├── .gitattributes
├── Data PreProcessing
    ├── Data PreProcessing.ipynb
    ├── Students_marks.csv
    └── data.csv
├── Frequency Distribution
    └── Frequency Distribution.ipynb
├── Inferential Stats
    ├── 1 way Anova.ipynb
    ├── 2 way Anova.ipynb
    ├── Central Limit Theorem.ipynb
    ├── Chi square.ipynb
    ├── Confidence Interval.ipynb
    ├── T-Test.ipynb
    ├── Z test.ipynb
    └── sampling.ipynb
├── Measure of Central Tendency
    └── Measure of central Tendency.ipynb
├── Measure of Dispersion
    └── Measure of Dispersion.ipynb
├── Measure of Shape
    └── Measure of Shape.ipynb
└── README.md


/.gitattributes:
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1 | # Auto detect text files and perform LF normalization
2 | * text=auto
3 | 


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/Data PreProcessing/Data PreProcessing.ipynb:
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "id": "6c811ddb",
  7 |    "metadata": {},
  8 |    "outputs": [],
  9 |    "source": [
 10 |     "import numpy as np\n",
 11 |     "import pandas as pd\n",
 12 |     "from matplotlib import pyplot as plt"
 13 |    ]
 14 |   },
 15 |   {
 16 |    "cell_type": "code",
 17 |    "execution_count": 9,
 18 |    "id": "278a0f03",
 19 |    "metadata": {},
 20 |    "outputs": [],
 21 |    "source": [
 22 |     "data = pd.read_csv(\"Students_marks.csv\")"
 23 |    ]
 24 |   },
 25 |   {
 26 |    "cell_type": "code",
 27 |    "execution_count": 10,
 28 |    "id": "5ab8e338",
 29 |    "metadata": {},
 30 |    "outputs": [
 31 |     {
 32 |      "data": {
 33 |       "text/html": [
 34 |        "<div>\n",
 35 |        "<style scoped>\n",
 36 |        "    .dataframe tbody tr th:only-of-type {\n",
 37 |        "        vertical-align: middle;\n",
 38 |        "    }\n",
 39 |        "\n",
 40 |        "    .dataframe tbody tr th {\n",
 41 |        "        vertical-align: top;\n",
 42 |        "    }\n",
 43 |        "\n",
 44 |        "    .dataframe thead th {\n",
 45 |        "        text-align: right;\n",
 46 |        "    }\n",
 47 |        "</style>\n",
 48 |        "<table border=\"1\" class=\"dataframe\">\n",
 49 |        "  <thead>\n",
 50 |        "    <tr style=\"text-align: right;\">\n",
 51 |        "      <th></th>\n",
 52 |        "      <th>Roll No.</th>\n",
 53 |        "      <th>Name</th>\n",
 54 |        "      <th>Data Structure</th>\n",
 55 |        "      <th>Mathematics</th>\n",
 56 |        "      <th>Operating System</th>\n",
 57 |        "    </tr>\n",
 58 |        "  </thead>\n",
 59 |        "  <tbody>\n",
 60 |        "    <tr>\n",
 61 |        "      <th>0</th>\n",
 62 |        "      <td>1</td>\n",
 63 |        "      <td>Avneet</td>\n",
 64 |        "      <td>25</td>\n",
 65 |        "      <td>19</td>\n",
 66 |        "      <td>17</td>\n",
 67 |        "    </tr>\n",
 68 |        "    <tr>\n",
 69 |        "      <th>1</th>\n",
 70 |        "      <td>2</td>\n",
 71 |        "      <td>Jay</td>\n",
 72 |        "      <td>23</td>\n",
 73 |        "      <td>21</td>\n",
 74 |        "      <td>14</td>\n",
 75 |        "    </tr>\n",
 76 |        "    <tr>\n",
 77 |        "      <th>2</th>\n",
 78 |        "      <td>3</td>\n",
 79 |        "      <td>Sadaf</td>\n",
 80 |        "      <td>28</td>\n",
 81 |        "      <td>23</td>\n",
 82 |        "      <td>21</td>\n",
 83 |        "    </tr>\n",
 84 |        "    <tr>\n",
 85 |        "      <th>3</th>\n",
 86 |        "      <td>4</td>\n",
 87 |        "      <td>Prakash</td>\n",
 88 |        "      <td>21</td>\n",
 89 |        "      <td>27</td>\n",
 90 |        "      <td>25</td>\n",
 91 |        "    </tr>\n",
 92 |        "    <tr>\n",
 93 |        "      <th>4</th>\n",
 94 |        "      <td>5</td>\n",
 95 |        "      <td>Sarita</td>\n",
 96 |        "      <td>23</td>\n",
 97 |        "      <td>22</td>\n",
 98 |        "      <td>9</td>\n",
 99 |        "    </tr>\n",
100 |        "  </tbody>\n",
101 |        "</table>\n",
102 |        "</div>"
103 |       ],
104 |       "text/plain": [
105 |        "   Roll No.     Name  Data Structure  Mathematics  Operating System\n",
106 |        "0         1  Avneet               25           19                17\n",
107 |        "1         2      Jay              23           21                14\n",
108 |        "2         3   Sadaf               28           23                21\n",
109 |        "3         4  Prakash              21           27                25\n",
110 |        "4         5   Sarita              23           22                 9"
111 |       ]
112 |      },
113 |      "execution_count": 10,
114 |      "metadata": {},
115 |      "output_type": "execute_result"
116 |     }
117 |    ],
118 |    "source": [
119 |     "data.head()"
120 |    ]
121 |   },
122 |   {
123 |    "cell_type": "code",
124 |    "execution_count": 11,
125 |    "id": "8a521628",
126 |    "metadata": {},
127 |    "outputs": [
128 |     {
129 |      "data": {
130 |       "text/html": [
131 |        "<div>\n",
132 |        "<style scoped>\n",
133 |        "    .dataframe tbody tr th:only-of-type {\n",
134 |        "        vertical-align: middle;\n",
135 |        "    }\n",
136 |        "\n",
137 |        "    .dataframe tbody tr th {\n",
138 |        "        vertical-align: top;\n",
139 |        "    }\n",
140 |        "\n",
141 |        "    .dataframe thead th {\n",
142 |        "        text-align: right;\n",
143 |        "    }\n",
144 |        "</style>\n",
145 |        "<table border=\"1\" class=\"dataframe\">\n",
146 |        "  <thead>\n",
147 |        "    <tr style=\"text-align: right;\">\n",
