1	5	6	22	51	10	5	40	1	1	28	2	4	2	10
1	1	9	12040.88	40	7	1	2	0	0	5	13	15	23	24	53	4	5	25	1	1	42	2	2	1	9
1	1	9	11209.8	43	5	1	2	1	31200	1	1	5	7	20	48	8	5	40	4	2	48	2	14	2	15
1	4	9	11330.25	44	1	2	0	1	63000	1	1	10	15	8	21	2	10	40	1	2	41	2	14	2	30.3
1	1	9	12447.11	43	1	2	2	1	1350	2	7	10	15	8	21	2	10	2	1	2	45	2	14	1	25
1	1	9	0	32	1	1	0	1	25000	1	1	4	4	21	49	9	4	35	1	1	56	2	13	2	NA
1	1	9	0	37	1	2	0	1	25000	1	1	4	4	3	8	2	4	36	1	1	42	2	13	2	NA
1	1	9	0	37	1	1	0	1	15000	1	1	9	14	14	35	3	9	35	1	1	56	2	12	2	NA
1	1	9	0	39	7	1	0	1	23000	1	1	5	7	17	40	5	5	30	1	1	33	2	8	1	NA
1	1	9	0	39	1	1	0	1	17000	3	4	3	3	19	43	7	3	40	1	1	49	2	12	2	NA
1	1	9	0	41	7	2	0	1	6000	2	7	10	16	11	28	3	10	16	1	1	20	2	2	1	NA
1	1	9	0	35	1	2	0	1	52000	1	1	10	16	10	27	2	10	40	1	1	51	2	13	2	NA
1	1	9	0	40	1	2	0	1	18000	2	7	11	18	13	33	3	11	30	1	2	44	2	11	1	NA
1	1	9	0	39	1	1	0	1	51000	1	1	12	20	20	48	8	12	49	1	1	43	2	11	2	NA
1	1	9	0	32	1	1	0	1	8000	4	12	11	18	13	33	3	11	24	1	1	63	2	4	1	NA
1	4	9	0	41	3	2	0	1	32000	1	1	10	16	11	28	3	10	40	1	1	43	2	9	2	NA
1	1	9	0	39	7	2	0	1	18000	2	7	10	16	11	28	3	10	20	1	1	42	2	9	1	NA
1	4	9	0	44	7	2	0	1	53000	1	1	10	15	8	21	2	10	40	1	1	27	2	15	2	NA
1	1	9	0	39	4	2	0	1	8000	2	7	10	16	22	51	10	10	24	1	1	62	2	9	1	NA
1	1	9	0	35	1	2	0	1	30000	1	1	10	16	14	35	3	10	40	1	1	45	2	11	2	NA
1	1	9	0	34	7	2	0	1	15600	3	2	5	7	19	43	7	5	40	1	1	47	2	8	2	NA
1	1	9	0	43	1	1	0	1	30000	1	1	5	7	16	38	4	5	40	4	2	35	2	14	2	NA
1	3	9	0	43	1	1	0	1	106154	1	1	10	15	8	21	2	10	48	1	2	41	2	14	2	NA
1	1	9	0	40	1	2	0	1	12000	2	7	5	7	9	23	2	5	25	1	2	33	2	6	1	NA
1	1	9	0	39	1	1	0	1	12000	2	7	10	16	15	37	3	10	30	1	2	43	2	6	1	NA
1	2	9	0	40	1	2	0	1	52000	1	1	6	8	17	40	5	6	40	2	2	60	2	13	2	NA
1	1	9	0	39	1	2	0	1	29300	1	1	12	20	17	40	5	12	48	4	2	54	2	9	2	NA
1	4	9	0	40	1	2	0	1	38000	1	1	13	21	17	40	5	13	40	2	2	54	2	14	2	NA
1	1	9	0	43	1	2	0	1	72000	3	3	10	16	10	25	2	10	40	4	2	32	2	8	2	NA
1	1	9	0	43	1	2	0	1	45500	1	1	10	15	8	21	2	10	40	1	2	42	2	14	2	NA
1	1	9	0	45	1	1	0	1	100000	1	1	9	13	7	18	2	9	50	1	2	46	2	15	2	NA
1	1	9	10854.55	44	7	1	2	1	60000	1	1	7	10	9	23	2	7	40	4	2	41	2	15	2	30.275
1	1	9	10608	40	5	1	0	1	60000	1	1	5	7	16	38	4	5	78	1	2	47	2	13	2	20.25
1	1	9	11414.79	39	7	1	2	1	5000	4	11	11	18	13	32	3	11	25	1	2	19	2	6	1	8.5
1	1	9	12179.14	43	1	1	2	1	73000	1	1	5	6	3	8	2	5	40	4	2	37	2	15	2	35.1
1	1	9	12120.12	40	1	2	2	1	98400	1	1	5	7	1	1	1	5	40	1	2	45	2	16	2	42.5
1	4	9	10424.65	44	5	2	0	1	54000	1	1	10	15	8	21	2	10	75	1	2	58	2	14	2	20.546667
1	1	9	10363.95	43	7	2	2	1	60000	1	1	9	14	7	19	2	9	40	3	2	42	2	13	2	28.85
1	3	9	15534.28	40	7	2	2	1	10000	2	7	10	15	15	37	3	10	7	1	2	20	2	15	1	8
