├── docs ├── Harrison_1978.pdf └── Valuation Using Hedonic Pricing Models.pdf ├── images ├── img02_model1_normalqq.png ├── img04_model2_normalqq.png ├── img05_model1_accuracy.png ├── img06_model2_accuracy.png ├── img01_model1_fitted-residuals.png └── img03_model2_fitted-residuals.png ├── .Rhistory ├── .gitignore ├── data ├── housing.names ├── predictions_model1.csv ├── predictions_model2.csv └── housing.data ├── hedonic_regression.R ├── README.md └── hedonic_R.ipynb /docs/Harrison_1978.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/buruzaemon/hedonic/HEAD/docs/Harrison_1978.pdf -------------------------------------------------------------------------------- /images/img02_model1_normalqq.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/buruzaemon/hedonic/HEAD/images/img02_model1_normalqq.png -------------------------------------------------------------------------------- /images/img04_model2_normalqq.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/buruzaemon/hedonic/HEAD/images/img04_model2_normalqq.png -------------------------------------------------------------------------------- /images/img05_model1_accuracy.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/buruzaemon/hedonic/HEAD/images/img05_model1_accuracy.png -------------------------------------------------------------------------------- /images/img06_model2_accuracy.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/buruzaemon/hedonic/HEAD/images/img06_model2_accuracy.png -------------------------------------------------------------------------------- /images/img01_model1_fitted-residuals.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/buruzaemon/hedonic/HEAD/images/img01_model1_fitted-residuals.png -------------------------------------------------------------------------------- /images/img03_model2_fitted-residuals.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/buruzaemon/hedonic/HEAD/images/img03_model2_fitted-residuals.png -------------------------------------------------------------------------------- /docs/Valuation Using Hedonic Pricing Models.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/buruzaemon/hedonic/HEAD/docs/Valuation Using Hedonic Pricing Models.pdf -------------------------------------------------------------------------------- /.Rhistory: -------------------------------------------------------------------------------- 1 | rm(list=ls()) 2 | load("F:/Area52/home/buruzaemon/dev/github/hedonic/.RData") 3 | y_hat = predict(model1, test) 4 | y 5 | y_hat 6 | df2 = data.frame(y, y_hat) 7 | names(df2) 8 | names(df2) = c("actual", "predicted") 9 | names(df2) 10 | head(df2) 11 | write.table("foo.csv", sep=",") 12 | setwd("F:/Area52/home/buruzaemon/dev/github/hedonic") 13 | write.table("foo.csv", sep=",") 14 | getwd() 15 | write.table(file.path("data", "foo.csv"), sep=",") 16 | getwd() 17 | ?write.csv2 18 | df2 19 | write.csv2(df2, file=file.path("data", "foo.csv")) 20 | write.csv(df2, file=file.path("data", "predictions.csv")) 21 | y_hat = predict(model1, test) 22 | df2 = data.frame(y, y_hat) 23 | names(df2) = c("actual", "predicted") 24 | df2 = data.frame(y_hat, y) 25 | names(df2) = c("predicted", "actual") 26 | write.csv(df2, file=file.path("data", "predictions_model1.csv")) 27 | y_hat = predict(model2, test) 28 | df2 = data.frame(y_hat, y) 29 | names(df2) = c("predicted", "actual") 30 | write.csv(df2, file=file.path("data", "predictions_model2.csv")) 31 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | env/ 12 | build/ 13 | develop-eggs/ 14 | dist/ 15 | downloads/ 16 | eggs/ 17 | .eggs/ 18 | lib/ 19 | lib64/ 20 | parts/ 21 | sdist/ 22 | var/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | 27 | # PyInstaller 28 | # Usually these files are written by a python script from a template 29 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 30 | *.manifest 31 | *.spec 32 | 33 | # Installer logs 34 | pip-log.txt 35 | pip-delete-this-directory.txt 36 | 37 | # Unit test / coverage reports 38 | htmlcov/ 39 | .tox/ 40 | .coverage 41 | .coverage.* 42 | .cache 43 | nosetests.xml 44 | coverage.xml 45 | *,cover 46 | .hypothesis/ 47 | 48 | # Translations 49 | *.mo 50 | *.pot 51 | 52 | # Django stuff: 53 | *.log 54 | local_settings.py 55 | 56 | # Flask stuff: 57 | instance/ 58 | .webassets-cache 59 | 60 | # Scrapy stuff: 61 | .scrapy 62 | 63 | # Sphinx documentation 64 | docs/_build/ 65 | 66 | # PyBuilder 67 | target/ 68 | 69 | # IPython Notebook 70 | .ipynb_checkpoints 71 | 72 | # pyenv 73 | .python-version 74 | 75 | # celery beat schedule file 76 | celerybeat-schedule 77 | 78 | # dotenv 79 | .env 80 | 81 | # virtualenv 82 | venv/ 83 | ENV/ 84 | 85 | # Spyder project settings 86 | .spyderproject 87 | 88 | # Rope project settings 89 | .ropeproject 90 | -------------------------------------------------------------------------------- /data/housing.names: -------------------------------------------------------------------------------- 1 | 1. Title: Boston Housing Data 2 | 3 | 2. Sources: 4 | (a) Origin: This dataset was taken from the StatLib library which is 5 | maintained at Carnegie Mellon University. 6 | (b) Creator: Harrison, D. and Rubinfeld, D.L. 'Hedonic prices and the 7 | demand for clean air', J. Environ. Economics & Management, 8 | vol.5, 81-102, 1978. 9 | (c) Date: July 7, 1993 10 | 11 | 3. Past Usage: 12 | - Used in Belsley, Kuh & Welsch, 'Regression diagnostics ...', Wiley, 13 | 1980. N.B. Various transformations are used in the table on 14 | pages 244-261. 15 | - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. 16 | In Proceedings on the Tenth International Conference of Machine 17 | Learning, 236-243, University of Massachusetts, Amherst. Morgan 18 | Kaufmann. 19 | 20 | 4. Relevant Information: 21 | 22 | Concerns housing values in suburbs of Boston. 23 | 24 | 5. Number of Instances: 506 25 | 26 | 6. Number of Attributes: 13 continuous attributes (including "class" 27 | attribute "MEDV"), 1 binary-valued attribute. 28 | 29 | 7. Attribute Information: 30 | 31 | 1. CRIM per capita crime rate by town 32 | 2. ZN proportion of residential land zoned for lots over 33 | 25,000 sq.ft. 34 | 3. INDUS proportion of non-retail business acres per town 35 | 4. CHAS Charles River dummy variable (= 1 if tract bounds 36 | river; 0 otherwise) 37 | 5. NOX nitric oxides concentration (parts per 10 million) 38 | 6. RM average number of rooms per dwelling 39 | 7. AGE proportion of owner-occupied units built prior to 1940 40 | 8. DIS weighted distances to five Boston employment centres 41 | 9. RAD index of accessibility to radial highways 42 | 10. TAX full-value property-tax rate per $10,000 43 | 11. PTRATIO pupil-teacher ratio by town 44 | 12. B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks 45 | by town 46 | 13. LSTAT % lower status of the population 47 | 14. MEDV Median value of owner-occupied homes in $1000's 48 | 49 | 8. Missing Attribute Values: None. 50 | 51 | 52 | 53 | -------------------------------------------------------------------------------- /hedonic_regression.R: -------------------------------------------------------------------------------- 1 | set.seed(1234) 2 | 3 | fin = file.path("data", "housing.data") 4 | df = read.table(fin, 5 | sep=",", 6 | col.names=c("CRIM", 7 | "ZN", 8 | "INDUS", 9 | "CHAS", 10 | "NOX", 11 | "RM", 12 | "AGE", 13 | "DIS", 14 | "RAD", 15 | "TAX", 16 | "PTRATIO", 17 | "B", 18 | "LSTAT", 19 | "MEDV")) 20 | 21 | str(df) 22 | 23 | df$CHAS = as.factor(df$CHAS) 24 | str(df) 25 | 26 | # shuffle the rows in our data randomly 27 | df = df[sample(nrow(df)),] 28 | train = df[c(1:400),] 29 | test = df[c(401:506),] 30 | 31 | # use all independent variables 32 | # Note the CRIM, INDUS, AGE, B appear to have little effect? 33 | model1 = lm(MEDV ~ ., data=train) 34 | summary(model1) 35 | 36 | # model1 has Adjusted R-squared of 0.7192 37 | 38 | # try another model w/out CRIM, INDUS, AGE, B 39 | model2 = lm(MEDV ~ ZN+CHAS+NOX+RM+DIS+RAD+TAX+PTRATIO+LSTAT, data=df) 40 | summary(model2) 41 | 42 | # model2 has Adjusted R-squared of 0.722 43 | # there is not much of a difference between model1 and model2 44 | 45 | library(ggfortify) 46 | #autoplot(model1, which=c(1,2)) 47 | autoplot(model1, which=c(1)) 48 | ggsave(file.path("images", "img01_model1_fitted-residuals.png")) 49 | autoplot(model1, which=c(2)) 50 | ggsave(file.path("images", "img02_model1_normalqq.png")) 51 | 52 | #autoplot(model2, which=c(1,2)) 53 | autoplot(model2, which=c(1)) 54 | ggsave(file.path("images", "img03_model2_fitted-residuals.png")) 55 | autoplot(model2, which=c(2)) 56 | ggsave(file.path("images", "img04_model2_normalqq.png")) 57 | 58 | y = test$MEDV 59 | test = subset(test, select=-c(MEDV)) 60 | 61 | # using model1, make predictions 62 | y_hat = predict(model1, test) 63 | 64 | # save to file 65 | tmp = data.frame(y_hat, y) 66 | names(tmp) = c("predicted", "actual") 67 | write.csv(tmp, file=file.path("data", "predictions_model1.csv")) 68 | 69 | # measure accuracy via Mean Squared Error 70 | sprintf("MSE for model1: %f", sum((y_hat - y)^2) / nrow(test)) 71 | 72 | # plot predicted MEDV vs actual MEDV 73 | ggplot(data.frame(y_hat, y), aes(x=y_hat, y=y)) + 74 | geom_point(color='blue') + 75 | geom_abline(color='red', linetype=2) + 76 | xlab("Predicted") + 77 | ylab("Actual") + 78 | ggtitle("Accuracy of model1") 79 | ggsave(file.path("images", "img05_model1_accuracy.png")) 80 | 81 | # and now do the same for model2 82 | y_hat = predict(model2, test) 83 | 84 | # save to file 85 | tmp = data.frame(y_hat, y) 86 | names(tmp) = c("predicted", "actual") 87 | write.csv(tmp, file=file.path("data", "predictions_model2.csv")) 88 | 89 | 90 | # measure accuracy via Mean Squared Error 91 | sprintf("MSE for model2: %f", sum((y_hat - y)^2) / nrow(test)) 92 | 93 | # plot predicted MEDV vs actual MEDV 94 | ggplot(data.frame(y_hat, y), aes(x=y_hat, y=y)) + 95 | geom_point(color='blue') + 96 | geom_abline(color='red', linetype=2) + 97 | xlab("Predicted") + 98 | ylab("Actual") + 99 | ggtitle("Accuracy of model2") 100 | ggsave(file.path("images", "img06_model2_accuracy.png")) 101 | -------------------------------------------------------------------------------- /data/predictions_model1.csv: -------------------------------------------------------------------------------- 1 | "","predicted","actual" 2 | "148",8.61611695308515,14.6 3 | "491",3.66037262387137,8.1 4 | "351",20.0506097397202,22.9 5 | "194",32.3856174781222,31.1 6 | "310",23.8423862789575,20.3 7 | "483",28.231681927653,25 8 | "76",24.2846831694706,21.4 9 | "10",18.6184198835453,18.9 10 | "451",16.0050034226839,13.4 11 | "268",39.5182233357837,50 12 | "75",26.2477303109193,24.1 13 | "335",21.5763472665654,20.7 14 | "425",15.6419293469677,11.7 15 | "486",22.3857183263039,21.2 16 | "324",19.7446278507109,18.5 17 | "475",16.9616948423274,13.8 18 | "47",21.0084218179237,20 19 | "167",36.2038179289653,50 20 | "394",20.1047177029505,13.8 21 | "135",13.2356026239652,15.6 22 | "57",24.8091320600704,24.7 23 | "230",31.7920394274516,31.5 24 | "183",33.3182994555384,37.9 25 | "285",31.3407087954707,32.2 26 | "279",30.7687799879291,29.1 27 | "331",21.8166196865023,19.8 28 | "169",26.5563997522611,23.8 29 | "173",23.4087960664281,23.1 30 | "469",17.4478717630259,19.1 31 | "477",20.0281376122367,16.7 32 | "51",21.5467116894015,19.7 33 | "102",25.1476937115817,26.5 34 | "446",11.4725660856691,11.8 35 | "254",28.5991949601374,42.8 36 | "276",33.9625478920035,32 37 | "90",30.3763750086933,28.7 38 | "222",23.478042860301,21.7 39 | "440",13.0441695054425,12.8 40 | "311",20.0127203765132,16.1 41 | "371",34.4217849113662,50 42 | "128",15.1755722463561,16.2 43 | "264",33.4854030150505,31 44 | "151",20.4921088170558,21.5 45 | "275",36.4913668810071,32.4 46 | "263",39.3171520075241,48.8 47 | "104",20.0798906397549,19.3 48 | "436",12.6889667840061,13.4 49 | "94",29.915757039808,25 50 | 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"239",28.8347453538659,23.7 82 | "281",38.0022180826281,45.4 83 | "414",13.1686873279102,16.3 84 | "49",8.78844016812406,14.4 85 | "120",21.1796309395968,19.3 86 | "441",12.8693821619121,10.5 87 | "498",19.2532593736735,18.3 88 | "447",17.1886967898977,14.9 89 | "405",8.27416193854372,8.5 90 | "458",13.1660405368488,13.5 91 | "290",27.0604728689276,24.8 92 | "176",31.07778933786,29.4 93 | "234",35.8230824317155,48.3 94 | "244",27.9364457333167,23.7 95 | "488",21.9092368858081,20.6 96 | "165",25.2459223832324,22.7 97 | "416",9.18621209504473,7.2 98 | "434",16.7654751109677,14.3 99 | "98",34.4814750188106,38.7 100 | "105",21.2302939139235,20.1 101 | "209",23.7062136464538,24.4 102 | "72",22.4994429721243,21.7 103 | "150",14.688832256507,15.4 104 | "305",32.8380776471536,36.1 105 | "294",26.6202411392809,23.9 106 | "170",26.6955591631494,22.3 107 | "297",27.543758308388,27.1 108 | -------------------------------------------------------------------------------- /data/predictions_model2.csv: -------------------------------------------------------------------------------- 1 | "","predicted","actual" 2 | "148",7.46127347906191,14.6 3 | "491",3.18198935836699,8.1 4 | "351",21.0511699865649,22.9 5 | "194",32.3530612475934,31.1 6 | "310",23.5431101906283,20.3 7 | "483",27.3196114660909,25 8 | "76",23.9611718051757,21.4 9 | "10",18.4542919256751,18.9 10 | "451",18.5498751571069,13.4 11 | "268",40.4996544948306,50 12 | "75",25.5230192524257,24.1 13 | "335",21.8568009004198,20.7 14 | "425",16.9578373231825,11.7 15 | "486",20.7683559880644,21.2 16 | "324",19.4232013834534,18.5 17 | "475",15.3554668782437,13.8 18 | "47",20.3332218755342,20 19 | "167",37.2703567041496,50 20 | "394",18.922643806556,13.8 21 | "135",14.043269120362,15.6 22 | "57",25.0854161242092,24.7 23 | "230",31.3117091757144,31.5 24 | "183",34.1044154298464,37.9 25 | "285",31.5227754392489,32.2 26 | "279",30.223059343075,29.1 27 | "331",22.0348346464198,19.8 28 | "169",26.9695855176744,23.8 29 | "173",22.6137323033325,23.1 30 | "469",16.5854604673487,19.1 31 | "477",18.6024110219729,16.7 32 | "51",21.0594840056127,19.7 33 | "102",25.5959894012601,26.5 34 | "446",13.7098534886014,11.8 35 | "254",29.9323928170531,42.8 36 | "276",33.8485982172564,32 37 | "90",31.1382091773851,28.7 38 | "222",23.1260209144552,21.7 39 | "440",11.519724157186,12.8 40 | "311",19.1486586548686,16.1 41 | "371",34.0110008723989,50 42 | "128",14.6775191699596,16.2 43 | "264",33.9557056411036,31 44 | "151",20.5739633158771,21.5 45 | "275",36.5136271172712,32.4 46 | "263",40.6964134171343,48.8 47 | "104",20.084843265199,19.3 48 | "436",14.551525811117,13.4 49 | "94",28.9352724643026,25 50 | "392",15.6595999736483,23.2 51 | "2",25.1721491993632,21.6 52 | "406",13.048781747391,5 53 | "43",25.6788743116698,25.3 54 | "467",15.9969160519984,19 55 | "442",15.9825442466241,17.1 56 | "492",13.1163520696445,13.6 57 | "35",14.7453167238882,13.5 58 | "13",20.4728967199921,21.7 59 | "242",23.3341095742077,20.1 60 | "28",15.337704402429,14.8 61 | "381",21.5247788059777,10.4 62 | "18",16.9174881383271,17.5 63 | "280",35.2334441578019,35.1 64 | "46",22.2278137990817,19.3 65 | "216",24.4928208683665,25 66 | "296",28.4235441618467,28.6 67 | "42",28.4585265368053,26.6 68 | "172",24.3964070877684,19.1 69 | "397",17.4407253525695,12.5 70 | "24",13.4848014396153,14.5 71 | "348",25.2631985180449,23.1 72 | "177",25.5389542243064,23.2 73 | "409",12.4547062670478,17.2 74 | "410",20.8267944031718,27.5 75 | "214",25.2573612613487,28.1 76 | "161",33.4516130836822,27 77 | "429",15.2864381732934,11 78 | "200",30.1350997306011,34.9 79 | "427",18.9067457330541,10.2 80 | "330",24.6912313986279,22.6 81 | "239",28.4620760886449,23.7 82 | "281",38.7317396234086,45.4 83 | "414",14.2105550017004,16.3 84 | "49",8.16445670227762,14.4 85 | "120",20.4340564693729,19.3 86 | "441",12.5997422050474,10.5 87 | "498",18.7379975463858,18.3 88 | "447",16.7331433506732,14.9 89 | "405",9.68166291548679,8.5 90 | "458",15.1832416175018,13.5 91 | "290",26.7498790305022,24.8 92 | "176",30.852130121362,29.4 93 | "234",37.0094300222606,48.3 94 | "244",27.5991463380754,23.7 95 | "488",19.9684934043052,20.6 96 | "165",24.4781284460911,22.7 97 | "416",11.966426665492,7.2 98 | "434",18.1111194550799,14.3 99 | "98",36.0488370854698,38.7 100 | "105",21.2594049777533,20.1 101 | "209",23.6987586643032,24.4 102 | "72",21.9491488442343,21.7 103 | "150",14.5178718891646,15.4 104 | "305",32.8894506160985,36.1 105 | "294",25.7110186396086,23.9 106 | "170",26.9153403404128,22.3 107 | "297",27.2996155808583,27.1 108 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Hedonic Regression 2 | Quick example of using linear regression for real estate valuation for homes 3 | in the Boston suburbs. 4 | 5 | 6 | ## Summary 7 | 8 | R's [`lm`](https://stat.ethz.ch/R-manual/R-devel/library/stats/html/lm.html) 9 | function for linear model fitting was used for a Hedonic regression to predict 10 | with fair accuracy the median value of owner-occupied homes. While there was 11 | some doubt as to the effect of independent variables `CRIM,` `INDUS`, `AGE` 12 | and `B`, a model including all the independent variables available seemed to 13 | work best at prediction. 14 | 15 | ### Cleaning the data 16 | 17 | The original `data/housing.data` had columns delimited by varying amounts of 18 | whitespace, so vim was used to clean up the file and change the delimiters to 19 | commas. 20 | 21 | ### Fitting a linear model for Hedonic Regression 22 | 23 | The basic model used was 24 | 25 | model1 = lm(MEDV ~ ., data=...) 26 | 27 | This specifies the `MEDV` column as the dependent variable, and treats all 28 | other columns as independent variables. Note that the `CHAS` column was 29 | converted from an integer to a factor. 30 | 31 | As the model using all independent variables was reporting questionable 32 | levels of effect of independent variables `CRIM,` `INDUS`, `AGE` and `B`, 33 | a second model not including those variables was also created. 34 | 35 | Plots for fitted-values vs. residuals, as well as those for theoretical 36 | quantiles vs. standardized residuals seemed to show very little difference 37 | between the two models. However, the first model using all thirteen of the 38 | available independent variables had a lower Adjusted R-squared value of 39 | 0.7192, compared to the second model using nine independent variables 40 | (Adjusted R-squared of 0.722). 41 | 42 | #### Model 1 details 43 | 44 | 45 | 46 | 47 |
49 |