148 |        "      <th></th>\n",
149 |        "      <th>Roll No.</th>\n",
150 |        "      <th>Data Structure</th>\n",
151 |        "      <th>Mathematics</th>\n",
152 |        "      <th>Operating System</th>\n",
153 |        "    </tr>\n",
154 |        "  </thead>\n",
155 |        "  <tbody>\n",
156 |        "    <tr>\n",
157 |        "      <th>count</th>\n",
158 |        "      <td>10.00000</td>\n",
159 |        "      <td>10.000000</td>\n",
160 |        "      <td>10.00000</td>\n",
161 |        "      <td>10.00000</td>\n",
162 |        "    </tr>\n",
163 |        "    <tr>\n",
164 |        "      <th>mean</th>\n",
165 |        "      <td>5.50000</td>\n",
166 |        "      <td>40.700000</td>\n",
167 |        "      <td>23.10000</td>\n",
168 |        "      <td>19.90000</td>\n",
169 |        "    </tr>\n",
170 |        "    <tr>\n",
171 |        "      <th>std</th>\n",
172 |        "      <td>3.02765</td>\n",
173 |        "      <td>52.493492</td>\n",
174 |        "      <td>3.60401</td>\n",
175 |        "      <td>5.95259</td>\n",
176 |        "    </tr>\n",
177 |        "    <tr>\n",
178 |        "      <th>min</th>\n",
179 |        "      <td>1.00000</td>\n",
180 |        "      <td>21.000000</td>\n",
181 |        "      <td>18.00000</td>\n",
182 |        "      <td>9.00000</td>\n",
183 |        "    </tr>\n",
184 |        "    <tr>\n",
185 |        "      <th>25%</th>\n",
186 |        "      <td>3.25000</td>\n",
187 |        "      <td>23.000000</td>\n",
188 |        "      <td>21.25000</td>\n",
189 |        "      <td>15.50000</td>\n",
190 |        "    </tr>\n",
191 |        "    <tr>\n",
192 |        "      <th>50%</th>\n",
193 |        "      <td>5.50000</td>\n",
194 |        "      <td>24.000000</td>\n",
195 |        "      <td>22.50000</td>\n",
196 |        "      <td>21.50000</td>\n",
197 |        "    </tr>\n",
198 |        "    <tr>\n",
199 |        "      <th>75%</th>\n",
200 |        "      <td>7.75000</td>\n",
201 |        "      <td>25.750000</td>\n",
202 |        "      <td>24.75000</td>\n",
203 |        "      <td>24.50000</td>\n",
204 |        "    </tr>\n",
205 |        "    <tr>\n",
206 |        "      <th>max</th>\n",
207 |        "      <td>10.00000</td>\n",
208 |        "      <td>190.000000</td>\n",
209 |        "      <td>30.00000</td>\n",
210 |        "      <td>28.00000</td>\n",
211 |        "    </tr>\n",
212 |        "  </tbody>\n",
213 |        "</table>\n",
214 |        "</div>"
215 |       ],
216 |       "text/plain": [
217 |        "       Roll No.  Data Structure  Mathematics  Operating System\n",
218 |        "count  10.00000       10.000000     10.00000          10.00000\n",
219 |        "mean    5.50000       40.700000     23.10000          19.90000\n",
220 |        "std     3.02765       52.493492      3.60401           5.95259\n",
221 |        "min     1.00000       21.000000     18.00000           9.00000\n",
222 |        "25%     3.25000       23.000000     21.25000          15.50000\n",
223 |        "50%     5.50000       24.000000     22.50000          21.50000\n",
224 |        "75%     7.75000       25.750000     24.75000          24.50000\n",
225 |        "max    10.00000      190.000000     30.00000          28.00000"
226 |       ]
227 |      },
228 |      "execution_count": 11,
229 |      "metadata": {},
230 |      "output_type": "execute_result"
231 |     }
232 |    ],
233 |    "source": [
234 |     "data.describe()"
235 |    ]
236 |   },
237 |   {
238 |    "cell_type": "code",
239 |    "execution_count": 12,
240 |    "id": "6c586532",
241 |    "metadata": {},
242 |    "outputs": [
243 |     {
244 |      "data": {
245 |       "text/html": [
246 |        "<div>\n",
247 |        "<style scoped>\n",
248 |        "    .dataframe tbody tr th:only-of-type {\n",
249 |        "        vertical-align: middle;\n",
250 |        "    }\n",
251 |        "\n",
252 |        "    .dataframe tbody tr th {\n",
253 |        "        vertical-align: top;\n",
254 |        "    }\n",
255 |        "\n",
256 |        "    .dataframe thead th {\n",
257 |        "        text-align: right;\n",
258 |        "    }\n",
259 |        "</style>\n",
260 |        "<table border=\"1\" class=\"dataframe\">\n",
261 |        "  <thead>\n",
262 |        "    <tr style=\"text-align: right;\">\n",
263 |        "      <th></th>\n",
264 |        "      <th>Roll No.</th>\n",
265 |        "      <th>Name</th>\n",
266 |        "      <th>Data Structure</th>\n",
267 |        "      <th>Mathematics</th>\n",
268 |        "      <th>Operating System</th>\n",
269 |        "    </tr>\n",
270 |        "  </thead>\n",
271 |        "  <tbody>\n",
272 |        "    <tr>\n",
273 |        "      <th>0</th>\n",
274 |        "      <td>1</td>\n",
275 |        "      <td>Avneet</td>\n",
276 |        "      <td>25</td>\n",
277 |        "      <td>19</td>\n",
278 |        "      <td>17</td>\n",
279 |        "    </tr>\n",
280 |        "    <tr>\n",
281 |        "      <th>1</th>\n",
282 |        "      <td>2</td>\n",
283 |        "      <td>Jay</td>\n",
284 |        "      <td>23</td>\n",
285 |        "      <td>21</td>\n",
286 |        "      <td>14</td>\n",
287 |        "    </tr>\n",
288 |        "    <tr>\n",
289 |        "      <th>2</th>\n",
290 |        "      <td>3</td>\n",
291 |        "      <td>Sadaf</td>\n",
292 |        "      <td>28</td>\n",
293 |        "      <td>23</td>\n",
294 |        "      <td>21</td>\n",
295 |        "    </tr>\n",
296 |        "    <tr>\n",
297 |        "      <th>3</th>\n",
298 |        "      <td>4</td>\n",
299 |        "      <td>Prakash</td>\n",
300 |        "      <td>21</td>\n",
301 |        "      <td>27</td>\n",