1	1	9	12207.05	44	1	1	2	1	115000	1	1	4	4	4	12	2	4	40	4	2	39	2	15	2	55.3
1	1	9	0	43	7	1	0	1	18720	3	5	7	10	9	23	2	7	40	1	1	31	2	15	2	NA
1	1	9	0	43	1	1	0	1	39000	1	1	5	7	17	40	5	5	40	4	2	50	2	12	2	NA
1	1	9	0	43	1	1	0	1	55000	1	1	5	6	4	12	2	9	40	4	2	51	2	12	2	NA
1	1	9	0	43	1	1	0	1	78000	1	1	13	21	4	12	2	9	50	4	2	34	2	16	2	NA
1	2	9	0	43	1	2	0	1	86000	1	1	13	21	2	7	1	13	40	1	2	55	2	15	2	NA
1	1	9	0	40	5	1	0	1	73000	1	1	10	16	10	27	2	10	40	2	2	59	2	14	2	NA
1	1	9	14192.32	39	5	2	2	1	18500	3	2	4	5	21	49	9	4	13	2	2	58	2	6	1	10.1
1	1	9	11049.37	40	1	1	0	1	52500	1	1	5	6	17	40	5	5	40	1	2	49	2	13	2	20.5
1	3	9	12208.37	38	1	2	0	1	7152	2	7	10	16	11	28	3	10	15	1	2	48	2	6	1	8
1	1	9	10900.76	39	1	1	0	1	60000	1	1	3	3	4	12	1	12	40	1	2	47	2	15	2	64.42
1	1	9	9370.1	40	1	2	2	1	33002	1	1	5	6	17	40	5	5	40	1	1	40	2	12	2	72.125
1	1	9	13281.53	39	1	1	2	1	25002	1	1	3	3	19	43	7	3	40	1	1	35	2	8	2	15
1	1	9	0	33	1	1	0	1	17000	1	1	12	20	20	48	8	12	40	1	1	37	2	10	2	NA
1	1	9	11683.23	40	7	1	2	1	80000	1	1	7	10	20	48	8	7	40	1	2	41	2	14	2	38.45
1	4	9	13738	39	1	2	0	1	40000	1	1	10	16	17	40	5	10	40	1	2	32	2	15	2	21.16
1	1	9	12138.36	41	1	1	0	1	35000	1	1	5	7	16	38	4	5	40	1	2	33	2	13	2	19
1	1	9	12210.99	40	1	2	2	1	6000	1	1	6	8	17	40	4	5	40	1	2	41	2	16	2	15.375
1	1	9	15777.92	40	7	2	2	1	7800	2	7	5	7	16	39	4	5	20	1	2	21	2	16	1	12
1	3	9	17581.65	40	7	1	2	1	3000	4	9	10	15	8	21	2	10	20	3	1	21	2	7	1	11.5
1	1	9	10144.82	43	1	2	2	1	36000	1	1	5	7	22	50	10	5	40	4	2	55	2	13	2	16.25
1	1	9	16335.56	37	1	1	2	1	23000	1	1	12	20	20	48	8	12	40	1	1	32	2	9	2	11.55
1	1	9	14069.03	40	7	2	2	1	45000	1	1	5	7	20	48	8	5	40	1	1	25	2	11	2	21.4
1	1	9	0	34	1	1	0	1	40000	3	3	8	12	19	44	7	3	40	1	1	39	2	13	2	NA
1	1	9	0	39	1	2	0	1	28000	1	1	4	4	21	49	9	4	40	1	1	42	2	12	2	NA
1	1	9	0	33	5	2	0	1	14000	3	5	4	4	21	49	9	4	-4	1	1	50	2	7	1	NA
1	2	9	10774.38	40	7	1	2	1	19000	3	4	8	12	20	48	8	8	40	1	2	45	2	6	2	16
1	1	9	14239.26	39	7	1	2	1	35000	1	1	3	3	19	44	7	3	40	1	1	40	2	8	2	10
1	1	9	13836.17	37	6	2	2	1	3000	2	7	10	16	15	37	3	10	20	1	1	33	2	13	1	8.45
1	1	9	13894.71	34	7	2	2	1	11000	1	1	11	18	2	5	1	11	35	1	1	27	2	9	2	8.6
1	1	9	12138.36	39	7	1	0	1	44332	1	1	5	6	22	51	10	5	40	1	2	34	2	11	2	20.43
1	1	9	12487.29	39	7	1	2	1	15000	2	7	11	18	13	33	3	11	-4	1	1	21	2	12	1	8
1	1	9	0	39	1	1	0	1	12000	1	1	5	6	17	40	5	5	40	1	1	22	2	12	2	NA
1	1	9	0	39	1	2	0	1	10000	2	7	11	18	13	33	3	11	32	1	1	21	2	12	1	NA
1	1	9	0	43	1	2	0	1	61000	1	1	8	11	2	5	1	8	37	1	2	35	2	11	2	NA
1	1	9	0	43	7	1	0	1	19200	1	1	9	14	17	40	5	9	40	1	2	27	2	11	2	NA
1	1	9	11640.8	38	7	1	2	0	0	5	13	15	23	24	53	5	5	38	1	2	18	2	15	2	9
1	1	9	0	39	1	2	0	1	18000	1	1	9	13	10	27	2	9	36	1	2	49	2	1	2	NA
1	1	9	0	39	7	1	0	0	0	5	13	15	23	24	53	4	11	20	6	2	20	2	11	1	NA