50 | 51 | #### Model 2 details 52 | 53 | 54 | 55 | 56 |
58 |
59 | 60 | ### Verifying the models 61 | 62 | The data was randomly shuffled and then split into a training set with 63 | 400 rows, with the remaining 106 rows used for validation. 64 | 65 | [Mean squared error](https://en.wikipedia.org/wiki/Mean_squared_error) 66 | was used to measure the differences in accuracy between our two models, 67 | with a lower score implying greater accuracy. 68 | 69 | The results: 70 | * model 1: 18.756444 71 | * model 2: 18.896384 72 | 73 | Predictions using both models were output to file under 74 | `data/predictions_model*.csv` 75 | 76 | Plots of the predicted `MEDV` vs. the actual `MEDV` values using both models 77 | were created. The accuracy of the models is visually interpreted as the 78 | distance of a point from the diagonal line y=x. 79 | 80 | #### Model 1 accuracy using all 13 variables 81 | ![](https://github.com/buruzaemon/hedonic/blob/master/images/img05_model1_accuracy.png?raw=true "Model 1 accuracy") 82 | 83 | #### Model 2 accuracy using 9 variables 84 | ![](https://github.com/buruzaemon/hedonic/blob/master/images/img06_model2_accuracy.png?raw=true "Model 2 accuracy") 85 | 86 | Since Model 1 has a lower mean squared error even when including all thirteen 87 | of the independent variables available in the given dataset, we suggest that 88 | Model 1 be used. 89 | 90 | As the points in this plot seem to lie fairly close to the diagonal, 91 | we conclude that the linear model works very well for Hedonic regression. 92 | R's `lm` and `predict` functions are well-documented, easy-to-use, 93 | and the results are reasonably simple to interpret and comprehend. 94 | 95 | 96 | ## Data 97 | 98 | * Data was obtained on 2016年 9月 22日 木曜日 00:32:22 99 | * URL: https://archive.ics.uci.edu/ml/datasets/Housing 100 | * Number of Instances: 506 101 | * Number of Attributes: 13 continuous attributes (including "class" attribute "MEDV"), 1 binary-valued attribute 102 | * Attribute Information: 103 | * `CRIM` ... per capita crime rate by town 104 | * `ZN` ... proportion of residential land zoned for lots over 25,000 sq.ft. 105 | * `INDUS` ... proportion of non-retail business acres per town 106 | * `CHAS` ... Charles River dummy variable (= 1 if tract bounds river; 0 otherwise) 107 | * `NOX` ... nitric oxides concentration (parts per 10 million) 108 | * `RM` ... average number of rooms per dwelling 109 | * `AGE` ... proportion of owner-occupied units built prior to 1940 110 | * `DIS` ... weighted distances to five Boston employment centres 111 | * `RAD` ... index of accessibility to radial highways 112 | * `TAX` ... full-value property-tax rate per $10,000 113 | * `PTRATIO` ... pupil-teacher ratio by town 114 | * `B` ... 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town 115 | * `LSTAT` ... % lower status of the population 116 | * `MEDV` ... Median value of owner-occupied homes in $1000's 117 | * No missing attributes in the data were found 118 | 119 | 120 | ## References 121 | The following references were used in this study, and are also stored under `docs`. 122 | * [Valuation Using Hedonic Pricing Models](http://scholarship.sha.cornell.edu/cgi/viewcontent.cgi?article=1058&context=crer), Monson, 2009 123 | * [Hedonic Housing Prices and the Demand for Clean Air](http://www.colorado.edu/ibs/crs/workshops/R_1-11-2012/root/Harrison_1978.pdf), Harrison & Rubinfeld, 1978 124 | -------------------------------------------------------------------------------- /hedonic_R.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": false 8 | }, 9 | "outputs": [ 10 | { 11 | "name": "stdout", 12 | "output_type": "stream", 13 | "text": [ 14 | "'data.frame':\t506 obs. of 14 variables:\n", 15 | " $ CRIM : num 0.00632 0.02731 0.02729 0.03237 0.06905 ...\n", 16 | " $ ZN : num 18 0 0 0 0 0 12.5 12.5 12.5 12.5 ...\n", 17 | " $ INDUS : num 2.31 7.07 7.07 2.18 2.18 2.18 7.87 7.87 7.87 7.87 ...\n", 18 | " $ CHAS : Factor w/ 2 levels \"0\",\"1\": 1 1 1 1 1 1 1 1 1 1 ...\n", 19 | " $ NOX : num 0.538 0.469 0.469 0.458 0.458 0.458 0.524 0.524 0.524 0.524 ...\n", 20 | " $ RM : num 6.58 6.42 7.18 7 7.15 ...\n", 21 | " $ AGE : num 65.2 78.9 61.1 45.8 54.2 58.7 66.6 96.1 100 85.9 ...\n", 22 | " $ DIS : num 4.09 4.97 4.97 6.06 6.06 ...\n", 23 | " $ RAD : int 1 2 2 3 3 3 5 5 5 5 ...\n", 24 | " $ TAX : num 296 242 242 222 222 222 311 311 311 311 ...\n", 25 | " $ PTRATIO: num 15.3 17.8 17.8 18.7 18.7 18.7 15.2 15.2 15.2 15.2 ...\n", 26 | " $ B : num 397 397 393 395 397 ...\n", 27 | " $ LSTAT : num 4.98 9.14 4.03 2.94 5.33 ...\n", 28 | " $ MEDV : num 24 21.6 34.7 33.4 36.2 28.7 22.9 27.1 16.5 18.9 ...\n" 29 | ] 30 | } 31 | ], 32 | "source": [ 33 | "set.seed(1234)\n", 34 | "\n", 35 | "fin = file.path(\"data\", \"housing.data\")\n", 36 | "df = read.table(fin, \n", 37 | " sep=\",\", \n", 38 | " col.names=c(\"CRIM\", \n", 39 | " \"ZN\", \n", 40 | " \"INDUS\", \n", 41 | " \"CHAS\", \n", 42 | " \"NOX\", \n", 43 | " \"RM\", \n", 44 | " \"AGE\", \n", 45 | " \"DIS\", \n", 46 | " \"RAD\", \n", 47 | " \"TAX\", \n", 48 | " \"PTRATIO\", \n", 49 | " \"B\",\n", 50 | " \"LSTAT\",\n", 51 | " \"MEDV\"))\n", 52 | "\n", 53 | "#str(df)\n", 54 | "\n", 55 | "df$CHAS = as.factor(df$CHAS)\n", 56 | "str(df)" 57 | ] 58 | }, 59 | { 60 | "cell_type": "code", 61 | "execution_count": 2, 62 | "metadata": { 63 | "collapsed": true 64 | }, 65 | "outputs": [], 66 | "source": [ 67 | "# shuffle the rows in our data randomly\n", 68 | "df = df[sample(nrow(df)),]\n", 69 | "train = df[c(1:400),]\n", 70 | "test = df[c(401:506),]" 71 | ] 72 | }, 73 | { 74 | "cell_type": "code", 75 | "execution_count": 3, 76 | "metadata": { 77 | "collapsed": false 78 | }, 79 | "outputs": [ 80 | { 81 | "data": { 82 | "text/plain": [ 83 | "\n", 84 | "Call:\n", 85 | "lm(formula = MEDV ~ ., data = train)\n", 86 | "\n", 87 | "Residuals:\n", 88 | " Min 1Q Median 3Q Max \n", 89 | "-13.6243 -2.9500 -0.5499 1.8155 24.8050 \n", 90 | "\n", 91 | "Coefficients:\n", 92 | " Estimate Std. Error t value Pr(>|t|) \n", 93 | "(Intercept) 45.471477 6.057729 7.506 4.24e-13 ***\n", 94 | "CRIM -0.083687 0.050065 -1.672 0.09542 . \n", 95 | "ZN 0.047491 0.015897 2.987 0.00299 ** \n", 96 | "INDUS 0.039260 0.071243 0.551 0.58191 \n", 97 | "CHAS1 2.747515 0.969562 2.834 0.00484 ** \n", 98 | "NOX -19.703295 4.463396 -4.414 1.32e-05 ***\n", 99 | "RM 2.946321 0.481168 6.123 2.26e-09 ***\n", 100 | "AGE -0.007292 0.015794 -0.462 0.64457 \n", 101 | "DIS -1.577449 0.226999 -6.949 1.57e-11 ***\n", 102 | "RAD 0.324745 0.079210 4.100 5.04e-05 ***\n", 103 | "TAX -0.013243 0.004346 -3.047 0.00247 ** \n", 104 | "PTRATIO -1.001927 0.154031 -6.505 2.42e-10 ***\n", 105 | "B 0.008531 0.003357 2.541 0.01144 * \n", 106 | "LSTAT -0.564716 0.057058 -9.897 < 2e-16 ***\n", 107 | "---\n", 108 | "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", 109 | "\n", 110 | "Residual standard error: 4.894 on 386 degrees of freedom\n", 111 | "Multiple R-squared: 0.7192,\tAdjusted R-squared: 0.7098 \n", 112 | "F-statistic: 76.07 on 13 and 386 DF, p-value: < 2.2e-16\n" 113 | ] 114 | }, 115 | "metadata": {}, 116 | "output_type": "display_data" 117 | } 118 | ], 119 | "source": [ 120 | "# use all independent variables\n", 121 | "# Note the CRIM, INDUS, AGE, B appear to have little effect?\n", 122 | "model1 = lm(MEDV ~ ., data=train)\n", 123 | "summary(model1)" 124 | ] 125 | }, 126 | { 127 | "cell_type": "code", 128 | "execution_count": 4, 129 | "metadata": { 130 | "collapsed": false 131 | }, 132 | "outputs": [ 133 | { 134 | "data": { 135 | "text/plain": [ 136 | "\n", 137 | "Call:\n", 138 | "lm(formula = MEDV ~ ZN + CHAS + NOX + RM + DIS + RAD + TAX + \n", 139 | " PTRATIO + LSTAT, data = train)\n", 140 | "\n", 141 | "Residuals:\n", 142 | " Min 1Q Median 3Q Max \n", 143 | "-12.9872 -2.8718 -0.6543 2.0191 25.1688 \n", 144 | "\n", 145 | "Coefficients:\n", 146 | " Estimate Std. Error t value Pr(>|t|) \n", 147 | "(Intercept) 48.968022 5.774913 8.479 4.73e-16 ***\n", 148 | "ZN 0.045166 0.015754 2.867 0.00437 ** \n", 149 | "CHAS1 3.057863 0.972225 3.145 0.00179 ** \n", 150 | "NOX -19.810398 4.128082 -4.799 2.28e-06 ***\n", 151 | "RM 2.830447 0.474357 5.967 5.43e-09 ***\n", 152 | "DIS -1.542903 0.212315 -7.267 2.02e-12 ***\n", 153 | "RAD 0.250940 0.072908 3.442 0.00064 ***\n", 154 | "TAX -0.012542 0.004055 -3.093 0.00213 ** \n", 155 | "PTRATIO -0.953186 0.152292 -6.259 1.02e-09 ***\n", 156 | "LSTAT -0.613036 0.052991 -11.569 < 2e-16 ***\n", 157 | "---\n", 158 | "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n", 159 | "\n", 160 | "Residual standard error: 4.952 on 390 degrees of freedom\n", 161 | "Multiple R-squared: 0.7095,\tAdjusted R-squared: 0.7028 \n", 162 | "F-statistic: 105.8 on 9 and 390 DF, p-value: < 2.2e-16\n" 163 | ] 164 | }, 165 | "metadata": {}, 166 | "output_type": "display_data" 167 | } 168 | ], 169 | "source": [ 170 | "# try another model w/out CRIM, INDUS, AGE, B\n", 171 | "model2 = lm(MEDV ~ ZN+CHAS+NOX+RM+DIS+RAD+TAX+PTRATIO+LSTAT, data=train)\n", 172 | "summary(model2)" 173 | ] 174 | }, 175 | { 176 | "cell_type": "code", 177 | "execution_count": 5, 178 | "metadata": { 179 | "collapsed": true 180 | }, 181 | "outputs": [], 182 | "source": [ 183 | "model1.res = resid(model1)\n", 