302 |        "      <td>25</td>\n",
303 |        "    </tr>\n",
304 |        "    <tr>\n",
305 |        "      <th>4</th>\n",
306 |        "      <td>5</td>\n",
307 |        "      <td>Sarita</td>\n",
308 |        "      <td>23</td>\n",
309 |        "      <td>22</td>\n",
310 |        "      <td>9</td>\n",
311 |        "    </tr>\n",
312 |        "    <tr>\n",
313 |        "      <th>5</th>\n",
314 |        "      <td>6</td>\n",
315 |        "      <td>Vasudha</td>\n",
316 |        "      <td>190</td>\n",
317 |        "      <td>30</td>\n",
318 |        "      <td>28</td>\n",
319 |        "    </tr>\n",
320 |        "    <tr>\n",
321 |        "      <th>6</th>\n",
322 |        "      <td>7</td>\n",
323 |        "      <td>Ankur</td>\n",
324 |        "      <td>24</td>\n",
325 |        "      <td>24</td>\n",
326 |        "      <td>22</td>\n",
327 |        "    </tr>\n",
328 |        "    <tr>\n",
329 |        "      <th>7</th>\n",
330 |        "      <td>8</td>\n",
331 |        "      <td>Laksh</td>\n",
332 |        "      <td>26</td>\n",
333 |        "      <td>25</td>\n",
334 |        "      <td>23</td>\n",
335 |        "    </tr>\n",
336 |        "    <tr>\n",
337 |        "      <th>8</th>\n",
338 |        "      <td>9</td>\n",
339 |        "      <td>Aman</td>\n",
340 |        "      <td>23</td>\n",
341 |        "      <td>18</td>\n",
342 |        "      <td>25</td>\n",
343 |        "    </tr>\n",
344 |        "    <tr>\n",
345 |        "      <th>9</th>\n",
346 |        "      <td>10</td>\n",
347 |        "      <td>Rajat</td>\n",
348 |        "      <td>24</td>\n",
349 |        "      <td>22</td>\n",
350 |        "      <td>15</td>\n",
351 |        "    </tr>\n",
352 |        "  </tbody>\n",
353 |        "</table>\n",
354 |        "</div>"
355 |       ],
356 |       "text/plain": [
357 |        "   Roll No.     Name  Data Structure  Mathematics  Operating System\n",
358 |        "0         1  Avneet               25           19                17\n",
359 |        "1         2      Jay              23           21                14\n",
360 |        "2         3   Sadaf               28           23                21\n",
361 |        "3         4  Prakash              21           27                25\n",
362 |        "4         5   Sarita              23           22                 9\n",
363 |        "5         6  Vasudha             190           30                28\n",
364 |        "6         7    Ankur              24           24                22\n",
365 |        "7         8    Laksh              26           25                23\n",
366 |        "8         9     Aman              23           18                25\n",
367 |        "9        10    Rajat              24           22                15"
368 |       ]
369 |      },
370 |      "execution_count": 12,
371 |      "metadata": {},
372 |      "output_type": "execute_result"
373 |     }
374 |    ],
375 |    "source": [
376 |     "data"
377 |    ]
378 |   },
379 |   {
380 |    "cell_type": "code",
381 |    "execution_count": 13,
382 |    "id": "333311d2",
383 |    "metadata": {},
384 |    "outputs": [],
385 |    "source": [
386 |     "data.drop(index = 5, axis = 1, inplace = True)"
387 |    ]
388 |   },
389 |   {
390 |    "cell_type": "code",
391 |    "execution_count": 14,
392 |    "id": "854dc7d4",
393 |    "metadata": {},
394 |    "outputs": [
395 |     {
396 |      "data": {
397 |       "text/html": [
398 |        "<div>\n",
399 |        "<style scoped>\n",
400 |        "    .dataframe tbody tr th:only-of-type {\n",
401 |        "        vertical-align: middle;\n",
402 |        "    }\n",
403 |        "\n",
404 |        "    .dataframe tbody tr th {\n",
405 |        "        vertical-align: top;\n",
406 |        "    }\n",
407 |        "\n",
408 |        "    .dataframe thead th {\n",
409 |        "        text-align: right;\n",
410 |        "    }\n",
411 |        "</style>\n",
412 |        "<table border=\"1\" class=\"dataframe\">\n",
413 |        "  <thead>\n",
414 |        "    <tr style=\"text-align: right;\">\n",
415 |        "      <th></th>\n",
416 |        "      <th>Roll No.</th>\n",
417 |        "      <th>Data Structure</th>\n",
418 |        "      <th>Mathematics</th>\n",
419 |        "      <th>Operating System</th>\n",
420 |        "    </tr>\n",
421 |        "  </thead>\n",
422 |        "  <tbody>\n",
423 |        "    <tr>\n",
424 |        "      <th>count</th>\n",
425 |        "      <td>9.000000</td>\n",
426 |        "      <td>9.000000</td>\n",
427 |        "      <td>9.000000</td>\n",
428 |        "      <td>9.000000</td>\n",
429 |        "    </tr>\n",
430 |        "    <tr>\n",
431 |        "      <th>mean</th>\n",
432 |        "      <td>5.444444</td>\n",
433 |        "      <td>24.111111</td>\n",
434 |        "      <td>22.333333</td>\n",
435 |        "      <td>19.000000</td>\n",
436 |        "    </tr>\n",
437 |        "    <tr>\n",
438 |        "      <th>std</th>\n",
439 |        "      <td>3.205897</td>\n",
440 |        "      <td>2.027588</td>\n",
441 |        "      <td>2.828427</td>\n",
442 |        "      <td>5.545268</td>\n",
443 |        "    </tr>\n",
444 |        "    <tr>\n",
445 |        "      <th>min</th>\n",
446 |        "      <td>1.000000</td>\n",
447 |        "      <td>21.000000</td>\n",
448 |        "      <td>18.000000</td>\n",
449 |        "      <td>9.000000</td>\n",
450 |        "    </tr>\n",
451 |        "    <tr>\n",
452 |        "      <th>25%</th>\n",
453 |        "      <td>3.000000</td>\n",
454 |        "      <td>23.000000</td>\n",
455 |        "      <td>21.000000</td>\n",