1	1	9	12372.86	42	1	2	2	1	55000	2	7	10	16	10	27	2	10	32	1	2	46	2	15	1	13.5
1	1	9	10829.21	40	1	1	0	1	42000	3	4	3	3	19	44	7	3	40	1	2	48	2	16	2	25
1	1	9	18452.25	39	7	1	2	0	0	5	13	15	23	24	53	5	5	40	3	2	26	2	9	2	8
1	1	9	15031.84	39	7	2	2	1	70000	1	1	10	16	10	27	2	10	60	4	2	33	2	13	2	21
1	1	9	12550.84	39	7	1	2	1	12000	4	8	11	18	13	32	3	11	-4	1	1	23	2	14	1	10
1	4	9	0	41	7	2	0	1	13000	2	7	10	15	8	22	2	10	25	6	2	22	2	8	1	NA
1	1	9	0	38	1	2	0	1	18000	1	1	9	14	14	35	3	9	40	1	1	52	2	12	2	NA
1	4	9	11937.68	43	7	2	0	1	71068	3	3	10	15	8	21	2	10	50	1	2	54	2	13	2	35.1
1	1	9	11034.58	39	1	1	2	1	45000	4	9	11	17	9	23	2	11	-4	1	2	58	2	15	1	NA
1	1	9	15072.06	40	7	1	2	1	18000	1	1	8	11	16	39	4	5	40	2	2	37	2	15	2	13.95
1	1	9	10423.84	41	1	1	2	1	110000	1	1	4	4	21	49	9	4	40	1	2	48	2	15	2	39.5
1	1	9	11833.57	41	1	2	2	1	40000	1	1	10	16	17	40	5	10	40	4	2	44	2	15	2	13.25
1	4	9	10423.84	40	1	1	2	1	35000	1	1	13	21	3	8	2	13	40	1	2	48	2	14	2	28.825
1	2	9	11493.29	44	2	2	2	1	110000	1	1	13	21	4	12	2	13	50	1	1	47	2	16	2	42.3
1	1	9	11441.57	40	1	2	2	1	20800	1	1	8	12	1	3	1	8	40	1	1	47	2	11	2	21.25
1	1	9	13976.88	44	7	1	2	1	30000	1	1	9	13	21	49	9	9	40	2	2	46	2	10	2	14.425
1	1	9	11690.6	43	7	1	2	1	23000	1	1	5	6	16	39	1	8	40	1	2	34	2	12	2	46.15
1	1	9	0	42	7	1	0	1	9800	2	7	10	15	9	23	5	5	65	6	1	24	2	16	2	NA
1	1	9	0	32	1	2	0	1	23000	1	1	10	16	14	35	3	10	40	1	1	41	2	8	2	NA
1	3	9	0	40	1	2	0	1	30000	1	1	10	15	15	37	3	10	40	1	1	46	2	14	2	NA
1	1	9	0	40	7	2	0	1	12000	2	7	10	15	8	21	2	10	35	1	1	20	2	13	2	NA
1	1	9	0	33	1	1	0	1	33400	1	1	3	3	19	44	7	3	40	1	1	42	2	11	2	NA
1	1	9	0	39	4	2	0	1	39000	1	1	11	18	1	1	1	11	50	1	2	42	2	12	2	NA
1	1	9	0	43	1	2	0	1	75000	1	1	10	16	10	25	2	10	40	4	2	56	2	7	2	NA
1	1	9	13042.06	40	7	2	2	1	45000	2	7	10	16	10	27	2	10	32	1	2	36	2	12	1	30.75
1	1	9	10275.46	39	1	1	2	1	49000	1	1	5	7	22	51	10	5	40	1	1	50	2	15	2	21.2
1	1	9	12487.29	39	7	1	2	1	3400	4	12	5	6	22	51	10	5	30	1	1	21	2	15	1	8
1	1	9	10658.56	43	1	2	2	1	4200	4	11	5	7	16	38	4	5	24	6	2	42	2	15	1	10
1	1	9	12364.19	44	1	2	2	1	160000	1	1	9	13	1	3	1	9	60	1	2	43	2	16	2	4.8
1	1	9	11829.81	43	1	1	2	1	409068	1	1	8	11	16	39	4	8	50	1	2	43	2	16	2	57.7
1	1	9	24793.87	43	1	1	2	1	27000	1	1	1	1	1	2	1	3	-4	2	1	29	2	14	1	NA
1	1	9	12464.66	44	1	2	2	1	35000	1	1	8	11	17	40	5	5	40	1	2	48	2	16	2	16.83
1	1	9	10959.35	39	5	1	2	1	26000	1	1	10	15	14	35	3	10	40	1	2	63	2	5	2	14.4
1	4	9	0	44	7	2	0	1	55000	1	1	10	15	8	21	2	10	40	1	1	28	2	15	2	NA
1	1	9	0	43	1	1	0	1	90000	1	1	4	5	5	13	2	4	40	4	2	52	2	13	2	NA
1	1	9	0	39	7	1	0	1	8000	3	5	4	4	21	49	9	4	40	4	2	31	2	10	2	NA
1	1	9	18461.75	40	7	1	2	1	12000	2	7	5	7	16	39	4	5	20	6	1	27	2	11	1	10