184 | "model2.res = resid(model2)" 185 | ] 186 | }, 187 | { 188 | "cell_type": "code", 189 | "execution_count": 6, 190 | "metadata": { 191 | "collapsed": false 192 | }, 193 | "outputs": [ 194 | { 195 | "data": { 196 | "image/png": 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BcIKRoYZ9QWTvvFlxmkLMTygm/GlDaq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\noaDTWt/IEZVslr7nzjVDm0IhBR96UxTiRu86xLxxQI+/LKTgoKZ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197 | "text/plain": [ 198 | "plot without title" 199 | ] 200 | }, 201 | "metadata": {}, 202 | "output_type": "display_data" 203 | } 204 | ], 205 | "source": [ 206 | "par(mfrow=c(2,1))\n", 207 | "plot(train$MEDV, model1.res)\n", 208 | "abline(a=0, b=0, col=\"red\")\n", 209 | "plot(train$MEDV, model2.res)\n", 210 | "abline(a=0, b=0, col=\"blue\")" 211 | ] 212 | }, 213 | { 214 | "cell_type": "code", 215 | "execution_count": null, 216 | "metadata": { 217 | "collapsed": true 218 | }, 219 | "outputs": [], 220 | "source": [] 221 | } 222 | ], 223 | "metadata": { 224 | "kernelspec": { 225 | "display_name": "R", 226 | "language": "R", 227 | "name": "ir" 228 | }, 229 | "language_info": { 230 | "codemirror_mode": "r", 231 | "file_extension": ".r", 232 | "mimetype": "text/x-r-source", 233 | "name": "R", 234 | "pygments_lexer": "r", 235 | "version": "3.3.1" 236 | } 237 | }, 238 | "nbformat": 4, 239 | "nbformat_minor": 0 240 | } 241 | -------------------------------------------------------------------------------- /data/housing.data: -------------------------------------------------------------------------------- 1 | 0.00632,18.00,2.310,0,0.5380,6.5750,65.20,4.0900,1,296.0,15.30,396.90,4.98,24.00 2 | 0.02731,0.00,7.070,0,0.4690,6.4210,78.90,4.9671,2,242.0,17.80,396.90,9.14,21.60 3 | 0.02729,0.00,7.070,0,0.4690,7.1850,61.10,4.9671,2,242.0,17.80,392.83,4.03,34.70 4 | 0.03237,0.00,2.180,0,0.4580,6.9980,45.80,6.0622,3,222.0,18.70,394.63,2.94,33.40 5 | 0.06905,0.00,2.180,0,0.4580,7.1470,54.20,6.0622,3,222.0,18.70,396.90,5.33,36.20 6 | 0.02985,0.00,2.180,0,0.4580,6.4300,58.70,6.0622,3,222.0,18.70,394.12,5.21,28.70 7 | 0.08829,12.50,7.870,0,0.5240,6.0120,66.60,5.5605,5,311.0,15.20,395.60,12.43,22.90 8 | 0.14455,12.50,7.870,0,0.5240,6.1720,96.10,5.9505,5,311.0,15.20,396.90,19.15,27.10 9 | 0.21124,12.50,7.870,0,0.5240,5.6310,100.00,6.0821,5,311.0,15.20,386.63,29.93,16.50 10 | 0.17004,12.50,7.870,0,0.5240,6.0040,85.90,6.5921,5,311.0,15.20,386.71,17.10,18.90 11 | 0.22489,12.50,7.870,0,0.5240,6.3770,94.30,6.3467,5,311.0,15.20,392.52,20.45,15.00 12 | 0.11747,12.50,7.870,0,0.5240,6.0090,82.90,6.2267,5,311.0,15.20,396.90,13.27,18.90 13 | 0.09378,12.50,7.870,0,0.5240,5.8890,39.00,5.4509,5,311.0,15.20,390.50,15.71,21.70 14 | 0.62976,0.00,8.140,0,0.5380,5.9490,61.80,4.7075,4,307.0,21.00,396.90,8.26,20.40 15 | 0.63796,0.00,8.140,0,0.5380,6.0960,84.50,4.4619,4,307.0,21.00,380.02,10.26,18.20 16 | 0.62739,0.00,8.140,0,0.5380,5.8340,56.50,4.4986,4,307.0,21.00,395.62,8.47,19.90 17 | 1.05393,0.00,8.140,0,0.5380,5.9350,29.30,4.4986,4,307.0,21.00,386.85,6.58,23.10 18 | 0.78420,0.00,8.140,0,0.5380,5.9900,81.70,4.2579,4,307.0,21.00,386.75,14.67,17.50 19 | 0.80271,0.00,8.140,0,0.5380,5.4560,36.60,3.7965,4,307.0,21.00,288.99,11.69,20.20 20 | 0.72580,0.00,8.140,0,0.5380,5.7270,69.50,3.7965,4,307.0,21.00,390.95,11.28,18.20 21 | 1.25179,0.00,8.140,0,0.5380,5.5700,98.10,3.7979,4,307.0,21.00,376.57,21.02,13.60 22 | 0.85204,0.00,8.140,0,0.5380,5.9650,89.20,4.0123,4,307.0,21.00,392.53,13.83,19.60 23 | 1.23247,0.00,8.140,0,0.5380,6.1420,91.70,3.9769,4,307.0,21.00,396.90,18.72,15.20 24 | 0.98843,0.00,8.140,0,0.5380,5.8130,100.00,4.0952,4,307.0,21.00,394.54,19.88,14.50 25 | 0.75026,0.00,8.140,0,0.5380,5.9240,94.10,4.3996,4,307.0,21.00,394.33,16.30,15.60 26 | 0.84054,0.00,8.140,0,0.5380,5.5990,85.70,4.4546,4,307.0,21.00,303.42,16.51,13.90 27 | 0.67191,0.00,8.140,0,0.5380,5.8130,90.30,4.6820,4,307.0,21.00,376.88,14.81,16.60 28 | 0.95577,0.00,8.140,0,0.5380,6.0470,88.80,4.4534,4,307.0,21.00,306.38,17.28,14.80 29 | 0.77299,0.00,8.140,0,0.5380,6.4950,94.40,4.4547,4,307.0,21.00,387.94,12.80,18.40 30 | 1.00245,0.00,8.140,0,0.5380,6.6740,87.30,4.2390,4,307.0,21.00,380.23,11.98,21.00 31 | 1.13081,0.00,8.140,0,0.5380,5.7130,94.10,4.2330,4,307.0,21.00,360.17,22.60,12.70 32 | 1.35472,0.00,8.140,0,0.5380,6.0720,100.00,4.1750,4,307.0,21.00,376.73,13.04,14.50 33 | 1.38799,0.00,8.140,0,0.5380,5.9500,82.00,3.9900,4,307.0,21.00,232.60,27.71,13.20 34 | 1.15172,0.00,8.140,0,0.5380,5.7010,95.00,3.7872,4,307.0,21.00,358.77,18.35,13.10 35 | 1.61282,0.00,8.140,0,0.5380,6.0960,96.90,3.7598,4,307.0,21.00,248.31,20.34,13.50 36 | 0.06417,0.00,5.960,0,0.4990,5.9330,68.20,3.3603,5,279.0,19.20,396.90,9.68,18.90 37 | 0.09744,0.00,5.960,0,0.4990,5.8410,61.40,3.3779,5,279.0,19.20,377.56,11.41,20.00 38 | 0.08014,0.00,5.960,0,0.4990,5.8500,41.50,3.9342,5,279.0,19.20,396.90,8.77,21.00 39 | 0.17505,0.00,5.960,0,0.4990,5.9660,30.20,3.8473,5,279.0,19.20,393.43,10.13,24.70 40 | 0.02763,75.00,2.950,0,0.4280,6.5950,21.80,5.4011,3,252.0,18.30,395.63,4.32,30.80 41 | 0.03359,75.00,2.950,0,0.4280,7.0240,15.80,5.4011,3,252.0,18.30,395.62,1.98,34.90 42 | 0.12744,0.00,6.910,0,0.4480,6.7700,2.90,5.7209,3,233.0,17.90,385.41,4.84,26.60 43 | 0.14150,0.00,6.910,0,0.4480,6.1690,6.60,5.7209,3,233.0,17.90,383.37,5.81,25.30 44 | 0.15936,0.00,6.910,0,0.4480,6.2110,6.50,5.7209,3,233.0,17.90,394.46,7.44,24.70 45 | 0.12269,0.00,6.910,0,0.4480,6.0690,40.00,5.7209,3,233.0,17.90,389.39,9.55,21.20 46 | 0.17142,0.00,6.910,0,0.4480,5.6820,33.80,5.1004,3,233.0,17.90,396.90,10.21,19.30 47 | 0.18836,0.00,6.910,0,0.4480,5.7860,33.30,5.1004,3,233.0,17.90,396.90,14.15,20.00 48 | 0.22927,0.00,6.910,0,0.4480,6.0300,85.50,5.6894,3,233.0,17.90,392.74,18.80,16.60 49 | 0.25387,0.00,6.910,0,0.4480,5.3990,95.30,5.8700,3,233.0,17.90,396.90,30.81,14.40 50 | 0.21977,0.00,6.910,0,0.4480,5.6020,62.00,6.0877,3,233.0,17.90,396.90,16.20,19.40 51 | 0.08873,21.00,5.640,0,0.4390,5.9630,45.70,6.8147,4,243.0,16.80,395.56,13.45,19.70 52 | 0.04337,21.00,5.640,0,0.4390,6.1150,63.00,6.8147,4,243.0,16.80,393.97,9.43,20.50 53 | 0.05360,21.00,5.640,0,0.4390,6.5110,21.10,6.8147,4,243.0,16.80,396.90,5.28,25.00 54 | 0.04981,21.00,5.640,0,0.4390,5.9980,21.40,6.8147,4,243.0,16.80,396.90,8.43,23.40 55 | 0.01360,75.00,4.000,0,0.4100,5.8880,47.60,7.3197,3,469.0,21.10,396.90,14.80,18.90 56 | 0.01311,90.00,1.220,0,0.4030,7.2490,21.90,8.6966,5,226.0,17.90,395.93,4.81,35.40 57 | 0.02055,85.00,0.740,0,0.4100,6.3830,35.70,9.1876,2,313.0,17.30,396.90,5.77,24.70 58 | 0.01432,100.00,1.320,0,0.4110,6.8160,40.50,8.3248,5,256.0,15.10,392.90,3.95,31.60 59 | 0.15445,25.00,5.130,0,0.4530,6.1450,29.20,7.8148,8,284.0,19.70,390.68,6.86,23.30 60 | 0.10328,25.00,5.130,0,0.4530,5.9270,47.20,6.9320,8,284.0,19.70,396.90,9.22,19.60 61 | 0.14932,25.00,5.130,0,0.4530,5.7410,66.20,7.2254,8,284.0,19.70,395.11,13.15,18.70 62 | 0.17171,25.00,5.130,0,0.4530,5.9660,93.40,6.8185,8,284.0,19.70,378.08,14.44,16.00 63 | 0.11027,25.00,5.130,0,0.4530,6.4560,67.80,7.2255,8,284.0,19.70,396.90,6.73,22.20 64 | 0.12650,25.00,5.130,0,0.4530,6.7620,43.40,7.9809,8,284.0,19.70,395.58,9.50,25.00 65 | 0.01951,17.50,1.380,0,0.4161,7.1040,59.50,9.2229,3,216.0,18.60,393.24,8.05,33.00 66 | 0.03584,80.00,3.370,0,0.3980,6.2900,17.80,6.6115,4,337.0,16.10,396.90,4.67,23.50 67 | 0.04379,80.00,3.370,0,0.3980,5.7870,31.10,6.6115,4,337.0,16.10,396.90,10.24,19.40 68 | 0.05789,12.50,6.070,0,0.4090,5.8780,21.40,6.4980,4,345.0,18.90,396.21,8.10,22.00 69 | 0.13554,12.50,6.070,0,0.4090,5.5940,36.80,6.4980,4,345.0,18.90,396.90,13.09,17.40 70 | 0.12816,12.50,6.070,0,0.4090,5.8850,33.00,6.4980,4,345.0,18.90,396.90,8.79,20.90 71 | 0.08826,0.00,10.810,0,0.4130,6.4170,6.60,5.2873,4,305.0,19.20,383.73,6.72,24.20 72 | 0.15876,0.00,10.810,0,0.4130,5.9610,17.50,5.2873,4,305.0,19.20,376.94,9.88,21.70 73 | 0.09164,0.00,10.810,0,0.4130,6.0650,7.80,5.2873,4,305.0,19.20,390.91,5.52,22.80 74 | 0.19539,0.00,10.810,0,0.4130,6.2450,6.20,5.2873,4,305.0,19.20,377.17,7.54,23.40 75 | 0.07896,0.00,12.830,0,0.4370,6.2730,6.00,4.2515,5,398.0,18.70,394.92,6.78,24.10 76 | 0.09512,0.00,12.830,0,0.4370,6.2860,45.00,4.5026,5,398.0,18.70,383.23,8.94,21.40 77 | 0.10153,0.00,12.830,0,0.4370,6.2790,74.50,4.0522,5,398.0,18.70,373.66,11.97,20.00 78 | 0.08707,0.00,12.830,0,0.4370,6.1400,45.80,4.0905,5,398.0,18.70,386.96,10.27,20.80 79 | 0.05646,0.00,12.830,0,0.4370,6.2320,53.70,5.0141,5,398.0,18.70,386.40,12.34,21.20 80 | 0.08387,0.00,12.830,0,0.4370,5.8740,36.60,4.5026,5,398.0,18.70,396.06,9.10,20.30 81 | 0.04113,25.00,4.860,0,0.4260,6.7270,33.50,5.4007,4,281.0,19.00,396.90,5.29,28.00 82 | 0.04462,25.00,4.860,0,0.4260,6.6190,70.40,5.4007,4,281.0,19.00,395.63,7.22,23.90 83 | 0.03659,25.00,4.860,0,0.4260,6.3020,32.20,5.4007,4,281.0,19.00,396.90,6.72,24.80 84 | 0.03551,25.00,4.860,0,0.4260,6.1670,46.70,5.4007,4,281.0,19.00,390.64,7.51,22.90 85 | 0.05059,0.00,4.490,0,0.4490,6.3890,48.00,4.7794,3,247.0,18.50,396.90,9.62,23.90 86 | 0.05735,0.00,4.490,0,0.4490,6.6300,56.10,4.4377,3,247.0,18.50,392.30,6.53,26.60 87 | 0.05188,0.00,4.490,0,0.4490,6.0150,45.10,4.4272,3,247.0,18.50,395.99,12.86,22.50 88 | 0.07151,0.00,4.490,0,0.4490,6.1210,56.80,3.7476,3,247.0,18.50,395.15,8.44,22.20 89 | 