456 |        "      <td>15.000000</td>\n",
457 |        "    </tr>\n",
458 |        "    <tr>\n",
459 |        "      <th>50%</th>\n",
460 |        "      <td>5.000000</td>\n",
461 |        "      <td>24.000000</td>\n",
462 |        "      <td>22.000000</td>\n",
463 |        "      <td>21.000000</td>\n",
464 |        "    </tr>\n",
465 |        "    <tr>\n",
466 |        "      <th>75%</th>\n",
467 |        "      <td>8.000000</td>\n",
468 |        "      <td>25.000000</td>\n",
469 |        "      <td>24.000000</td>\n",
470 |        "      <td>23.000000</td>\n",
471 |        "    </tr>\n",
472 |        "    <tr>\n",
473 |        "      <th>max</th>\n",
474 |        "      <td>10.000000</td>\n",
475 |        "      <td>28.000000</td>\n",
476 |        "      <td>27.000000</td>\n",
477 |        "      <td>25.000000</td>\n",
478 |        "    </tr>\n",
479 |        "  </tbody>\n",
480 |        "</table>\n",
481 |        "</div>"
482 |       ],
483 |       "text/plain": [
484 |        "        Roll No.  Data Structure  Mathematics  Operating System\n",
485 |        "count   9.000000        9.000000     9.000000          9.000000\n",
486 |        "mean    5.444444       24.111111    22.333333         19.000000\n",
487 |        "std     3.205897        2.027588     2.828427          5.545268\n",
488 |        "min     1.000000       21.000000    18.000000          9.000000\n",
489 |        "25%     3.000000       23.000000    21.000000         15.000000\n",
490 |        "50%     5.000000       24.000000    22.000000         21.000000\n",
491 |        "75%     8.000000       25.000000    24.000000         23.000000\n",
492 |        "max    10.000000       28.000000    27.000000         25.000000"
493 |       ]
494 |      },
495 |      "execution_count": 14,
496 |      "metadata": {},
497 |      "output_type": "execute_result"
498 |     }
499 |    ],
500 |    "source": [
501 |     "data.describe()"
502 |    ]
503 |   },
504 |   {
505 |    "cell_type": "code",
506 |    "execution_count": 15,
507 |    "id": "7beb46ac",
508 |    "metadata": {},
509 |    "outputs": [
510 |     {
511 |      "data": {
512 |       "image/png": 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\n",
513 |       "text/plain": [
514 |        "<Figure size 640x480 with 1 Axes>"
515 |       ]
516 |      },
517 |      "metadata": {},
518 |      "output_type": "display_data"
519 |     }
520 |    ],
521 |    "source": [
522 |     "plt.plot(data[\"Name\"], data[\"Data Structure\"])\n",
523 |     "plt.title(\"Marks of students in Data Structure\")\n",
524 |     "plt.xlabel(\"Students Name\")\n",
525 |     "plt.ylabel(\"Students Marks\")\n",
526 |     "plt.show()"
527 |    ]
528 |   },
529 |   {
530 |    "cell_type": "code",
531 |    "execution_count": null,
532 |    "id": "11598e89",
533 |    "metadata": {},
534 |    "outputs": [],
535 |    "source": []
536 |   }
537 |  ],
538 |  "metadata": {
539 |   "kernelspec": {
540 |    "display_name": "Python 3 (ipykernel)",
541 |    "language": "python",
542 |    "name": "python3"
543 |   },
544 |   "language_info": {
545 |    "codemirror_mode": {
546 |     "name": "ipython",
547 |     "version": 3
548 |    },
549 |    "file_extension": ".py",
550 |    "mimetype": "text/x-python",
551 |    "name": "python",
552 |    "nbconvert_exporter": "python",
553 |    "pygments_lexer": "ipython3",
554 |    "version": "3.11.1"
555 |   }
556 |  },
557 |  "nbformat": 4,
558 |  "nbformat_minor": 5
559 | }
560 | 


--------------------------------------------------------------------------------
/Data PreProcessing/Students_marks.csv:
--------------------------------------------------------------------------------
 1 | Roll No.,Name,Gender,Data Structure,Mathematics,Operating System,Average marks,Ranking
 2 | 1,Avneet ,F,25,19,17,20.33,Good
 3 | 2,Jay,M,23,21,14,19.33,Okay
 4 | 3,Sadaf ,F,28,23,21,24,Good
 5 | 4,Prakash,M,21,27,28,25.33,Very Good
 6 | 5,Sarita,F,23,22,9,18,Okay
 7 | 6,Vasudha,F,190,30,28,82.66,Very Good
 8 | 7,Ankur,M,24,24,22,23.33,Good
 9 | 8,Laksh,M,26,25,27,26,Very Good
10 | 9,Aman,M,23,18,25,22,Good
11 | 10,Rajat,M,24,22,15,20.33,Good
12 | 


--------------------------------------------------------------------------------
/Inferential Stats/1 way Anova.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 2,
  6 |    "id": "0b714302",
  7 |    "metadata": {},
  8 |    "outputs": [],
  9 |    "source": [
 10 |     "from scipy.stats import f_oneway"
 11 |    ]
 12 |   },
 13 |   {
 14 |    "cell_type": "markdown",
 15 |    "id": "d478a556",
 16 |    "metadata": {},
 17 |    "source": [
 18 |     "Performance when each of the engine with different types of oil is applied"
 19 |    ]
 20 |   },
 21 |   {
 22 |    "cell_type": "code",
 23 |    "execution_count": 3,
 24 |    "id": "404662ec",
 25 |    "metadata": {},
 26 |    "outputs": [],
 27 |    "source": [
 28 |     "performance1 = [89, 89, 88, 78, 79]\n",
 29 |     "performance2 = [93, 92, 94, 89, 88]\n",
 30 |     "performance3 = [89, 88, 89, 93, 90]\n",
 31 |     "performance4 = [81, 78, 81, 92, 82]"
 32 |    ]
 33 |   },
 34 |   {
 35 |    "cell_type": "code",
 36 |    "execution_count": 4,
 37 |    "id": "7bcd5b60",
 38 |    "metadata": {},
 39 |    "outputs": [],
 40 |    "source": [
 41 |     "alpha = 0.05"
 42 |    ]
 43 |   },
 44 |   {
 45 |    "cell_type": "code",
 46 |    "execution_count": 5,
 47 |    "id": "97b31724",
 48 |    "metadata": {},
 49 |    "outputs": [],
 50 |    "source": [
 51 |     "stat, pvalue = f_oneway(performance1, performance2, performance3, performance4)"
 52 |    ]
 53 |   },
 54 |   {
 55 |    "cell_type": "code",
 56 |    "execution_count": 6,