1	4	9	11216.27	39	1	2	2	1	7000	4	10	11	18	13	33	3	11	17	1	1	50	2	15	1	8
1	2	9	11683.23	43	7	1	0	1	47000	3	3	6	8	22	51	10	6	47	1	2	41	2	12	2	26.595745
1	1	9	12210.99	43	1	2	2	1	55000	1	1	5	7	16	38	4	5	50	1	2	40	2	15	2	21.16
1	4	9	0	43	1	2	0	1	66000	1	1	10	16	6	17	2	10	40	2	2	47	2	14	2	NA
1	1	9	13738	46	7	2	2	1	43000	1	1	10	16	5	15	2	10	40	1	2	32	2	16	2	20.675
1	1	9	12525.35	37	5	1	2	1	44000	1	1	9	13	5	16	2	4	40	4	2	36	2	15	2	9
1	1	9	15975.87	43	7	2	2	1	47000	1	1	9	13	3	8	2	9	75	1	2	23	2	13	2	25.64
1	4	9	10696.48	42	1	2	2	1	2700	4	11	10	15	12	29	3	10	-4	1	2	58	2	9	1	NA
1	1	9	14955.73	43	7	2	2	1	50000	1	1	6	8	17	40	5	6	40	4	2	33	2	11	2	20
1	1	9	12136.33	39	1	1	2	1	68640	1	1	5	7	16	38	4	5	40	1	2	38	2	13	2	33
1	1	9	0	39	6	2	0	1	15000	1	1	11	18	16	39	4	11	40	1	1	20	2	14	2	NA
1	1	9	0	40	1	1	0	1	20000	4	11	3	3	19	43	7	3	20	1	2	56	2	13	1	NA
1	1	9	0	43	1	2	0	1	75000	1	1	9	13	5	13	2	9	30	4	2	34	2	16	1	NA
1	1	9	0	43	1	1	0	1	90000	3	3	4	4	1	3	1	4	45	4	2	44	2	15	2	NA
1	1	9	10982.71	40	7	2	2	1	3000	2	7	10	15	9	23	2	10	8	1	1	19	2	9	1	10
1	1	9	19006.73	37	7	1	2	0	0	5	13	15	23	24	53	2	10	16	6	1	18	2	14	1	10
1	1	9	13484.97	43	1	2	2	1	45000	2	7	10	16	10	25	2	10	34	1	2	29	2	15	1	19.34
1	1	9	12487.29	36	1	1	2	1	32000	1	1	3	3	19	43	7	3	40	1	1	24	2	7	2	22.88
1	1	9	16335.56	34	7	1	2	1	19200	3	2	3	3	19	46	7	3	40	1	1	31	2	8	2	9.5
1	1	9	10887.06	40	5	1	0	1	140000	1	1	6	9	21	49	9	6	50	1	1	47	2	15	2	50
1	1	9	15153.98	34	1	2	2	0	0	5	13	15	23	24	53	3	12	21	1	1	39	2	8	1	8.571429
1	1	9	12288.6	40	7	1	2	1	60000	1	1	5	6	3	8	2	5	40	1	2	35	2	15	2	28.825
1	1	9	13977.55	39	7	1	2	1	9600	3	5	7	10	20	48	2	9	40	4	2	31	2	8	2	16
1	1	9	11143.07	44	1	1	2	1	22000	3	5	9	13	1	2	1	9	52	1	2	54	2	16	2	48.076923
1	1	9	0	40	7	1	0	1	1500	4	12	5	7	22	51	10	5	-4	1	1	29	2	14	1	NA
1	1	9	0	40	1	1	0	1	120000	1	1	9	13	1	1	1	9	40	1	2	42	2	16	2	NA
1	1	9	0	40	5	2	0	1	20520	3	2	10	15	17	40	5	10	40	1	1	49	2	7	2	NA
1	2	9	0	42	1	1	0	1	130000	1	1	12	20	20	48	8	12	60	4	2	54	2	13	2	NA
1	1	9	10144.82	39	5	2	2	1	14400	1	1	11	18	13	33	3	11	40	4	2	58	2	5	2	10
1	1	9	0	39	1	1	0	1	26000	1	1	9	14	14	34	3	9	32	1	1	55	2	12	1	NA
1	1	9	20418.76	37	7	1	2	1	11000	3	4	5	7	17	40	5	5	28	3	1	30	2	8	1	9.25
1	1	9	12954.5	37	3	1	2	1	10000	1	1	6	8	22	51	10	6	40	1	1	25	2	14	2	12.85
1	1	9	0	34	1	1	0	1	27000	1	1	3	3	19	44	7	3	-4	1	1	39	2	9	1	NA
1	1	9	11241.18	45	1	1	2	1	75000	1	1	8	11	7	18	2	8	40	1	2	44	2	15	2	30.275
1	1	9	10588.62	40	1	1	2	1	409068	1	1	5	6	16	38	4	5	70	1	2	54	2	16	2	41.214286
1	1	9	0	43	4	2	0	1	90000	1	1	4	4	4	12	2	4	60	1	2	51	2	14	2	NA
1	1	9	1491.35	39	3	1	2	1	23459	1	1	2	2	20	48	8	3	55	1	2	58	2	15	2	23.5