0.05660,0.00,3.410,0,0.4890,7.0070,86.30,3.4217,2,270.0,17.80,396.90,5.50,23.60 90 | 0.05302,0.00,3.410,0,0.4890,7.0790,63.10,3.4145,2,270.0,17.80,396.06,5.70,28.70 91 | 0.04684,0.00,3.410,0,0.4890,6.4170,66.10,3.0923,2,270.0,17.80,392.18,8.81,22.60 92 | 0.03932,0.00,3.410,0,0.4890,6.4050,73.90,3.0921,2,270.0,17.80,393.55,8.20,22.00 93 | 0.04203,28.00,15.040,0,0.4640,6.4420,53.60,3.6659,4,270.0,18.20,395.01,8.16,22.90 94 | 0.02875,28.00,15.040,0,0.4640,6.2110,28.90,3.6659,4,270.0,18.20,396.33,6.21,25.00 95 | 0.04294,28.00,15.040,0,0.4640,6.2490,77.30,3.6150,4,270.0,18.20,396.90,10.59,20.60 96 | 0.12204,0.00,2.890,0,0.4450,6.6250,57.80,3.4952,2,276.0,18.00,357.98,6.65,28.40 97 | 0.11504,0.00,2.890,0,0.4450,6.1630,69.60,3.4952,2,276.0,18.00,391.83,11.34,21.40 98 | 0.12083,0.00,2.890,0,0.4450,8.0690,76.00,3.4952,2,276.0,18.00,396.90,4.21,38.70 99 | 0.08187,0.00,2.890,0,0.4450,7.8200,36.90,3.4952,2,276.0,18.00,393.53,3.57,43.80 100 | 0.06860,0.00,2.890,0,0.4450,7.4160,62.50,3.4952,2,276.0,18.00,396.90,6.19,33.20 101 | 0.14866,0.00,8.560,0,0.5200,6.7270,79.90,2.7778,5,384.0,20.90,394.76,9.42,27.50 102 | 0.11432,0.00,8.560,0,0.5200,6.7810,71.30,2.8561,5,384.0,20.90,395.58,7.67,26.50 103 | 0.22876,0.00,8.560,0,0.5200,6.4050,85.40,2.7147,5,384.0,20.90,70.80,10.63,18.60 104 | 0.21161,0.00,8.560,0,0.5200,6.1370,87.40,2.7147,5,384.0,20.90,394.47,13.44,19.30 105 | 0.13960,0.00,8.560,0,0.5200,6.1670,90.00,2.4210,5,384.0,20.90,392.69,12.33,20.10 106 | 0.13262,0.00,8.560,0,0.5200,5.8510,96.70,2.1069,5,384.0,20.90,394.05,16.47,19.50 107 | 0.17120,0.00,8.560,0,0.5200,5.8360,91.90,2.2110,5,384.0,20.90,395.67,18.66,19.50 108 | 0.13117,0.00,8.560,0,0.5200,6.1270,85.20,2.1224,5,384.0,20.90,387.69,14.09,20.40 109 | 0.12802,0.00,8.560,0,0.5200,6.4740,97.10,2.4329,5,384.0,20.90,395.24,12.27,19.80 110 | 0.26363,0.00,8.560,0,0.5200,6.2290,91.20,2.5451,5,384.0,20.90,391.23,15.55,19.40 111 | 0.10793,0.00,8.560,0,0.5200,6.1950,54.40,2.7778,5,384.0,20.90,393.49,13.00,21.70 112 | 0.10084,0.00,10.010,0,0.5470,6.7150,81.60,2.6775,6,432.0,17.80,395.59,10.16,22.80 113 | 0.12329,0.00,10.010,0,0.5470,5.9130,92.90,2.3534,6,432.0,17.80,394.95,16.21,18.80 114 | 0.22212,0.00,10.010,0,0.5470,6.0920,95.40,2.5480,6,432.0,17.80,396.90,17.09,18.70 115 | 0.14231,0.00,10.010,0,0.5470,6.2540,84.20,2.2565,6,432.0,17.80,388.74,10.45,18.50 116 | 0.17134,0.00,10.010,0,0.5470,5.9280,88.20,2.4631,6,432.0,17.80,344.91,15.76,18.30 117 | 0.13158,0.00,10.010,0,0.5470,6.1760,72.50,2.7301,6,432.0,17.80,393.30,12.04,21.20 118 | 0.15098,0.00,10.010,0,0.5470,6.0210,82.60,2.7474,6,432.0,17.80,394.51,10.30,19.20 119 | 0.13058,0.00,10.010,0,0.5470,5.8720,73.10,2.4775,6,432.0,17.80,338.63,15.37,20.40 120 | 0.14476,0.00,10.010,0,0.5470,5.7310,65.20,2.7592,6,432.0,17.80,391.50,13.61,19.30 121 | 0.06899,0.00,25.650,0,0.5810,5.8700,69.70,2.2577,2,188.0,19.10,389.15,14.37,22.00 122 | 0.07165,0.00,25.650,0,0.5810,6.0040,84.10,2.1974,2,188.0,19.10,377.67,14.27,20.30 123 | 0.09299,0.00,25.650,0,0.5810,5.9610,92.90,2.0869,2,188.0,19.10,378.09,17.93,20.50 124 | 0.15038,0.00,25.650,0,0.5810,5.8560,97.00,1.9444,2,188.0,19.10,370.31,25.41,17.30 125 | 0.09849,0.00,25.650,0,0.5810,5.8790,95.80,2.0063,2,188.0,19.10,379.38,17.58,18.80 126 | 0.16902,0.00,25.650,0,0.5810,5.9860,88.40,1.9929,2,188.0,19.10,385.02,14.81,21.40 127 | 0.38735,0.00,25.650,0,0.5810,5.6130,95.60,1.7572,2,188.0,19.10,359.29,27.26,15.70 128 | 0.25915,0.00,21.890,0,0.6240,5.6930,96.00,1.7883,4,437.0,21.20,392.11,17.19,16.20 129 | 0.32543,0.00,21.890,0,0.6240,6.4310,98.80,1.8125,4,437.0,21.20,396.90,15.39,18.00 130 | 0.88125,0.00,21.890,0,0.6240,5.6370,94.70,1.9799,4,437.0,21.20,396.90,18.34,14.30 131 | 0.34006,0.00,21.890,0,0.6240,6.4580,98.90,2.1185,4,437.0,21.20,395.04,12.60,19.20 132 | 1.19294,0.00,21.890,0,0.6240,6.3260,97.70,2.2710,4,437.0,21.20,396.90,12.26,19.60 133 | 0.59005,0.00,21.890,0,0.6240,6.3720,97.90,2.3274,4,437.0,21.20,385.76,11.12,23.00 134 | 0.32982,0.00,21.890,0,0.6240,5.8220,95.40,2.4699,4,437.0,21.20,388.69,15.03,18.40 135 | 0.97617,0.00,21.890,0,0.6240,5.7570,98.40,2.3460,4,437.0,21.20,262.76,17.31,15.60 136 | 0.55778,0.00,21.890,0,0.6240,6.3350,98.20,2.1107,4,437.0,21.20,394.67,16.96,18.10 137 | 0.32264,0.00,21.890,0,0.6240,5.9420,93.50,1.9669,4,437.0,21.20,378.25,16.90,17.40 138 | 0.35233,0.00,21.890,0,0.6240,6.4540,98.40,1.8498,4,437.0,21.20,394.08,14.59,17.10 139 | 0.24980,0.00,21.890,0,0.6240,5.8570,98.20,1.6686,4,437.0,21.20,392.04,21.32,13.30 140 | 0.54452,0.00,21.890,0,0.6240,6.1510,97.90,1.6687,4,437.0,21.20,396.90,18.46,17.80 141 | 0.29090,0.00,21.890,0,0.6240,6.1740,93.60,1.6119,4,437.0,21.20,388.08,24.16,14.00 142 | 1.62864,0.00,21.890,0,0.6240,5.0190,100.00,1.4394,4,437.0,21.20,396.90,34.41,14.40 143 | 3.32105,0.00,19.580,1,0.8710,5.4030,100.00,1.3216,5,403.0,14.70,396.90,26.82,13.40 144 | 4.09740,0.00,19.580,0,0.8710,5.4680,100.00,1.4118,5,403.0,14.70,396.90,26.42,15.60 145 | 2.77974,0.00,19.580,0,0.8710,4.9030,97.80,1.3459,5,403.0,14.70,396.90,29.29,11.80 146 | 2.37934,0.00,19.580,0,0.8710,6.1300,100.00,1.4191,5,403.0,14.70,172.91,27.80,13.80 147 | 2.15505,0.00,19.580,0,0.8710,5.6280,100.00,1.5166,5,403.0,14.70,169.27,16.65,15.60 148 | 2.36862,0.00,19.580,0,0.8710,4.9260,95.70,1.4608,5,403.0,14.70,391.71,29.53,14.60 149 | 2.33099,0.00,19.580,0,0.8710,5.1860,93.80,1.5296,5,403.0,14.70,356.99,28.32,17.80 150 | 2.73397,0.00,19.580,0,0.8710,5.5970,94.90,1.5257,5,403.0,14.70,351.85,21.45,15.40 151 | 1.65660,0.00,19.580,0,0.8710,6.1220,97.30,1.6180,5,403.0,14.70,372.80,14.10,21.50 152 | 1.49632,0.00,19.580,0,0.8710,5.4040,100.00,1.5916,5,403.0,14.70,341.60,13.28,19.60 153 | 1.12658,0.00,19.580,1,0.8710,5.0120,88.00,1.6102,5,403.0,14.70,343.28,12.12,15.30 154 | 2.14918,0.00,19.580,0,0.8710,5.7090,98.50,1.6232,5,403.0,14.70,261.95,15.79,19.40 155 | 1.41385,0.00,19.580,1,0.8710,6.1290,96.00,1.7494,5,403.0,14.70,321.02,15.12,17.00 156 | 3.53501,0.00,19.580,1,0.8710,6.1520,82.60,1.7455,5,403.0,14.70,88.01,15.02,15.60 157 | 2.44668,0.00,19.580,0,0.8710,5.2720,94.00,1.7364,5,403.0,14.70,88.63,16.14,13.10 158 | 1.22358,0.00,19.580,0,0.6050,6.9430,97.40,1.8773,5,403.0,14.70,363.43,4.59,41.30 159 | 1.34284,0.00,19.580,0,0.6050,6.0660,100.00,1.7573,5,403.0,14.70,353.89,6.43,24.30 160 | 1.42502,0.00,19.580,0,0.8710,6.5100,100.00,1.7659,5,403.0,14.70,364.31,7.39,23.30 161 | 1.27346,0.00,19.580,1,0.6050,6.2500,92.60,1.7984,5,403.0,14.70,338.92,5.50,27.00 162 | 1.46336,0.00,19.580,0,0.6050,7.4890,90.80,1.9709,5,403.0,14.70,374.43,1.73,50.00 163 | 1.83377,0.00,19.580,1,0.6050,7.8020,98.20,2.0407,5,403.0,14.70,389.61,1.92,50.00 164 | 1.51902,0.00,19.580,1,0.6050,8.3750,93.90,2.1620,5,403.0,14.70,388.45,3.32,50.00 165 | 2.24236,0.00,19.580,0,0.6050,5.8540,91.80,2.4220,5,403.0,14.70,395.11,11.64,22.70 166 | 2.92400,0.00,19.580,0,0.6050,6.1010,93.00,2.2834,5,403.0,14.70,240.16,9.81,25.00 167 | 2.01019,0.00,19.580,0,0.6050,7.9290,96.20,2.0459,5,403.0,14.70,369.30,3.70,50.00 168 | 1.80028,0.00,19.580,0,0.6050,5.8770,79.20,2.4259,5,403.0,14.70,227.61,12.14,23.80 169 | 2.30040,0.00,19.580,0,0.6050,6.3190,96.10,2.1000,5,403.0,14.70,297.09,11.10,23.80 170 | 2.44953,0.00,19.580,0,0.6050,6.4020,95.20,2.2625,5,403.0,14.70,330.04,11.32,22.30 171 | 1.20742,0.00,19.580,0,0.6050,5.8750,94.60,2.4259,5,403.0,14.70,292.29,14.43,17.40 172 | 2.31390,0.00,19.580,0,0.6050,5.8800,97.30,2.3887,5,403.0,14.70,348.13,12.03,19.10 173 | 0.13914,0.00,4.050,0,0.5100,5.5720,88.50,2.5961,5,296.0,16.60,396.90,14.69,23.10 174 | 0.09178,0.00,4.050,0,0.5100,6.4160,84.10,2.6463,5,296.0,16.60,395.50,9.04,23.60 175 | 0.08447,0.00,4.050,0,0.5100,5.8590,68.70,2.7019,5,296.0,16.60,393.23,9.64,22.60 176 | 0.06664,0.00,4.050,0,0.5100,6.5460,33.10,3.1323,5,296.0,16.60,390.96,5.33,29.40 177 | 0.07022,0.00,4.050,0,0.5100,6.0200,47.20,3.5549,5,296.0,16.60,393.23,10.11,23.20 178 | 0.05425,0.00,4.050,0,0.5100,6.3150,73.40,3.3175,5,296.0,16.60,395.60,6.29,24.60 179 | 0.06642,0.00,4.050,0,0.5100,6.8600,74.40,2.9153,5,296.0,16.60,391.27,6.92,29.90 180 | 0.05780,0.00,2.460,0,0.4880,6.9800,58.40,2.8290,3,193.0,17.80,396.90,5.04,37.20 181 | 0.06588,0.00,2.460,0,0.4880,7.7650,83.30,2.7410,3,193.0,17.80,395.56,7.56,39.80 182 | 0.06888,0.00,2.460,0,0.4880,6.1440,62.20,2.5979,3,193.0,17.80,396.90,9.45,36.20 183 | 0.09103,0.00,2.460,0,0.4880,7.1550,92.20,2.7006,3,193.0,17.80,394.12,4.82,37.90 184 | 0.10008,0.00,2.460,0,0.4880,6.5630,95.60,2.8470,3,193.0,17.80,396.90,5.68,32.50 185 | 0.08308,0.00,2.460,0,0.4880,5.6040,89.80,2.9879,3,193.0,17.80,391.00,13.98,26.40 186 | 0.06047,0.00,2.460,0,0.4880,6.1530,68.80,3.2797,3,193.0,17.80,387.11,13.15,29.60 187 | 0.05602,0.00,2.460,0,0.4880,7.8310,53.60,3.1992,3,193.0,17.80,392.63,4.45,50.00 188 | 0.07875,45.00,3.440,0,0.4370,6.7820,41.10,3.7886,5,398.0,15.20,393.87,6.68,32.00 189 | 0.12579,45.00,3.440,0,0.4370,6.5560,29.10,4.5667,5,398.0,15.20,382.84,4.56,29.80 190 | 0.08370,45.00,3.440,0,0.4370,7.1850,38.90,4.5667,5,398.0,15.20,396.90,5.39,34.90 191 | 0.09068,45.00,3.440,0,0.4370,6.9510,21.50,6.4798,5,398.0,15.20,377.68,5.10,37.00 192 | 0.06911,45.00,3.440,0,0.4370,6.7390,30.80,6.4798,5,398.0,15.20,389.71,4.69,30.50 193 | 0.08664,45.00,3.440,0,0.4370,7.1780,26.30,6.4798,5,398.0,15.20,390.49,2.87,36.40 194 | 0.02187,60.00,2.930,0,0.4010,6.8000,9.90,6.2196,1,265.0,15.60,393.37,5.03,31.10 