 57 |    "id": "4fc90cd7",
 58 |    "metadata": {},
 59 |    "outputs": [
 60 |     {
 61 |      "data": {
 62 |       "text/plain": [
 63 |        "(4.625000000000002, 0.01633645983978022)"
 64 |       ]
 65 |      },
 66 |      "execution_count": 6,
 67 |      "metadata": {},
 68 |      "output_type": "execute_result"
 69 |     }
 70 |    ],
 71 |    "source": [
 72 |     "stat, pvalue"
 73 |    ]
 74 |   },
 75 |   {
 76 |    "cell_type": "code",
 77 |    "execution_count": 7,
 78 |    "id": "10af033d",
 79 |    "metadata": {},
 80 |    "outputs": [
 81 |     {
 82 |      "name": "stdout",
 83 |      "output_type": "stream",
 84 |      "text": [
 85 |       "Reject the null Hypothesis\n"
 86 |      ]
 87 |     }
 88 |    ],
 89 |    "source": [
 90 |     "if(pvalue<alpha):\n",
 91 |     "    print(\"Reject the null Hypothesis\")\n",
 92 |     "else:\n",
 93 |     "    print(\"Failed to reject the null Hypothesis\")"
 94 |    ]
 95 |   },
 96 |   {
 97 |    "cell_type": "markdown",
 98 |    "id": "2346dd54",
 99 |    "metadata": {},
100 |    "source": [
101 |     "This implies that we have sufficient proof to say that there exists a difference in the performance among four different engine oils. "
102 |    ]
103 |   },
104 |   {
105 |    "cell_type": "code",
106 |    "execution_count": 11,
107 |    "id": "afcc750a",
108 |    "metadata": {},
109 |    "outputs": [],
110 |    "source": [
111 |     "e = [2,4,2]\n",
112 |     "m =[2,3,4]\n",
113 |     "s = [1,2,5]"
114 |    ]
115 |   },
116 |   {
117 |    "cell_type": "code",
118 |    "execution_count": 12,
119 |    "id": "73cd8603",
120 |    "metadata": {},
121 |    "outputs": [
122 |     {
123 |      "data": {
124 |       "text/plain": [
125 |        "F_onewayResult(statistic=0.04999999999999999, pvalue=0.9516215013591449)"
126 |       ]
127 |      },
128 |      "execution_count": 12,
129 |      "metadata": {},
130 |      "output_type": "execute_result"
131 |     }
132 |    ],
133 |    "source": [
134 |     "f_oneway(e,m,s)"
135 |    ]
136 |   }
137 |  ],
138 |  "metadata": {
139 |   "kernelspec": {
140 |    "display_name": "Python 3 (ipykernel)",
141 |    "language": "python",
142 |    "name": "python3"
143 |   },
144 |   "language_info": {
145 |    "codemirror_mode": {
146 |     "name": "ipython",
147 |     "version": 3
148 |    },
149 |    "file_extension": ".py",
150 |    "mimetype": "text/x-python",
151 |    "name": "python",
152 |    "nbconvert_exporter": "python",
153 |    "pygments_lexer": "ipython3",
154 |    "version": "3.11.1"
155 |   }
156 |  },
157 |  "nbformat": 4,
158 |  "nbformat_minor": 5
159 | }
160 | 


--------------------------------------------------------------------------------
/Inferential Stats/2 way Anova.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 3,
  6 |    "id": "6ea2d460",
  7 |    "metadata": {},
  8 |    "outputs": [],
  9 |    "source": [
 10 |     "import numpy as np\n",
 11 |     "import pandas as pd\n",
 12 |     "import statsmodels.api as sm\n",
 13 |     "from statsmodels.formula.api import ols"
 14 |    ]
 15 |   },
 16 |   {
 17 |    "cell_type": "markdown",
 18 |    "id": "e5e33a81",
 19 |    "metadata": {},
 20 |    "source": [
 21 |     "Test whether plant growth is affected by fertilizers and watering frequency."
 22 |    ]
 23 |   },
 24 |   {
 25 |    "cell_type": "code",
 26 |    "execution_count": 4,
 27 |    "id": "8f6429be",
 28 |    "metadata": {},
 29 |    "outputs": [],
 30 |    "source": [
 31 |     "# Create a dataframe\n",
 32 |     "dataframe = pd.DataFrame({'Fertilizer': np.repeat(['daily', 'weekly'], 15),\n",
 33 |     "                          'Watering': np.repeat(['daily', 'weekly'], 15),\n",
 34 |     "                          'height': [14, 16, 15, 15, 16, 13, 12, 11, 14, \n",
 35 |     "                                     15, 16, 16, 17, 18, 14, 13, 14, 14, \n",
 36 |     "                                     14, 15, 16, 16, 17, 18, 14, 13, 14, \n",
 37 |     "                                     14, 14, 15]})"
 38 |    ]
 39 |   },
 40 |   {
 41 |    "cell_type": "code",
 42 |    "execution_count": 5,
 43 |    "id": "8255afd8",
 44 |    "metadata": {},
 45 |    "outputs": [
 46 |     {
 47 |      "data": {
 48 |       "text/html": [
 49 |        "<div>\n",
 50 |        "<style scoped>\n",
 51 |        "    .dataframe tbody tr th:only-of-type {\n",
 52 |        "        vertical-align: middle;\n",
 53 |        "    }\n",
 54 |        "\n",
 55 |        "    .dataframe tbody tr th {\n",
 56 |        "        vertical-align: top;\n",
 57 |        "    }\n",
 58 |        "\n",
 59 |        "    .dataframe thead th {\n",
 60 |        "        text-align: right;\n",
 61 |        "    }\n",
 62 |        "</style>\n",
 63 |        "<table border=\"1\" class=\"dataframe\">\n",
 64 |        "  <thead>\n",
 65 |        "    <tr style=\"text-align: right;\">\n",
 66 |        "      <th></th>\n",
 67 |        "      <th>Fertilizer</th>\n",
 68 |        "      <th>Watering</th>\n",
 69 |        "      <th>height</th>\n",
 70 |        "    </tr>\n",
 71 |        "  </thead>\n",
 72 |        "  <tbody>\n",
 73 |        "    <tr>\n",
 74 |        "      <th>0</th>\n",
 75 |        "      <td>daily</td>\n",
 76 |        "      <td>daily</td>\n",
 77 |        "      <td>14</td>\n",
 78 |        "    </tr>\n",
 79 |        "    <tr>\n",
 80 |        "      <th>1</th>\n",
 81 |        "      <td>daily</td>\n",
 82 |        "      <td>daily</td>\n",
 83 |        "      <td>16</td>\n",
 84 |        "    </tr>\n",
 85 |        "    <tr>\n",
 86 |        "      <th>2</th>\n",
 87 |        "      <td>daily</td>\n",
 88 |        "      <td>daily</td>\n",