1	1	9	0	39	1	2	0	1	24002	1	1	10	16	17	40	5	10	45	1	2	52	2	14	2	NA
1	1	9	1411.11	39	1	1	2	1	40000	3	3	4	5	20	48	8	4	56	4	2	47	2	14	2	12.25
1	1	9	1315.6	40	1	1	2	1	30000	3	2	5	7	20	48	8	12	40	4	2	22	2	14	2	17
1	1	9	1739.38	39	3	1	2	1	25000	1	1	3	3	19	44	7	3	50	1	2	28	2	13	2	12
1	2	9	1664.39	44	5	2	2	1	65000	1	1	10	16	3	8	2	10	50	1	2	61	2	13	2	32
1	4	9	2209.36	39	1	1	0	1	141030	1	1	6	9	20	48	8	6	-4	4	2	65	2	15	1	45
1	1	9	2192.86	40	7	2	2	1	31000	1	1	10	16	17	40	5	10	40	1	2	23	2	14	2	15
1	4	9	1555.23	43	1	2	0	1	60000	1	1	10	15	8	21	2	10	44	1	2	50	2	15	2	26.227273
1	3	9	1056.67	42	5	2	2	1	50000	1	1	10	15	8	20	2	10	40	6	2	48	1	12	2	25
1	3	9	1875.69	40	7	2	0	1	36000	3	3	13	21	5	13	2	13	38	1	2	26	2	16	2	14.5
1	1	9	1707.66	43	3	2	2	1	22000	2	7	5	7	13	32	3	5	30	4	2	40	2	13	1	13
1	2	9	1664.39	40	7	2	0	1	50000	1	1	13	21	17	40	5	13	40	1	2	60	2	8	2	14
1	1	9	1523.89	39	5	1	2	1	29500	1	1	11	18	13	32	3	11	40	1	2	42	2	9	2	15
1	1	9	0	39	7	2	0	1	32000	2	7	11	18	13	33	3	11	28	1	1	24	2	9	1	NA
1	3	9	0	43	5	2	0	1	48000	1	1	13	21	12	30	3	13	40	1	2	48	2	11	2	NA
1	1	9	0	39	7	1	0	1	2400	4	12	10	16	10	27	2	10	40	3	2	19	2	11	2	NA
1	1	9	0	34	5	2	0	1	22276	1	1	13	21	15	37	3	12	60	4	2	45	2	6	2	NA
1	3	9	0	45	1	2	0	1	45000	2	7	10	16	10	25	2	10	20	4	2	36	2	16	1	NA
1	1	9	0	33	1	1	0	1	12000	4	9	9	14	14	35	3	10	24	4	2	35	2	7	1	NA
1	2	9	0	39	1	2	0	1	10000	4	10	10	16	11	28	3	10	40	3	2	57	2	13	2	NA
1	1	9	0	39	1	1	0	1	106000	1	1	5	7	16	38	4	5	40	2	2	44	2	13	2	NA
1	1	9	0	41	1	1	0	0	0	5	13	15	23	24	53	3	10	-4	4	2	52	2	14	1	NA
1	1	9	1425.23	39	1	1	0	1	70000	1	1	5	7	22	51	10	5	50	1	2	54	2	13	2	17
1	1	9	1395.36	40	1	1	2	1	73000	1	1	12	20	6	17	2	12	55	1	2	45	2	14	2	25.527273
1	3	9	0	43	1	1	0	1	43000	1	1	13	21	6	17	2	13	40	1	2	47	2	15	2	NA
1	1	9	0	39	5	1	0	1	40000	3	3	3	3	19	43	7	3	35	1	2	35	2	10	2	NA
1	1	9	0	40	1	2	0	1	45000	1	1	8	11	2	7	1	8	40	1	2	33	2	14	2	NA
1	4	9	1656.01	43	2	2	0	1	51000	1	1	10	15	8	21	2	10	45	1	2	46	2	16	2	21.8
1	1	9	1656.01	39	1	2	2	1	40000	1	1	10	16	16	39	4	10	40	1	2	48	2	15	2	20
1	4	9	1444.14	40	7	1	0	1	70000	1	1	6	9	20	48	8	6	40	1	2	49	2	13	2	33.65
1	1	9	0	42	7	1	0	1	38000	1	1	11	18	13	33	3	11	40	4	2	38	2	8	2	NA
1	1	9	0	41	7	1	0	1	42000	1	1	3	3	19	45	7	3	40	4	2	27	2	10	2	NA
1	1	9	2293.67	41	1	2	0	1	31000	1	1	10	16	11	28	3	10	40	4	2	51	2	13	2	16
1	1	9	2589.84	43	1	2	2	1	40000	1	1	5	7	16	38	4	5	40	4	2	32	2	15	2	9.25
1	1	9	2236.51	39	1	1	2	1	80000	1	1	6	9	20	48	8	6	40	6	2	41	2	14	2	31
1	1	9	2450.95	39	3	1	0	1	34320	1	1	5	7	13	33	3	5	40	4	2	42	2	14	2	14
1	1	9	2589.84	43	1	2	2	1	47000	1	1	10	16	16	38	4	10	40	4	2	31	2	14	2	9.25
1	1	9	2450.95	42	1	1	2	1	62000	1	1	6	8	22	50	10	6	40	4	2	43	2	14	2	26.45