195 | 0.01439,60.00,2.930,0,0.4010,6.6040,18.80,6.2196,1,265.0,15.60,376.70,4.38,29.10 196 | 0.01381,80.00,0.460,0,0.4220,7.8750,32.00,5.6484,4,255.0,14.40,394.23,2.97,50.00 197 | 0.04011,80.00,1.520,0,0.4040,7.2870,34.10,7.3090,2,329.0,12.60,396.90,4.08,33.30 198 | 0.04666,80.00,1.520,0,0.4040,7.1070,36.60,7.3090,2,329.0,12.60,354.31,8.61,30.30 199 | 0.03768,80.00,1.520,0,0.4040,7.2740,38.30,7.3090,2,329.0,12.60,392.20,6.62,34.60 200 | 0.03150,95.00,1.470,0,0.4030,6.9750,15.30,7.6534,3,402.0,17.00,396.90,4.56,34.90 201 | 0.01778,95.00,1.470,0,0.4030,7.1350,13.90,7.6534,3,402.0,17.00,384.30,4.45,32.90 202 | 0.03445,82.50,2.030,0,0.4150,6.1620,38.40,6.2700,2,348.0,14.70,393.77,7.43,24.10 203 | 0.02177,82.50,2.030,0,0.4150,7.6100,15.70,6.2700,2,348.0,14.70,395.38,3.11,42.30 204 | 0.03510,95.00,2.680,0,0.4161,7.8530,33.20,5.1180,4,224.0,14.70,392.78,3.81,48.50 205 | 0.02009,95.00,2.680,0,0.4161,8.0340,31.90,5.1180,4,224.0,14.70,390.55,2.88,50.00 206 | 0.13642,0.00,10.590,0,0.4890,5.8910,22.30,3.9454,4,277.0,18.60,396.90,10.87,22.60 207 | 0.22969,0.00,10.590,0,0.4890,6.3260,52.50,4.3549,4,277.0,18.60,394.87,10.97,24.40 208 | 0.25199,0.00,10.590,0,0.4890,5.7830,72.70,4.3549,4,277.0,18.60,389.43,18.06,22.50 209 | 0.13587,0.00,10.590,1,0.4890,6.0640,59.10,4.2392,4,277.0,18.60,381.32,14.66,24.40 210 | 0.43571,0.00,10.590,1,0.4890,5.3440,100.00,3.8750,4,277.0,18.60,396.90,23.09,20.00 211 | 0.17446,0.00,10.590,1,0.4890,5.9600,92.10,3.8771,4,277.0,18.60,393.25,17.27,21.70 212 | 0.37578,0.00,10.590,1,0.4890,5.4040,88.60,3.6650,4,277.0,18.60,395.24,23.98,19.30 213 | 0.21719,0.00,10.590,1,0.4890,5.8070,53.80,3.6526,4,277.0,18.60,390.94,16.03,22.40 214 | 0.14052,0.00,10.590,0,0.4890,6.3750,32.30,3.9454,4,277.0,18.60,385.81,9.38,28.10 215 | 0.28955,0.00,10.590,0,0.4890,5.4120,9.80,3.5875,4,277.0,18.60,348.93,29.55,23.70 216 | 0.19802,0.00,10.590,0,0.4890,6.1820,42.40,3.9454,4,277.0,18.60,393.63,9.47,25.00 217 | 0.04560,0.00,13.890,1,0.5500,5.8880,56.00,3.1121,5,276.0,16.40,392.80,13.51,23.30 218 | 0.07013,0.00,13.890,0,0.5500,6.6420,85.10,3.4211,5,276.0,16.40,392.78,9.69,28.70 219 | 0.11069,0.00,13.890,1,0.5500,5.9510,93.80,2.8893,5,276.0,16.40,396.90,17.92,21.50 220 | 0.11425,0.00,13.890,1,0.5500,6.3730,92.40,3.3633,5,276.0,16.40,393.74,10.50,23.00 221 | 0.35809,0.00,6.200,1,0.5070,6.9510,88.50,2.8617,8,307.0,17.40,391.70,9.71,26.70 222 | 0.40771,0.00,6.200,1,0.5070,6.1640,91.30,3.0480,8,307.0,17.40,395.24,21.46,21.70 223 | 0.62356,0.00,6.200,1,0.5070,6.8790,77.70,3.2721,8,307.0,17.40,390.39,9.93,27.50 224 | 0.61470,0.00,6.200,0,0.5070,6.6180,80.80,3.2721,8,307.0,17.40,396.90,7.60,30.10 225 | 0.31533,0.00,6.200,0,0.5040,8.2660,78.30,2.8944,8,307.0,17.40,385.05,4.14,44.80 226 | 0.52693,0.00,6.200,0,0.5040,8.7250,83.00,2.8944,8,307.0,17.40,382.00,4.63,50.00 227 | 0.38214,0.00,6.200,0,0.5040,8.0400,86.50,3.2157,8,307.0,17.40,387.38,3.13,37.60 228 | 0.41238,0.00,6.200,0,0.5040,7.1630,79.90,3.2157,8,307.0,17.40,372.08,6.36,31.60 229 | 0.29819,0.00,6.200,0,0.5040,7.6860,17.00,3.3751,8,307.0,17.40,377.51,3.92,46.70 230 | 0.44178,0.00,6.200,0,0.5040,6.5520,21.40,3.3751,8,307.0,17.40,380.34,3.76,31.50 231 | 0.53700,0.00,6.200,0,0.5040,5.9810,68.10,3.6715,8,307.0,17.40,378.35,11.65,24.30 232 | 0.46296,0.00,6.200,0,0.5040,7.4120,76.90,3.6715,8,307.0,17.40,376.14,5.25,31.70 233 | 0.57529,0.00,6.200,0,0.5070,8.3370,73.30,3.8384,8,307.0,17.40,385.91,2.47,41.70 234 | 0.33147,0.00,6.200,0,0.5070,8.2470,70.40,3.6519,8,307.0,17.40,378.95,3.95,48.30 235 | 0.44791,0.00,6.200,1,0.5070,6.7260,66.50,3.6519,8,307.0,17.40,360.20,8.05,29.00 236 | 0.33045,0.00,6.200,0,0.5070,6.0860,61.50,3.6519,8,307.0,17.40,376.75,10.88,24.00 237 | 0.52058,0.00,6.200,1,0.5070,6.6310,76.50,4.1480,8,307.0,17.40,388.45,9.54,25.10 238 | 0.51183,0.00,6.200,0,0.5070,7.3580,71.60,4.1480,8,307.0,17.40,390.07,4.73,31.50 239 | 0.08244,30.00,4.930,0,0.4280,6.4810,18.50,6.1899,6,300.0,16.60,379.41,6.36,23.70 240 | 0.09252,30.00,4.930,0,0.4280,6.6060,42.20,6.1899,6,300.0,16.60,383.78,7.37,23.30 241 | 0.11329,30.00,4.930,0,0.4280,6.8970,54.30,6.3361,6,300.0,16.60,391.25,11.38,22.00 242 | 0.10612,30.00,4.930,0,0.4280,6.0950,65.10,6.3361,6,300.0,16.60,394.62,12.40,20.10 243 | 0.10290,30.00,4.930,0,0.4280,6.3580,52.90,7.0355,6,300.0,16.60,372.75,11.22,22.20 244 | 0.12757,30.00,4.930,0,0.4280,6.3930,7.80,7.0355,6,300.0,16.60,374.71,5.19,23.70 245 | 0.20608,22.00,5.860,0,0.4310,5.5930,76.50,7.9549,7,330.0,19.10,372.49,12.50,17.60 246 | 0.19133,22.00,5.860,0,0.4310,5.6050,70.20,7.9549,7,330.0,19.10,389.13,18.46,18.50 247 | 0.33983,22.00,5.860,0,0.4310,6.1080,34.90,8.0555,7,330.0,19.10,390.18,9.16,24.30 248 | 0.19657,22.00,5.860,0,0.4310,6.2260,79.20,8.0555,7,330.0,19.10,376.14,10.15,20.50 249 | 0.16439,22.00,5.860,0,0.4310,6.4330,49.10,7.8265,7,330.0,19.10,374.71,9.52,24.50 250 | 0.19073,22.00,5.860,0,0.4310,6.7180,17.50,7.8265,7,330.0,19.10,393.74,6.56,26.20 251 | 0.14030,22.00,5.860,0,0.4310,6.4870,13.00,7.3967,7,330.0,19.10,396.28,5.90,24.40 252 | 0.21409,22.00,5.860,0,0.4310,6.4380,8.90,7.3967,7,330.0,19.10,377.07,3.59,24.80 253 | 0.08221,22.00,5.860,0,0.4310,6.9570,6.80,8.9067,7,330.0,19.10,386.09,3.53,29.60 254 | 0.36894,22.00,5.860,0,0.4310,8.2590,8.40,8.9067,7,330.0,19.10,396.90,3.54,42.80 255 | 0.04819,80.00,3.640,0,0.3920,6.1080,32.00,9.2203,1,315.0,16.40,392.89,6.57,21.90 256 | 0.03548,80.00,3.640,0,0.3920,5.8760,19.10,9.2203,1,315.0,16.40,395.18,9.25,20.90 257 | 0.01538,90.00,3.750,0,0.3940,7.4540,34.20,6.3361,3,244.0,15.90,386.34,3.11,44.00 258 | 0.61154,20.00,3.970,0,0.6470,8.7040,86.90,1.8010,5,264.0,13.00,389.70,5.12,50.00 259 | 0.66351,20.00,3.970,0,0.6470,7.3330,100.00,1.8946,5,264.0,13.00,383.29,7.79,36.00 260 | 0.65665,20.00,3.970,0,0.6470,6.8420,100.00,2.0107,5,264.0,13.00,391.93,6.90,30.10 261 | 0.54011,20.00,3.970,0,0.6470,7.2030,81.80,2.1121,5,264.0,13.00,392.80,9.59,33.80 262 | 0.53412,20.00,3.970,0,0.6470,7.5200,89.40,2.1398,5,264.0,13.00,388.37,7.26,43.10 263 | 0.52014,20.00,3.970,0,0.6470,8.3980,91.50,2.2885,5,264.0,13.00,386.86,5.91,48.80 264 | 0.82526,20.00,3.970,0,0.6470,7.3270,94.50,2.0788,5,264.0,13.00,393.42,11.25,31.00 265 | 0.55007,20.00,3.970,0,0.6470,7.2060,91.60,1.9301,5,264.0,13.00,387.89,8.10,36.50 266 | 0.76162,20.00,3.970,0,0.6470,5.5600,62.80,1.9865,5,264.0,13.00,392.40,10.45,22.80 267 | 0.78570,20.00,3.970,0,0.6470,7.0140,84.60,2.1329,5,264.0,13.00,384.07,14.79,30.70 268 | 0.57834,20.00,3.970,0,0.5750,8.2970,67.00,2.4216,5,264.0,13.00,384.54,7.44,50.00 269 | 0.54050,20.00,3.970,0,0.5750,7.4700,52.60,2.8720,5,264.0,13.00,390.30,3.16,43.50 270 | 0.09065,20.00,6.960,1,0.4640,5.9200,61.50,3.9175,3,223.0,18.60,391.34,13.65,20.70 271 | 0.29916,20.00,6.960,0,0.4640,5.8560,42.10,4.4290,3,223.0,18.60,388.65,13.00,21.10 272 | 0.16211,20.00,6.960,0,0.4640,6.2400,16.30,4.4290,3,223.0,18.60,396.90,6.59,25.20 273 | 0.11460,20.00,6.960,0,0.4640,6.5380,58.70,3.9175,3,223.0,18.60,394.96,7.73,24.40 274 | 0.22188,20.00,6.960,1,0.4640,7.6910,51.80,4.3665,3,223.0,18.60,390.77,6.58,35.20 275 | 0.05644,40.00,6.410,1,0.4470,6.7580,32.90,4.0776,4,254.0,17.60,396.90,3.53,32.40 276 | 0.09604,40.00,6.410,0,0.4470,6.8540,42.80,4.2673,4,254.0,17.60,396.90,2.98,32.00 277 | 0.10469,40.00,6.410,1,0.4470,7.2670,49.00,4.7872,4,254.0,17.60,389.25,6.05,33.20 278 | 0.06127,40.00,6.410,1,0.4470,6.8260,27.60,4.8628,4,254.0,17.60,393.45,4.16,33.10 279 | 0.07978,40.00,6.410,0,0.4470,6.4820,32.10,4.1403,4,254.0,17.60,396.90,7.19,29.10 280 | 0.21038,20.00,3.330,0,0.4429,6.8120,32.20,4.1007,5,216.0,14.90,396.90,4.85,35.10 281 | 0.03578,20.00,3.330,0,0.4429,7.8200,64.50,4.6947,5,216.0,14.90,387.31,3.76,45.40 282 | 0.03705,20.00,3.330,0,0.4429,6.9680,37.20,5.2447,5,216.0,14.90,392.23,4.59,35.40 283 | 0.06129,20.00,3.330,1,0.4429,7.6450,49.70,5.2119,5,216.0,14.90,377.07,3.01,46.00 284 | 0.01501,90.00,1.210,1,0.4010,7.9230,24.80,5.8850,1,198.0,13.60,395.52,3.16,50.00 285 | 0.00906,90.00,2.970,0,0.4000,7.0880,20.80,7.3073,1,285.0,15.30,394.72,7.85,32.20 286 | 0.01096,55.00,2.250,0,0.3890,6.4530,31.90,7.3073,1,300.0,15.30,394.72,8.23,22.00 287 | 0.01965,80.00,1.760,0,0.3850,6.2300,31.50,9.0892,1,241.0,18.20,341.60,12.93,20.10 288 | 0.03871,52.50,5.320,0,0.4050,6.2090,31.30,7.3172,6,293.0,16.60,396.90,7.14,23.20 289 | 0.04590,52.50,5.320,0,0.4050,6.3150,45.60,7.3172,6,293.0,16.60,396.90,7.60,22.30 290 | 0.04297,52.50,5.320,0,0.4050,6.5650,22.90,7.3172,6,293.0,16.60,371.72,9.51,24.80 291 | 0.03502,80.00,4.950,0,0.4110,6.8610,27.90,5.1167,4,245.0,19.20,396.90,3.33,28.50 292 | 0.07886,80.00,4.950,0,0.4110,7.1480,27.70,5.1167,4,245.0,19.20,396.90,3.56,37.30 293 | 0.03615,80.00,4.950,0,0.4110,6.6300,23.40,5.1167,4,245.0,19.20,396.90,4.70,27.90 294 | 0.08265,0.00,13.920,0,0.4370,6.1270,18.40,5.5027,4,289.0,16.00,396.90,8.58,23.90 295 | 0.08199,0.00,13.920,0,0.4370,6.0090,42.30,5.5027,4,289.0,16.00,396.90,10.40,21.70 296 | 0.12932,0.00,13.920,0,0.4370,6.6780,31.10,5.9604,4,289.0,16.00,396.90,6.27,28.60 297 | 0.05372,0.00,13.920,0,0.4370,6.5490,51.00,5.9604,4,289.0,16.00,392.85,7.39,27.10 298 | 0.14103,0.00,13.920,0,0.4370,5.7900,58.00,6.3200,4,289.0,16.00,396.90,15.84,20.30 299 | 0.06466,70.00,2.240,0,0.4000,6.3450,20.10,7.8278,5,358.0,14.80,368.24,4.97,22.50 