 89 |        "      <td>15</td>\n",
 90 |        "    </tr>\n",
 91 |        "    <tr>\n",
 92 |        "      <th>3</th>\n",
 93 |        "      <td>daily</td>\n",
 94 |        "      <td>daily</td>\n",
 95 |        "      <td>15</td>\n",
 96 |        "    </tr>\n",
 97 |        "    <tr>\n",
 98 |        "      <th>4</th>\n",
 99 |        "      <td>daily</td>\n",
100 |        "      <td>daily</td>\n",
101 |        "      <td>16</td>\n",
102 |        "    </tr>\n",
103 |        "    <tr>\n",
104 |        "      <th>5</th>\n",
105 |        "      <td>daily</td>\n",
106 |        "      <td>daily</td>\n",
107 |        "      <td>13</td>\n",
108 |        "    </tr>\n",
109 |        "    <tr>\n",
110 |        "      <th>6</th>\n",
111 |        "      <td>daily</td>\n",
112 |        "      <td>daily</td>\n",
113 |        "      <td>12</td>\n",
114 |        "    </tr>\n",
115 |        "    <tr>\n",
116 |        "      <th>7</th>\n",
117 |        "      <td>daily</td>\n",
118 |        "      <td>daily</td>\n",
119 |        "      <td>11</td>\n",
120 |        "    </tr>\n",
121 |        "    <tr>\n",
122 |        "      <th>8</th>\n",
123 |        "      <td>daily</td>\n",
124 |        "      <td>daily</td>\n",
125 |        "      <td>14</td>\n",
126 |        "    </tr>\n",
127 |        "    <tr>\n",
128 |        "      <th>9</th>\n",
129 |        "      <td>daily</td>\n",
130 |        "      <td>daily</td>\n",
131 |        "      <td>15</td>\n",
132 |        "    </tr>\n",
133 |        "    <tr>\n",
134 |        "      <th>10</th>\n",
135 |        "      <td>daily</td>\n",
136 |        "      <td>daily</td>\n",
137 |        "      <td>16</td>\n",
138 |        "    </tr>\n",
139 |        "    <tr>\n",
140 |        "      <th>11</th>\n",
141 |        "      <td>daily</td>\n",
142 |        "      <td>daily</td>\n",
143 |        "      <td>16</td>\n",
144 |        "    </tr>\n",
145 |        "    <tr>\n",
146 |        "      <th>12</th>\n",
147 |        "      <td>daily</td>\n",
148 |        "      <td>daily</td>\n",
149 |        "      <td>17</td>\n",
150 |        "    </tr>\n",
151 |        "    <tr>\n",
152 |        "      <th>13</th>\n",
153 |        "      <td>daily</td>\n",
154 |        "      <td>daily</td>\n",
155 |        "      <td>18</td>\n",
156 |        "    </tr>\n",
157 |        "    <tr>\n",
158 |        "      <th>14</th>\n",
159 |        "      <td>daily</td>\n",
160 |        "      <td>daily</td>\n",
161 |        "      <td>14</td>\n",
162 |        "    </tr>\n",
163 |        "    <tr>\n",
164 |        "      <th>15</th>\n",
165 |        "      <td>weekly</td>\n",
166 |        "      <td>weekly</td>\n",
167 |        "      <td>13</td>\n",
168 |        "    </tr>\n",
169 |        "    <tr>\n",
170 |        "      <th>16</th>\n",
171 |        "      <td>weekly</td>\n",
172 |        "      <td>weekly</td>\n",
173 |        "      <td>14</td>\n",
174 |        "    </tr>\n",
175 |        "    <tr>\n",
176 |        "      <th>17</th>\n",
177 |        "      <td>weekly</td>\n",
178 |        "      <td>weekly</td>\n",
179 |        "      <td>14</td>\n",
180 |        "    </tr>\n",
181 |        "    <tr>\n",
182 |        "      <th>18</th>\n",
183 |        "      <td>weekly</td>\n",
184 |        "      <td>weekly</td>\n",
185 |        "      <td>14</td>\n",
186 |        "    </tr>\n",
187 |        "    <tr>\n",
188 |        "      <th>19</th>\n",
189 |        "      <td>weekly</td>\n",
190 |        "      <td>weekly</td>\n",
191 |        "      <td>15</td>\n",
192 |        "    </tr>\n",
193 |        "    <tr>\n",
194 |        "      <th>20</th>\n",
195 |        "      <td>weekly</td>\n",
196 |        "      <td>weekly</td>\n",
197 |        "      <td>16</td>\n",
198 |        "    </tr>\n",
199 |        "    <tr>\n",
200 |        "      <th>21</th>\n",
201 |        "      <td>weekly</td>\n",
202 |        "      <td>weekly</td>\n",
203 |        "      <td>16</td>\n",
204 |        "    </tr>\n",
205 |        "    <tr>\n",
206 |        "      <th>22</th>\n",
207 |        "      <td>weekly</td>\n",
208 |        "      <td>weekly</td>\n",
209 |        "      <td>17</td>\n",
210 |        "    </tr>\n",
211 |        "    <tr>\n",
212 |        "      <th>23</th>\n",
213 |        "      <td>weekly</td>\n",
214 |        "      <td>weekly</td>\n",
215 |        "      <td>18</td>\n",
216 |        "    </tr>\n",
217 |        "    <tr>\n",
218 |        "      <th>24</th>\n",
219 |        "      <td>weekly</td>\n",
220 |        "      <td>weekly</td>\n",
221 |        "      <td>14</td>\n",
222 |        "    </tr>\n",
223 |        "    <tr>\n",
224 |        "      <th>25</th>\n",
225 |        "      <td>weekly</td>\n",
226 |        "      <td>weekly</td>\n",
227 |        "      <td>13</td>\n",
228 |        "    </tr>\n",
229 |        "    <tr>\n",
230 |        "      <th>26</th>\n",
231 |        "      <td>weekly</td>\n",
232 |        "      <td>weekly</td>\n",
233 |        "      <td>14</td>\n",
234 |        "    </tr>\n",
235 |        "    <tr>\n",
236 |        "      <th>27</th>\n",
237 |        "      <td>weekly</td>\n",
238 |        "      <td>weekly</td>\n",
239 |        "      <td>14</td>\n",
240 |        "    </tr>\n",
241 |        "    <tr>\n",
242 |        "      <th>28</th>\n",
243 |        "      <td>weekly</td>\n",
244 |        "      <td>weekly</td>\n",
245 |        "      <td>14</td>\n",
246 |        "    </tr>\n",
247 |        "    <tr>\n",
248 |        "      <th>29</th>\n",
249 |        "      <td>weekly</td>\n",
250 |        "      <td>weekly</td>\n",
251 |        "      <td>15</td>\n",
252 |        "    </tr>\n",