1	3	9	2287.67	43	3	2	2	1	67000	1	1	10	15	1	3	1	10	60	4	2	36	2	13	2	19.866667
1	1	9	3023.11	39	7	2	2	1	5800	2	7	5	7	16	39	4	5	-4	3	1	25	2	16	1	8.5
1	1	9	2251.53	39	7	2	2	1	70542	1	1	9	14	12	31	3	9	40	6	2	65	2	16	2	11.88
1	1	9	2438.25	39	7	1	0	1	42640	1	1	5	7	16	39	4	5	40	4	2	55	2	10	2	20.5
1	1	9	2685.16	42	1	2	2	1	30000	1	1	10	16	17	40	5	10	32	4	2	34	2	9	1	15.76
1	1	9	2603.29	40	1	1	0	1	125000	1	1	3	3	19	43	7	3	45	1	2	41	2	16	2	53.422222
1	1	9	2293.67	43	5	2	2	1	45000	1	1	10	16	10	25	2	10	40	4	2	50	2	13	2	26.9
1	1	9	2916.57	43	1	1	2	1	65000	1	1	10	16	10	25	2	10	36	4	2	26	2	14	2	31
1	1	9	2299.86	43	1	2	2	1	25000	1	1	9	14	17	40	5	9	40	4	2	27	2	14	2	13
1	1	9	2589.84	46	1	2	0	1	60000	1	1	10	16	10	24	2	10	40	4	2	33	2	13	2	29.8
1	1	9	2405.36	40	7	2	2	1	11200	4	8	11	18	13	33	3	11	-4	6	2	22	2	13	1	10
1	3	9	2349.49	44	7	2	0	1	40000	1	1	10	15	8	21	2	10	40	1	2	31	2	14	2	29.8
1	1	9	2832.77	43	7	2	2	1	6002	2	7	5	7	16	39	2	10	40	1	2	24	2	2	2	8
1	1	9	0	43	1	1	0	1	90000	1	1	8	11	3	8	2	7	40	4	2	52	2	14	2	NA
1	1	9	0	42	1	1	0	1	90000	1	1	8	12	1	2	1	8	55	1	2	45	2	14	2	NA
1	1	9	0	39	1	1	0	1	45000	1	1	4	5	22	51	10	4	-4	6	2	45	2	13	1	NA
1	1	9	0	39	1	2	0	1	12000	3	4	1	1	16	39	4	5	38	4	2	47	2	13	2	NA
1	4	9	0	39	7	1	0	1	18000	1	1	10	15	8	21	2	10	-4	6	2	30	2	9	1	NA
1	1	9	0	45	1	2	0	1	433605	1	1	9	13	7	18	2	9	40	6	2	43	2	14	2	NA
1	1	9	0	39	1	1	0	1	409068	1	1	8	12	16	39	4	8	-4	6	2	56	2	14	1	NA
1	3	9	0	44	7	1	0	1	70000	1	1	10	15	17	40	5	10	40	1	2	53	2	13	2	NA
1	1	9	0	40	7	1	0	1	8000	2	7	11	17	15	37	3	11	-4	6	2	22	2	15	1	NA
1	1	9	0	43	1	2	0	1	60002	1	1	8	11	2	5	1	8	40	4	2	49	2	15	2	NA
1	3	9	0	45	7	2	0	1	50000	1	1	13	21	7	18	2	13	50	4	2	31	2	16	2	NA
1	2	9	0	39	1	1	0	1	55000	1	1	6	8	17	40	5	6	40	3	2	42	2	14	2	NA
1	1	9	0	39	1	1	0	1	15000	1	1	9	14	14	34	3	9	60	1	2	41	2	15	2	NA
1	1	9	0	42	1	2	0	1	58000	1	1	5	7	16	38	4	5	40	4	2	46	2	14	2	NA
1	1	9	0	46	7	2	0	1	73000	1	1	10	16	10	25	2	10	40	4	2	28	2	15	2	NA
1	4	9	2226.87	41	7	1	0	1	48728	1	1	6	9	20	48	8	6	40	4	2	52	2	12	2	23.05
2 | 


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/revised/R/r_stats_analysis.R:
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  1 | 
  2 | #An example analysis of a fairly simple dataset.
  3 | #The data is from the 2010 CPS March supplement. 
  4 | #(Also called the Annual Social and Economic Supplement.)
  5 | #The goal is examining the difference in hourly pay
  6 | #between males and females. The column A_ERNLWT is 
  7 | #survey weights, PTFT is part-time/full-time status, 
  8 | #educ is education level, ind is what industry the
  9 | #person works in and occ is what occupation they work in. 