300 | 0.05561,70.00,2.240,0,0.4000,7.0410,10.00,7.8278,5,358.0,14.80,371.58,4.74,29.00 301 | 0.04417,70.00,2.240,0,0.4000,6.8710,47.40,7.8278,5,358.0,14.80,390.86,6.07,24.80 302 | 0.03537,34.00,6.090,0,0.4330,6.5900,40.40,5.4917,7,329.0,16.10,395.75,9.50,22.00 303 | 0.09266,34.00,6.090,0,0.4330,6.4950,18.40,5.4917,7,329.0,16.10,383.61,8.67,26.40 304 | 0.10000,34.00,6.090,0,0.4330,6.9820,17.70,5.4917,7,329.0,16.10,390.43,4.86,33.10 305 | 0.05515,33.00,2.180,0,0.4720,7.2360,41.10,4.0220,7,222.0,18.40,393.68,6.93,36.10 306 | 0.05479,33.00,2.180,0,0.4720,6.6160,58.10,3.3700,7,222.0,18.40,393.36,8.93,28.40 307 | 0.07503,33.00,2.180,0,0.4720,7.4200,71.90,3.0992,7,222.0,18.40,396.90,6.47,33.40 308 | 0.04932,33.00,2.180,0,0.4720,6.8490,70.30,3.1827,7,222.0,18.40,396.90,7.53,28.20 309 | 0.49298,0.00,9.900,0,0.5440,6.6350,82.50,3.3175,4,304.0,18.40,396.90,4.54,22.80 310 | 0.34940,0.00,9.900,0,0.5440,5.9720,76.70,3.1025,4,304.0,18.40,396.24,9.97,20.30 311 | 2.63548,0.00,9.900,0,0.5440,4.9730,37.80,2.5194,4,304.0,18.40,350.45,12.64,16.10 312 | 0.79041,0.00,9.900,0,0.5440,6.1220,52.80,2.6403,4,304.0,18.40,396.90,5.98,22.10 313 | 0.26169,0.00,9.900,0,0.5440,6.0230,90.40,2.8340,4,304.0,18.40,396.30,11.72,19.40 314 | 0.26938,0.00,9.900,0,0.5440,6.2660,82.80,3.2628,4,304.0,18.40,393.39,7.90,21.60 315 | 0.36920,0.00,9.900,0,0.5440,6.5670,87.30,3.6023,4,304.0,18.40,395.69,9.28,23.80 316 | 0.25356,0.00,9.900,0,0.5440,5.7050,77.70,3.9450,4,304.0,18.40,396.42,11.50,16.20 317 | 0.31827,0.00,9.900,0,0.5440,5.9140,83.20,3.9986,4,304.0,18.40,390.70,18.33,17.80 318 | 0.24522,0.00,9.900,0,0.5440,5.7820,71.70,4.0317,4,304.0,18.40,396.90,15.94,19.80 319 | 0.40202,0.00,9.900,0,0.5440,6.3820,67.20,3.5325,4,304.0,18.40,395.21,10.36,23.10 320 | 0.47547,0.00,9.900,0,0.5440,6.1130,58.80,4.0019,4,304.0,18.40,396.23,12.73,21.00 321 | 0.16760,0.00,7.380,0,0.4930,6.4260,52.30,4.5404,5,287.0,19.60,396.90,7.20,23.80 322 | 0.18159,0.00,7.380,0,0.4930,6.3760,54.30,4.5404,5,287.0,19.60,396.90,6.87,23.10 323 | 0.35114,0.00,7.380,0,0.4930,6.0410,49.90,4.7211,5,287.0,19.60,396.90,7.70,20.40 324 | 0.28392,0.00,7.380,0,0.4930,5.7080,74.30,4.7211,5,287.0,19.60,391.13,11.74,18.50 325 | 0.34109,0.00,7.380,0,0.4930,6.4150,40.10,4.7211,5,287.0,19.60,396.90,6.12,25.00 326 | 0.19186,0.00,7.380,0,0.4930,6.4310,14.70,5.4159,5,287.0,19.60,393.68,5.08,24.60 327 | 0.30347,0.00,7.380,0,0.4930,6.3120,28.90,5.4159,5,287.0,19.60,396.90,6.15,23.00 328 | 0.24103,0.00,7.380,0,0.4930,6.0830,43.70,5.4159,5,287.0,19.60,396.90,12.79,22.20 329 | 0.06617,0.00,3.240,0,0.4600,5.8680,25.80,5.2146,4,430.0,16.90,382.44,9.97,19.30 330 | 0.06724,0.00,3.240,0,0.4600,6.3330,17.20,5.2146,4,430.0,16.90,375.21,7.34,22.60 331 | 0.04544,0.00,3.240,0,0.4600,6.1440,32.20,5.8736,4,430.0,16.90,368.57,9.09,19.80 332 | 0.05023,35.00,6.060,0,0.4379,5.7060,28.40,6.6407,1,304.0,16.90,394.02,12.43,17.10 333 | 0.03466,35.00,6.060,0,0.4379,6.0310,23.30,6.6407,1,304.0,16.90,362.25,7.83,19.40 334 | 0.05083,0.00,5.190,0,0.5150,6.3160,38.10,6.4584,5,224.0,20.20,389.71,5.68,22.20 335 | 0.03738,0.00,5.190,0,0.5150,6.3100,38.50,6.4584,5,224.0,20.20,389.40,6.75,20.70 336 | 0.03961,0.00,5.190,0,0.5150,6.0370,34.50,5.9853,5,224.0,20.20,396.90,8.01,21.10 337 | 0.03427,0.00,5.190,0,0.5150,5.8690,46.30,5.2311,5,224.0,20.20,396.90,9.80,19.50 338 | 0.03041,0.00,5.190,0,0.5150,5.8950,59.60,5.6150,5,224.0,20.20,394.81,10.56,18.50 339 | 0.03306,0.00,5.190,0,0.5150,6.0590,37.30,4.8122,5,224.0,20.20,396.14,8.51,20.60 340 | 0.05497,0.00,5.190,0,0.5150,5.9850,45.40,4.8122,5,224.0,20.20,396.90,9.74,19.00 341 | 0.06151,0.00,5.190,0,0.5150,5.9680,58.50,4.8122,5,224.0,20.20,396.90,9.29,18.70 342 | 0.01301,35.00,1.520,0,0.4420,7.2410,49.30,7.0379,1,284.0,15.50,394.74,5.49,32.70 343 | 0.02498,0.00,1.890,0,0.5180,6.5400,59.70,6.2669,1,422.0,15.90,389.96,8.65,16.50 344 | 0.02543,55.00,3.780,0,0.4840,6.6960,56.40,5.7321,5,370.0,17.60,396.90,7.18,23.90 345 | 0.03049,55.00,3.780,0,0.4840,6.8740,28.10,6.4654,5,370.0,17.60,387.97,4.61,31.20 346 | 0.03113,0.00,4.390,0,0.4420,6.0140,48.50,8.0136,3,352.0,18.80,385.64,10.53,17.50 347 | 0.06162,0.00,4.390,0,0.4420,5.8980,52.30,8.0136,3,352.0,18.80,364.61,12.67,17.20 348 | 0.01870,85.00,4.150,0,0.4290,6.5160,27.70,8.5353,4,351.0,17.90,392.43,6.36,23.10 349 | 0.01501,80.00,2.010,0,0.4350,6.6350,29.70,8.3440,4,280.0,17.00,390.94,5.99,24.50 350 | 0.02899,40.00,1.250,0,0.4290,6.9390,34.50,8.7921,1,335.0,19.70,389.85,5.89,26.60 351 | 0.06211,40.00,1.250,0,0.4290,6.4900,44.40,8.7921,1,335.0,19.70,396.90,5.98,22.90 352 | 0.07950,60.00,1.690,0,0.4110,6.5790,35.90,10.7103,4,411.0,18.30,370.78,5.49,24.10 353 | 0.07244,60.00,1.690,0,0.4110,5.8840,18.50,10.7103,4,411.0,18.30,392.33,7.79,18.60 354 | 0.01709,90.00,2.020,0,0.4100,6.7280,36.10,12.1265,5,187.0,17.00,384.46,4.50,30.10 355 | 0.04301,80.00,1.910,0,0.4130,5.6630,21.90,10.5857,4,334.0,22.00,382.80,8.05,18.20 356 | 0.10659,80.00,1.910,0,0.4130,5.9360,19.50,10.5857,4,334.0,22.00,376.04,5.57,20.60 357 | 8.98296,0.00,18.100,1,0.7700,6.2120,97.40,2.1222,24,666.0,20.20,377.73,17.60,17.80 358 | 3.84970,0.00,18.100,1,0.7700,6.3950,91.00,2.5052,24,666.0,20.20,391.34,13.27,21.70 359 | 5.20177,0.00,18.100,1,0.7700,6.1270,83.40,2.7227,24,666.0,20.20,395.43,11.48,22.70 360 | 4.26131,0.00,18.100,0,0.7700,6.1120,81.30,2.5091,24,666.0,20.20,390.74,12.67,22.60 361 | 4.54192,0.00,18.100,0,0.7700,6.3980,88.00,2.5182,24,666.0,20.20,374.56,7.79,25.00 362 | 3.83684,0.00,18.100,0,0.7700,6.2510,91.10,2.2955,24,666.0,20.20,350.65,14.19,19.90 363 | 3.67822,0.00,18.100,0,0.7700,5.3620,96.20,2.1036,24,666.0,20.20,380.79,10.19,20.80 364 | 4.22239,0.00,18.100,1,0.7700,5.8030,89.00,1.9047,24,666.0,20.20,353.04,14.64,16.80 365 | 3.47428,0.00,18.100,1,0.7180,8.7800,82.90,1.9047,24,666.0,20.20,354.55,5.29,21.90 366 | 4.55587,0.00,18.100,0,0.7180,3.5610,87.90,1.6132,24,666.0,20.20,354.70,7.12,27.50 367 | 3.69695,0.00,18.100,0,0.7180,4.9630,91.40,1.7523,24,666.0,20.20,316.03,14.00,21.90 368 | 13.52220,0.00,18.100,0,0.6310,3.8630,100.00,1.5106,24,666.0,20.20,131.42,13.33,23.10 369 | 4.89822,0.00,18.100,0,0.6310,4.9700,100.00,1.3325,24,666.0,20.20,375.52,3.26,50.00 370 | 5.66998,0.00,18.100,1,0.6310,6.6830,96.80,1.3567,24,666.0,20.20,375.33,3.73,50.00 371 | 6.53876,0.00,18.100,1,0.6310,7.0160,97.50,1.2024,24,666.0,20.20,392.05,2.96,50.00 372 | 9.23230,0.00,18.100,0,0.6310,6.2160,100.00,1.1691,24,666.0,20.20,366.15,9.53,50.00 373 | 8.26725,0.00,18.100,1,0.6680,5.8750,89.60,1.1296,24,666.0,20.20,347.88,8.88,50.00 374 | 11.10810,0.00,18.100,0,0.6680,4.9060,100.00,1.1742,24,666.0,20.20,396.90,34.77,13.80 375 | 18.49820,0.00,18.100,0,0.6680,4.1380,100.00,1.1370,24,666.0,20.20,396.90,37.97,13.80 376 | 19.60910,0.00,18.100,0,0.6710,7.3130,97.90,1.3163,24,666.0,20.20,396.90,13.44,15.00 377 | 15.28800,0.00,18.100,0,0.6710,6.6490,93.30,1.3449,24,666.0,20.20,363.02,23.24,13.90 378 | 9.82349,0.00,18.100,0,0.6710,6.7940,98.80,1.3580,24,666.0,20.20,396.90,21.24,13.30 379 | 23.64820,0.00,18.100,0,0.6710,6.3800,96.20,1.3861,24,666.0,20.20,396.90,23.69,13.10 380 | 17.86670,0.00,18.100,0,0.6710,6.2230,100.00,1.3861,24,666.0,20.20,393.74,21.78,10.20 381 | 88.97620,0.00,18.100,0,0.6710,6.9680,91.90,1.4165,24,666.0,20.20,396.90,17.21,10.40 382 | 15.87440,0.00,18.100,0,0.6710,6.5450,99.10,1.5192,24,666.0,20.20,396.90,21.08,10.90 383 | 9.18702,0.00,18.100,0,0.7000,5.5360,100.00,1.5804,24,666.0,20.20,396.90,23.60,11.30 384 | 7.99248,0.00,18.100,0,0.7000,5.5200,100.00,1.5331,24,666.0,20.20,396.90,24.56,12.30 385 | 20.08490,0.00,18.100,0,0.7000,4.3680,91.20,1.4395,24,666.0,20.20,285.83,30.63,8.80 386 | 16.81180,0.00,18.100,0,0.7000,5.2770,98.10,1.4261,24,666.0,20.20,396.90,30.81,7.20 387 | 24.39380,0.00,18.100,0,0.7000,4.6520,100.00,1.4672,24,666.0,20.20,396.90,28.28,10.50 388 | 22.59710,0.00,18.100,0,0.7000,5.0000,89.50,1.5184,24,666.0,20.20,396.90,31.99,7.40 389 | 14.33370,0.00,18.100,0,0.7000,4.8800,100.00,1.5895,24,666.0,20.20,372.92,30.62,10.20 390 | 8.15174,0.00,18.100,0,0.7000,5.3900,98.90,1.7281,24,666.0,20.20,396.90,20.85,11.50 391 | 6.96215,0.00,18.100,0,0.7000,5.7130,97.00,1.9265,24,666.0,20.20,394.43,17.11,15.10 392 | 5.29305,0.00,18.100,0,0.7000,6.0510,82.50,2.1678,24,666.0,20.20,378.38,18.76,23.20 393 | 11.57790,0.00,18.100,0,0.7000,5.0360,97.00,1.7700,24,666.0,20.20,396.90,25.68,9.70 394 | 8.64476,0.00,18.100,0,0.6930,6.1930,92.60,1.7912,24,666.0,20.20,396.90,15.17,13.80 395 | 13.35980,0.00,18.100,0,0.6930,5.8870,94.70,1.7821,24,666.0,20.20,396.90,16.35,12.70 396 | 8.71675,0.00,18.100,0,0.6930,6.4710,98.80,1.7257,24,666.0,20.20,391.98,17.12,13.10 397 | 5.87205,0.00,18.100,0,0.6930,6.4050,96.00,1.6768,24,666.0,20.20,396.90,19.37,12.50 398 | 7.67202,0.00,18.100,0,0.6930,5.7470,98.90,1.6334,24,666.0,20.20,393.10,19.92,8.50 399 | 38.35180,0.00,18.100,0,0.6930,5.4530,100.00,1.4896,24,666.0,20.20,396.90,30.59,5.00 400 | 9.91655,0.00,18.100,0,0.6930,5.8520,77.80,1.5004,24,666.0,20.20,338.16,29.97,6.30 401 | 25.04610,0.00,18.100,0,0.6930,5.9870,100.00,1.5888,24,666.0,20.20,396.90,26.77,5.60 402 | 14.23620,0.00,18.100,0,0.6930,6.3430,100.00,1.5741,24,666.0,20.20,396.90,20.32,7.20 403 | 9.59571,0.00,18.100,0,0.6930,6.4040,100.00,1.6390,24,666.0,20.20,376.11,20.31,12.10 404 | 