253 |        "  </tbody>\n",
254 |        "</table>\n",
255 |        "</div>"
256 |       ],
257 |       "text/plain": [
258 |        "   Fertilizer Watering  height\n",
259 |        "0       daily    daily      14\n",
260 |        "1       daily    daily      16\n",
261 |        "2       daily    daily      15\n",
262 |        "3       daily    daily      15\n",
263 |        "4       daily    daily      16\n",
264 |        "5       daily    daily      13\n",
265 |        "6       daily    daily      12\n",
266 |        "7       daily    daily      11\n",
267 |        "8       daily    daily      14\n",
268 |        "9       daily    daily      15\n",
269 |        "10      daily    daily      16\n",
270 |        "11      daily    daily      16\n",
271 |        "12      daily    daily      17\n",
272 |        "13      daily    daily      18\n",
273 |        "14      daily    daily      14\n",
274 |        "15     weekly   weekly      13\n",
275 |        "16     weekly   weekly      14\n",
276 |        "17     weekly   weekly      14\n",
277 |        "18     weekly   weekly      14\n",
278 |        "19     weekly   weekly      15\n",
279 |        "20     weekly   weekly      16\n",
280 |        "21     weekly   weekly      16\n",
281 |        "22     weekly   weekly      17\n",
282 |        "23     weekly   weekly      18\n",
283 |        "24     weekly   weekly      14\n",
284 |        "25     weekly   weekly      13\n",
285 |        "26     weekly   weekly      14\n",
286 |        "27     weekly   weekly      14\n",
287 |        "28     weekly   weekly      14\n",
288 |        "29     weekly   weekly      15"
289 |       ]
290 |      },
291 |      "execution_count": 5,
292 |      "metadata": {},
293 |      "output_type": "execute_result"
294 |     }
295 |    ],
296 |    "source": [
297 |     "dataframe"
298 |    ]
299 |   },
300 |   {
301 |    "cell_type": "code",
302 |    "execution_count": 6,
303 |    "id": "1e8e9f32",
304 |    "metadata": {
305 |     "scrolled": true
306 |    },
307 |    "outputs": [
308 |     {
309 |      "name": "stdout",
310 |      "output_type": "stream",
311 |      "text": [
312 |       "                       df     sum_sq   mean_sq         F    PR(>F)\n",
313 |       "Fertilizer            1.0   0.033333  0.033333  0.012069  0.913305\n",
314 |       "Watering              1.0   0.000369  0.000369  0.000133  0.990865\n",
315 |       "Fertilizer:Watering   1.0   0.040866  0.040866  0.014796  0.904053\n",
316 |       "Residual             28.0  77.333333  2.761905       NaN       NaN\n"
317 |      ]
318 |     }
319 |    ],
320 |    "source": [
321 |     "model = ols('height ~ Fertilizer + Watering + Fertilizer : Watering',  data=dataframe).fit()\n",
322 |     "\n",
323 |     "result = sm.stats.anova_lm(model, type=2)\n",
324 |     "\n",
325 |     "print(result)"
326 |    ]
327 |   },
328 |   {
329 |    "cell_type": "code",
330 |    "execution_count": 7,
331 |    "id": "df431b57",
332 |    "metadata": {},
333 |    "outputs": [],
334 |    "source": [
335 |     "model = ols('height ~ Fertilizer + Watering + Fertilizer : Watering',  data=dataframe)"
336 |    ]
337 |   },
338 |   {
339 |    "cell_type": "code",
340 |    "execution_count": null,
341 |    "id": "7b2c4b74",
342 |    "metadata": {},
343 |    "outputs": [],
344 |    "source": []
345 |   },
346 |   {
347 |    "cell_type": "markdown",
348 |    "id": "84203d6d",
349 |    "metadata": {},
350 |    "source": []
351 |   }
352 |  ],
353 |  "metadata": {
354 |   "kernelspec": {
355 |    "display_name": "Python 3 (ipykernel)",
356 |    "language": "python",
357 |    "name": "python3"
358 |   },
359 |   "language_info": {
360 |    "codemirror_mode": {
361 |     "name": "ipython",
362 |     "version": 3
363 |    },
364 |    "file_extension": ".py",
365 |    "mimetype": "text/x-python",
366 |    "name": "python",
367 |    "nbconvert_exporter": "python",
368 |    "pygments_lexer": "ipython3",
369 |    "version": "3.11.1"
370 |   }
371 |  },
372 |  "nbformat": 4,
373 |  "nbformat_minor": 5
374 | }
375 | 


--------------------------------------------------------------------------------
/Inferential Stats/Chi square.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 3,
  6 |    "id": "02740abc",
  7 |    "metadata": {},
  8 |    "outputs": [],
  9 |    "source": [
 10 |     "from scipy.stats import chisquare"
 11 |    ]
 12 |   },
 13 |   {
 14 |    "cell_type": "code",
 15 |    "execution_count": 4,
 16 |    "id": "71b74700",
 17 |    "metadata": {},
 18 |    "outputs": [],
 19 |    "source": [
 20 |     "chi_square, pvalue =chisquare([48,24,24])"
 21 |    ]
 22 |   },
 23 |   {
 24 |    "cell_type": "code",
 25 |    "execution_count": 5,
 26 |    "id": "84c64655",
 27 |    "metadata": {},
 28 |    "outputs": [
 29 |     {
 30 |      "data": {
 31 |       "text/plain": [
 32 |        "(12.0, 0.002478752176666357)"
 33 |       ]
 34 |      },
 35 |      "execution_count": 5,
 36 |      "metadata": {},
 37 |      "output_type": "execute_result"
 38 |     }
 39 |    ],
 40 |    "source": [
 41 |     "chi_square, pvalue"
 42 |    ]
 43 |   },
 44 |   {
 45 |    "cell_type": "code",
 46 |    "execution_count": 6,
 47 |    "id": "6a2a14fc",
 48 |    "metadata": {},
 49 |    "outputs": [],
 50 |    "source": [
 51 |     "alpha = 0.01"
 52 |    ]
 53 |   },
 54 |   {
 55 |    "cell_type": "code",
 56 |    "execution_count": 7,
 57 |    "id": "50878e4d",
 58 |    "metadata": {},
 59 |    "outputs": [
 60 |     {
 61 |      "name": "stdout",
 62 |      "output_type": "stream",
 63 |      "text": [
 64 |       "Dependent (reject H0)\n"
 65 |      ]
 66 |     }
 67 |    ],