 10 | #A full description of most of the variables sometimes with 
 11 | #slightly different names, can be found at
 12 | #http://www.census.gov/apsd/techdoc/cps/cpsmar10.pdf
 13 | 
 14 | 
 15 | dat<-read.table("marchCPS_2010.txt", header=T)
 16 | 
 17 | dim(dat)
 18 | 
 19 | 
 20 | ####Actual analysis code
 21 | 
 22 | #Remove everyone that doesn't have an houlry wage data
 23 | 
 24 | indhw<-which(!is.na(dat$hrwage))
 25 | hrdat<-dat[indhw,]
 26 | 
 27 | #Remove several people who just didn't fit the models (using the
 28 | #standard model checking techniques below) 
 29 | 
 30 | hrdat<-hrdat[-c(408,516,853,1198),]
 31 | 
 32 | indf<-which(hrdat$sex==2)
 33 | indm<-which(hrdat$sex==1)
 34 | 
 35 | #Make the factors actually factors
 36 | #The columns numbers correspond to GEDIV, educ, marstat, sex, occ,
 37 | #ind, race, hispanic, disabled, and PTFT
 38 | 
 39 | tmp<-rep("female", length(hrdat$sex))
 40 | tmp[hrdat$sex==1]="male"
 41 | hrdat$sex <- factor(tmp)
 42 | 
 43 | factor_indices <- c(3,5,6,17,18,20,21,23,25)
 44 | for(i in factor_indices){
 45 |     hrdat[,i] <- factor(hrdat[,i])
 46 | }
 47 | 
 48 | #With each of these models, typically do some 
 49 | #commands to look more at the models, like summary(),
 50 | #, anova for the model on its own or betwen two models to see
 51 | #how much additional explantory power you get with the added
 52 | #variables, and plots to look at residuals, qqplot, and hist of residuals
 53 | 
 54 | model1<-lm(log(hrwage)~sex, data = hrdat, weights = A_ERNLWT)
 55 | 
 56 | model2<-lm(log(hrwage)~sex+educ+age+PTFT, data=hrdat, weights=A_ERNLWT)
 57 | 
 58 | model2.5<-lm(log(hrwage)~sex+educ+age+I(age^2)+PTFT+ind+occ,
 59 |                 data=hrdat, weights=A_ERNLWT)
 60 | 
 61 | 
 62 | pdf('model_diagnostics.pdf', width=16,height=7)
 63 | par(mfrow=c(1,3))
 64 | hist(model2.5$res, breaks=40, main="Histogram of Residuals", 
 65 |         xlab="Residuals", cex.main=2, cex.lab=1.5)
 66 | plot(model2.5$fitted, model2.5$res, main="Residual vs. Fitted Values", 
 67 |         xlab="Fitted Values", ylab="Residuals",cex.main=2, cex.lab=1.5)
 68 | lines(lowess(model2.5$fitted, model2.5$res), col='red', lwd=3)
 69 | abline(h=0, col='green', lwd=3)
 70 | qqnorm(model2.5$res,cex.main=2, cex.lab=1.5)
 71 | qqline(as.vector(model2.5$res), col='red')
 72 | dev.off()
 73 | 
 74 | 
 75 | indic<-(hrdat$PMHRUSLT>0)
 76 | 
 77 | 
 78 | #Used mean replacement for all the people whose hours varied,
 79 | #where the mean of 22 was the mean over all part time workers 
 80 | #w/o missing data and using their weights
 81 | #Done just as a sanity check on the above models
 82 | 
 83 | model3<-lm(log(hrwage)~ sex+educ+PTFT+age +I(age^2)+marstat+GEDIV+race+ 
 84 |     hispanic+I(PMHRUSLT*indic+22*(1-indic))+disabled, 
 85 |     data = hrdat, weights = A_ERNLWT)
 86 | 
 87 | 
 88 | 
 89 | #These models bin the works by age group, <=30, 31-40, 41-50, >50.
 90 | #This was done to see what the difference betweens males and 
 91 | #females was in each bin. This was done as a sanity check for the
 92 | #later exploratory analysis that fit lowess curves across age to 
 93 | #males and females seperately. I wanted to make sure that the trends 
 94 | #observed were real. (Where the gap was smaller for younger workers,
 95 | #expanded for middle age workers, and then contracted again.)
 96 | 
 97 | model7.1<-lm(log(hrwage)~sex+educ+PTFT+age+I(age^2), 
 98 |         data = subset(hrdat, age<=30), weights=A_ERNLWT )
 99 | 
100 | model7.2<-lm(log(hrwage)~sex+educ+PTFT+age+I(age^2), 
101 |         data = subset(hrdat, age>30 & age<=40), weights=A_ERNLWT )
102 | 
103 | model7.3<-lm(log(hrwage)~sex+educ+PTFT+age+I(age^2), 
104 |         data = subset(hrdat, age>40 & age<=50), weights=A_ERNLWT )
105 | 
106 | model7.4<-lm(log(hrwage)~sex+educ+PTFT+age+I(age^2), 
107 |         data = subset(hrdat, age>50), weights=A_ERNLWT )
108 | 
109 | 
110 | ####
111 | 
112 | 
113 | #This analysis switches gears and focuses on occupation. 
114 | #The males and females are broken down by occupation and the
115 | #weighted mean of their hourly wage are compared.