24.80170,0.00,18.100,0,0.6930,5.3490,96.00,1.7028,24,666.0,20.20,396.90,19.77,8.30 405 | 41.52920,0.00,18.100,0,0.6930,5.5310,85.40,1.6074,24,666.0,20.20,329.46,27.38,8.50 406 | 67.92080,0.00,18.100,0,0.6930,5.6830,100.00,1.4254,24,666.0,20.20,384.97,22.98,5.00 407 | 20.71620,0.00,18.100,0,0.6590,4.1380,100.00,1.1781,24,666.0,20.20,370.22,23.34,11.90 408 | 11.95110,0.00,18.100,0,0.6590,5.6080,100.00,1.2852,24,666.0,20.20,332.09,12.13,27.90 409 | 7.40389,0.00,18.100,0,0.5970,5.6170,97.90,1.4547,24,666.0,20.20,314.64,26.40,17.20 410 | 14.43830,0.00,18.100,0,0.5970,6.8520,100.00,1.4655,24,666.0,20.20,179.36,19.78,27.50 411 | 51.13580,0.00,18.100,0,0.5970,5.7570,100.00,1.4130,24,666.0,20.20,2.60,10.11,15.00 412 | 14.05070,0.00,18.100,0,0.5970,6.6570,100.00,1.5275,24,666.0,20.20,35.05,21.22,17.20 413 | 18.81100,0.00,18.100,0,0.5970,4.6280,100.00,1.5539,24,666.0,20.20,28.79,34.37,17.90 414 | 28.65580,0.00,18.100,0,0.5970,5.1550,100.00,1.5894,24,666.0,20.20,210.97,20.08,16.30 415 | 45.74610,0.00,18.100,0,0.6930,4.5190,100.00,1.6582,24,666.0,20.20,88.27,36.98,7.00 416 | 18.08460,0.00,18.100,0,0.6790,6.4340,100.00,1.8347,24,666.0,20.20,27.25,29.05,7.20 417 | 10.83420,0.00,18.100,0,0.6790,6.7820,90.80,1.8195,24,666.0,20.20,21.57,25.79,7.50 418 | 25.94060,0.00,18.100,0,0.6790,5.3040,89.10,1.6475,24,666.0,20.20,127.36,26.64,10.40 419 | 73.53410,0.00,18.100,0,0.6790,5.9570,100.00,1.8026,24,666.0,20.20,16.45,20.62,8.80 420 | 11.81230,0.00,18.100,0,0.7180,6.8240,76.50,1.7940,24,666.0,20.20,48.45,22.74,8.40 421 | 11.08740,0.00,18.100,0,0.7180,6.4110,100.00,1.8589,24,666.0,20.20,318.75,15.02,16.70 422 | 7.02259,0.00,18.100,0,0.7180,6.0060,95.30,1.8746,24,666.0,20.20,319.98,15.70,14.20 423 | 12.04820,0.00,18.100,0,0.6140,5.6480,87.60,1.9512,24,666.0,20.20,291.55,14.10,20.80 424 | 7.05042,0.00,18.100,0,0.6140,6.1030,85.10,2.0218,24,666.0,20.20,2.52,23.29,13.40 425 | 8.79212,0.00,18.100,0,0.5840,5.5650,70.60,2.0635,24,666.0,20.20,3.65,17.16,11.70 426 | 15.86030,0.00,18.100,0,0.6790,5.8960,95.40,1.9096,24,666.0,20.20,7.68,24.39,8.30 427 | 12.24720,0.00,18.100,0,0.5840,5.8370,59.70,1.9976,24,666.0,20.20,24.65,15.69,10.20 428 | 37.66190,0.00,18.100,0,0.6790,6.2020,78.70,1.8629,24,666.0,20.20,18.82,14.52,10.90 429 | 7.36711,0.00,18.100,0,0.6790,6.1930,78.10,1.9356,24,666.0,20.20,96.73,21.52,11.00 430 | 9.33889,0.00,18.100,0,0.6790,6.3800,95.60,1.9682,24,666.0,20.20,60.72,24.08,9.50 431 | 8.49213,0.00,18.100,0,0.5840,6.3480,86.10,2.0527,24,666.0,20.20,83.45,17.64,14.50 432 | 10.06230,0.00,18.100,0,0.5840,6.8330,94.30,2.0882,24,666.0,20.20,81.33,19.69,14.10 433 | 6.44405,0.00,18.100,0,0.5840,6.4250,74.80,2.2004,24,666.0,20.20,97.95,12.03,16.10 434 | 5.58107,0.00,18.100,0,0.7130,6.4360,87.90,2.3158,24,666.0,20.20,100.19,16.22,14.30 435 | 13.91340,0.00,18.100,0,0.7130,6.2080,95.00,2.2222,24,666.0,20.20,100.63,15.17,11.70 436 | 11.16040,0.00,18.100,0,0.7400,6.6290,94.60,2.1247,24,666.0,20.20,109.85,23.27,13.40 437 | 14.42080,0.00,18.100,0,0.7400,6.4610,93.30,2.0026,24,666.0,20.20,27.49,18.05,9.60 438 | 15.17720,0.00,18.100,0,0.7400,6.1520,100.00,1.9142,24,666.0,20.20,9.32,26.45,8.70 439 | 13.67810,0.00,18.100,0,0.7400,5.9350,87.90,1.8206,24,666.0,20.20,68.95,34.02,8.40 440 | 9.39063,0.00,18.100,0,0.7400,5.6270,93.90,1.8172,24,666.0,20.20,396.90,22.88,12.80 441 | 22.05110,0.00,18.100,0,0.7400,5.8180,92.40,1.8662,24,666.0,20.20,391.45,22.11,10.50 442 | 9.72418,0.00,18.100,0,0.7400,6.4060,97.20,2.0651,24,666.0,20.20,385.96,19.52,17.10 443 | 5.66637,0.00,18.100,0,0.7400,6.2190,100.00,2.0048,24,666.0,20.20,395.69,16.59,18.40 444 | 9.96654,0.00,18.100,0,0.7400,6.4850,100.00,1.9784,24,666.0,20.20,386.73,18.85,15.40 445 | 12.80230,0.00,18.100,0,0.7400,5.8540,96.60,1.8956,24,666.0,20.20,240.52,23.79,10.80 446 | 10.67180,0.00,18.100,0,0.7400,6.4590,94.80,1.9879,24,666.0,20.20,43.06,23.98,11.80 447 | 6.28807,0.00,18.100,0,0.7400,6.3410,96.40,2.0720,24,666.0,20.20,318.01,17.79,14.90 448 | 9.92485,0.00,18.100,0,0.7400,6.2510,96.60,2.1980,24,666.0,20.20,388.52,16.44,12.60 449 | 9.32909,0.00,18.100,0,0.7130,6.1850,98.70,2.2616,24,666.0,20.20,396.90,18.13,14.10 450 | 7.52601,0.00,18.100,0,0.7130,6.4170,98.30,2.1850,24,666.0,20.20,304.21,19.31,13.00 451 | 6.71772,0.00,18.100,0,0.7130,6.7490,92.60,2.3236,24,666.0,20.20,0.32,17.44,13.40 452 | 5.44114,0.00,18.100,0,0.7130,6.6550,98.20,2.3552,24,666.0,20.20,355.29,17.73,15.20 453 | 5.09017,0.00,18.100,0,0.7130,6.2970,91.80,2.3682,24,666.0,20.20,385.09,17.27,16.10 454 | 8.24809,0.00,18.100,0,0.7130,7.3930,99.30,2.4527,24,666.0,20.20,375.87,16.74,17.80 455 | 9.51363,0.00,18.100,0,0.7130,6.7280,94.10,2.4961,24,666.0,20.20,6.68,18.71,14.90 456 | 4.75237,0.00,18.100,0,0.7130,6.5250,86.50,2.4358,24,666.0,20.20,50.92,18.13,14.10 457 | 4.66883,0.00,18.100,0,0.7130,5.9760,87.90,2.5806,24,666.0,20.20,10.48,19.01,12.70 458 | 8.20058,0.00,18.100,0,0.7130,5.9360,80.30,2.7792,24,666.0,20.20,3.50,16.94,13.50 459 | 7.75223,0.00,18.100,0,0.7130,6.3010,83.70,2.7831,24,666.0,20.20,272.21,16.23,14.90 460 | 6.80117,0.00,18.100,0,0.7130,6.0810,84.40,2.7175,24,666.0,20.20,396.90,14.70,20.00 461 | 4.81213,0.00,18.100,0,0.7130,6.7010,90.00,2.5975,24,666.0,20.20,255.23,16.42,16.40 462 | 3.69311,0.00,18.100,0,0.7130,6.3760,88.40,2.5671,24,666.0,20.20,391.43,14.65,17.70 463 | 6.65492,0.00,18.100,0,0.7130,6.3170,83.00,2.7344,24,666.0,20.20,396.90,13.99,19.50 464 | 5.82115,0.00,18.100,0,0.7130,6.5130,89.90,2.8016,24,666.0,20.20,393.82,10.29,20.20 465 | 7.83932,0.00,18.100,0,0.6550,6.2090,65.40,2.9634,24,666.0,20.20,396.90,13.22,21.40 466 | 3.16360,0.00,18.100,0,0.6550,5.7590,48.20,3.0665,24,666.0,20.20,334.40,14.13,19.90 467 | 3.77498,0.00,18.100,0,0.6550,5.9520,84.70,2.8715,24,666.0,20.20,22.01,17.15,19.00 468 | 4.42228,0.00,18.100,0,0.5840,6.0030,94.50,2.5403,24,666.0,20.20,331.29,21.32,19.10 469 | 15.57570,0.00,18.100,0,0.5800,5.9260,71.00,2.9084,24,666.0,20.20,368.74,18.13,19.10 470 | 13.07510,0.00,18.100,0,0.5800,5.7130,56.70,2.8237,24,666.0,20.20,396.90,14.76,20.10 471 | 4.34879,0.00,18.100,0,0.5800,6.1670,84.00,3.0334,24,666.0,20.20,396.90,16.29,19.90 472 | 4.03841,0.00,18.100,0,0.5320,6.2290,90.70,3.0993,24,666.0,20.20,395.33,12.87,19.60 473 | 3.56868,0.00,18.100,0,0.5800,6.4370,75.00,2.8965,24,666.0,20.20,393.37,14.36,23.20 474 | 4.64689,0.00,18.100,0,0.6140,6.9800,67.60,2.5329,24,666.0,20.20,374.68,11.66,29.80 475 | 8.05579,0.00,18.100,0,0.5840,5.4270,95.40,2.4298,24,666.0,20.20,352.58,18.14,13.80 476 | 6.39312,0.00,18.100,0,0.5840,6.1620,97.40,2.2060,24,666.0,20.20,302.76,24.10,13.30 477 | 4.87141,0.00,18.100,0,0.6140,6.4840,93.60,2.3053,24,666.0,20.20,396.21,18.68,16.70 478 | 15.02340,0.00,18.100,0,0.6140,5.3040,97.30,2.1007,24,666.0,20.20,349.48,24.91,12.00 479 | 10.23300,0.00,18.100,0,0.6140,6.1850,96.70,2.1705,24,666.0,20.20,379.70,18.03,14.60 480 | 14.33370,0.00,18.100,0,0.6140,6.2290,88.00,1.9512,24,666.0,20.20,383.32,13.11,21.40 481 | 5.82401,0.00,18.100,0,0.5320,6.2420,64.70,3.4242,24,666.0,20.20,396.90,10.74,23.00 482 | 5.70818,0.00,18.100,0,0.5320,6.7500,74.90,3.3317,24,666.0,20.20,393.07,7.74,23.70 483 | 5.73116,0.00,18.100,0,0.5320,7.0610,77.00,3.4106,24,666.0,20.20,395.28,7.01,25.00 484 | 2.81838,0.00,18.100,0,0.5320,5.7620,40.30,4.0983,24,666.0,20.20,392.92,10.42,21.80 485 | 2.37857,0.00,18.100,0,0.5830,5.8710,41.90,3.7240,24,666.0,20.20,370.73,13.34,20.60 486 | 3.67367,0.00,18.100,0,0.5830,6.3120,51.90,3.9917,24,666.0,20.20,388.62,10.58,21.20 487 | 5.69175,0.00,18.100,0,0.5830,6.1140,79.80,3.5459,24,666.0,20.20,392.68,14.98,19.10 488 | 4.83567,0.00,18.100,0,0.5830,5.9050,53.20,3.1523,24,666.0,20.20,388.22,11.45,20.60 489 | 0.15086,0.00,27.740,0,0.6090,5.4540,92.70,1.8209,4,711.0,20.10,395.09,18.06,15.20 490 | 0.18337,0.00,27.740,0,0.6090,5.4140,98.30,1.7554,4,711.0,20.10,344.05,23.97,7.00 491 | 0.20746,0.00,27.740,0,0.6090,5.0930,98.00,1.8226,4,711.0,20.10,318.43,29.68,8.10 492 | 0.10574,0.00,27.740,0,0.6090,5.9830,98.80,1.8681,4,711.0,20.10,390.11,18.07,13.60 493 | 0.11132,0.00,27.740,0,0.6090,5.9830,83.50,2.1099,4,711.0,20.10,396.90,13.35,20.10 494 | 0.17331,0.00,9.690,0,0.5850,5.7070,54.00,2.3817,6,391.0,19.20,396.90,12.01,21.80 495 | 0.27957,0.00,9.690,0,0.5850,5.9260,42.60,2.3817,6,391.0,19.20,396.90,13.59,24.50 496 | 0.17899,0.00,9.690,0,0.5850,5.6700,28.80,2.7986,6,391.0,19.20,393.29,17.60,23.10 497 | 0.28960,0.00,9.690,0,0.5850,5.3900,72.90,2.7986,6,391.0,19.20,396.90,21.14,19.70 498 | 0.26838,0.00,9.690,0,0.5850,5.7940,70.60,2.8927,6,391.0,19.20,396.90,14.10,18.30 499 | 0.23912,0.00,9.690,0,0.5850,6.0190,65.30,2.4091,6,391.0,19.20,396.90,12.92,21.20 500 | 0.17783,0.00,9.690,0,0.5850,5.5690,73.50,2.3999,6,391.0,19.20,395.77,15.10,17.50 501 | 0.22438,0.00,9.690,0,0.5850,6.0270,79.70,2.4982,6,391.0,19.20,396.90,14.33,16.80 502 | 0.06263,0.00,11.930,0,0.5730,6.5930,69.10,2.4786,1,273.0,21.00,391.99,9.67,22.40 503 | 0.04527,0.00,11.930,0,0.5730,6.1200,76.70,2.2875,1,273.0,21.00,396.90,9.08,20.60 504 | 0.06076,0.00,11.930,0,0.5730,6.9760,91.00,2.1675,1,273.0,21.00,396.90,5.64,23.90 505 | 0.10959,0.00,11.930,0,0.5730,6.7940,89.30,2.3889,1,273.0,21.00,393.45,6.48,22.00 506 | 0.04741,0.00,11.930,0,0.5730,6.0300,80.80,2.5050,1,273.0,21.00,396.90,7.88,11.90 507 | --------------------------------------------------------------------------------