116 | #(The weights used are again the survey weights.)
117 | #The matrix wocc stores all of that, and 
118 | #Use xtable to format tables in latex
119 | 
120 | sum_stat<-function(x){
121 | 	tmp1 <- sum(x$A_ERNLWT)
122 | 	tmp2 <- weighted.mean(x$hrwage, x$A_ERNLWT)
123 | 	c(survey.wt=tmp1, avr.hr.wage=tmp2)
124 | }
125 | 
126 | split.by <-list(hrdat$sex, hrdat$occ)
127 | wocc<-split(hrdat, split.by)
128 | 
129 | wocc <-lapply(wocc, sum_stat)
130 | wocc<-do.call(rbind, wocc)
131 | 
132 | wocc<-split(data.frame(wocc), rep(levels(hrdat$sex), 9))
133 | 
134 | #Plotting hourly wage versus age and fitting a lowess curve to it. 
135 | #Done sepereately for both males and females. Just to get a feel for 
136 | #what their salary trajectories look like. Though it's not totally
137 | #clear from the picture, the gap is smaller proportionally at younger 
138 | #ages than at older ones. (This can be seen as above by binning the ages,
139 | #or via a separate analysis by fitting an interaction term between
140 | #sex and age.)
141 | 
142 | plot(lowess(hrdat$age[indm], (hrdat$hrwage)[indm]), col='blue', ylim=c(7,25), 
143 |     xlab="Age", ylab = "Hourly Wage", main="Hourly Wage vs. Age", type='l',
144 |     cex.main=2, cex.lab=1.5, lwd=2)
145 | lines(lowess(hrdat$age[indf], (hrdat$hrwage)[indf]), col='deeppink')
146 | legend('topleft', c("Males", "Females"), cex=1.25, col=c("blue", "deeppink"),
147 |     lty=1, lwd=2)
148 | 
149 | 
150 | 
151 | 
152 | 
153 | 


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/revised/summary.txt:
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 1 | A simple comparison of R and Python (statsmodels) for doing some
 2 | exploratory data analysis and fitting of simple OLS models. 
 3 | 
 4 | See summary.txt in the folder for the original analysis first. 
 5 | This document only gives corrections/suggestions/changes
 6 | relative to the original analysis. 
 7 | 
 8 | -------------------------------------------------------------
 9 | Compared to the original .r and .py files, in these revised version:
10 | -The R code was cleaned up because I realized I didn't need to use
11 |     as.factor if I made the relevant variables into factors
12 | -The python code was cleaned up by computing the 'sub-design matrices'
13 |     associated with each factor variable before hand and stashing
14 |     them in a dictionary
15 | -Names were added to the variables in the regression by creating them
16 |     from the calls to sm.categorical and stashing them in a dictionary
17 | 
18 | Notably, the helper fucntions and stashing of the pieces of design matrices
19 | simplified the calls for model fitting, but they didn't noticeably shorten
20 | the code. They also required a small increase in complexity. (In terms of the
21 | data structures and function calls used to create the list of names and
22 | the design matrices.) 
23 | --------------------------------------------------------------
24 | 
25 | Comments:
26 | 
27 | ***Pasting without autoindent can be done in the shell IPython and not
28 | just the IPython qtconsole. The relevant commands are paste and cpaste. 
29 | I found cpaste to be more what I was looking for, but mileage
30 | may vary. 
31 | 
32 | (Though the shell IPython still has the limitation that when hitting
33 | the up arrow key you cannot recall multi-line pasted inputs as a 
34 | single block. You can do that in the IPython qtconsole.) 
35 | 
36 | ***There are some options for pretty printing a matrix. One is to adjust
37 | settings in np.set_printoptions as needed, and the other is to use 
38 | sm.iolib.SimpleTable from statsmodels. 
39 | 
40 | (In the future the addition of pandas to statsmodels should alleviate 
41 | this as well.) 
42 | 
43 | ***The user can add names to the variables in a statsmodels regression
44 | by using the xname option on a regression results object. However, the
45 | user must supply the names. In particular, this means keeping track 
46 | of the levels for a categorical variable. 
47 | 
48 | ***One can simplify the construction of design matrices by, at the
49 | beginning of the analysis, creating a dictionary that associates
50 | variable names with parts of the design matrix associate with each 
51 | variable. Then a helper function can call np.hstack to combine 
52 | these sub-pieces of the design matrix into a whole design matrix. 
53 | 
54 | A similar strategy can be used to keep track of the names associated with
55 | each variable. (In particular the names for each level for a categorical
56 | variable.) 
57 | 
58 | However, that method doesn't allow subsetting of the data. The problem 
59 | is that for some subsets of the data not all levels for categorical 
60 | variable will be present. If you used the same matrices and names as 
61 | for the full dataset you'd have columns all all 0's in the design 
62 | matrix or (if those columns were eliminated) too many names. 
63 | 
64 | Creating a function for generating names and design matrices that
65 | can take a subset of the data is more involved. (And I did not implement
66 | it.) In particular, it would have to generate both the names and the
67 | design matrix together, as well as keep track of what variables were
68 | categorical (and hence needed sm.categorical called) and which were not.
69 | 
70 | 


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