 68 |    "source": [
 69 |     "if (pvalue < alpha):\n",
 70 |     "    print('Dependent (reject H0)')\n",
 71 |     "else:\n",
 72 |     "    print('Independent (H0 holds true)')"
 73 |    ]
 74 |   },
 75 |   {
 76 |    "cell_type": "code",
 77 |    "execution_count": 8,
 78 |    "id": "45ae954c",
 79 |    "metadata": {},
 80 |    "outputs": [],
 81 |    "source": [
 82 |     "chi_square, pvalue =chisquare([5,9,13,7,6])"
 83 |    ]
 84 |   },
 85 |   {
 86 |    "cell_type": "code",
 87 |    "execution_count": 9,
 88 |    "id": "3f39a87a",
 89 |    "metadata": {},
 90 |    "outputs": [
 91 |     {
 92 |      "data": {
 93 |       "text/plain": [
 94 |        "(5.0, 0.2872974951836458)"
 95 |       ]
 96 |      },
 97 |      "execution_count": 9,
 98 |      "metadata": {},
 99 |      "output_type": "execute_result"
100 |     }
101 |    ],
102 |    "source": [
103 |     "chi_square, pvalue "
104 |    ]
105 |   },
106 |   {
107 |    "cell_type": "code",
108 |    "execution_count": 10,
109 |    "id": "ab7d9ec5",
110 |    "metadata": {},
111 |    "outputs": [],
112 |    "source": [
113 |     "alpha = 0.05"
114 |    ]
115 |   },
116 |   {
117 |    "cell_type": "code",
118 |    "execution_count": 11,
119 |    "id": "aaa915fe",
120 |    "metadata": {},
121 |    "outputs": [
122 |     {
123 |      "name": "stdout",
124 |      "output_type": "stream",
125 |      "text": [
126 |       "Independent (H0 holds true)\n"
127 |      ]
128 |     }
129 |    ],
130 |    "source": [
131 |     "if (pvalue < alpha):\n",
132 |     "    print('Dependent (reject H0)')\n",
133 |     "else:\n",
134 |     "    print('Independent (H0 holds true)')"
135 |    ]
136 |   },
137 |   {
138 |    "cell_type": "code",
139 |    "execution_count": 12,
140 |    "id": "fbc6caf9",
141 |    "metadata": {},
142 |    "outputs": [],
143 |    "source": [
144 |     "from scipy.stats import chi2_contingency"
145 |    ]
146 |   },
147 |   {
148 |    "cell_type": "code",
149 |    "execution_count": 13,
150 |    "id": "0b7dab21",
151 |    "metadata": {},
152 |    "outputs": [],
153 |    "source": [
154 |     "data = [[207,282,241],[234,242,232]]"
155 |    ]
156 |   },
157 |   {
158 |    "cell_type": "code",
159 |    "execution_count": 14,
160 |    "id": "30162d0c",
161 |    "metadata": {},
162 |    "outputs": [],
163 |    "source": [
164 |     "stat, pvalue,dof,exp_freq =chi2_contingency(data)"
165 |    ]
166 |   },
167 |   {
168 |    "cell_type": "code",
169 |    "execution_count": 16,
170 |    "id": "e182b164",
171 |    "metadata": {
172 |     "scrolled": true
173 |    },
174 |    "outputs": [
175 |     {
176 |      "data": {
177 |       "text/plain": [
178 |        "(4.542228269825232,\n",
179 |        " 0.10319714047309392,\n",
180 |        " 2,\n",
181 |        " array([[223.87343533, 266.00834492, 240.11821975],\n",
182 |        "        [217.12656467, 257.99165508, 232.88178025]]))"
183 |       ]
184 |      },
185 |      "execution_count": 16,
186 |      "metadata": {},
187 |      "output_type": "execute_result"
188 |     }
189 |    ],
190 |    "source": [
191 |     "stat, pvalue,dof,exp_freq"
192 |    ]
193 |   },
194 |   {
195 |    "cell_type": "code",
196 |    "execution_count": 15,
197 |    "id": "d6d97824",
198 |    "metadata": {},
199 |    "outputs": [],
200 |    "source": [
201 |     "outlook_data = [[2,3],[4,0],[3,2]]"
202 |    ]
203 |   },
204 |   {
205 |    "cell_type": "code",
206 |    "execution_count": 16,
207 |    "id": "2e80f782",
208 |    "metadata": {},
209 |    "outputs": [
210 |     {
211 |      "data": {
212 |       "text/plain": [
213 |        "Chi2ContingencyResult(statistic=3.546666666666666, pvalue=0.16976615743981124, dof=2, expected_freq=array([[3.21428571, 1.78571429],\n",
214 |        "       [2.57142857, 1.42857143],\n",
215 |        "       [3.21428571, 1.78571429]]))"
216 |       ]
217 |      },
218 |      "execution_count": 16,
219 |      "metadata": {},
220 |      "output_type": "execute_result"
221 |     }
222 |    ],
223 |    "source": [
224 |     "chi2_contingency(outlook_data)"
225 |    ]
226 |   },
227 |   {
228 |    "cell_type": "code",
229 |    "execution_count": 18,
230 |    "id": "5ae0c20a",
231 |    "metadata": {},
232 |    "outputs": [],
233 |    "source": [
234 |     "wind_data = [[3,3],[6,2]]"
235 |    ]
236 |   },
237 |   {
238 |    "cell_type": "code",
239 |    "execution_count": 19,
240 |    "id": "56b6f970",
241 |    "metadata": {},
242 |    "outputs": [
243 |     {
244 |      "data": {
245 |       "text/plain": [
246 |        "Chi2ContingencyResult(statistic=0.16203703703703703, pvalue=0.687287949348002, dof=1, expected_freq=array([[3.85714286, 2.14285714],\n",
247 |        "       [5.14285714, 2.85714286]]))"
248 |       ]
249 |      },
250 |      "execution_count": 19,
251 |      "metadata": {},
252 |      "output_type": "execute_result"
253 |     }
254 |    ],
255 |    "source": [
256 |     "chi2_contingency(wind_data)"
257 |    ]
258 |   },
259 |   {
260 |    "cell_type": "code",
261 |    "execution_count": 20,
262 |    "id": "be05bdc3",
263 |    "metadata": {},
264 |    "outputs": [],
265 |    "source": [
266 |     "obs = [3,3,6,2]\n",
267 |     "exp = [3.85714286, 2.14285714, 5.14285714, 2.85714286]"
268 |    ]
269 |   },
270 |   {
271 |    "cell_type": "code",
272 |    "execution_count": 26,
273 |    "id": "0f835729",
274 |    "metadata": {},
275 |    "outputs": [
276 |     {
277 |      "data": {
278 |       "text/plain": [
279 |        "Power_divergenceResult(statistic=0.9333333396938274, pvalue=0.8173771570701455)"
280 |       ]
281 |      },
282 |      "execution_count": 26,
283 |      "metadata": {},
284 |      "output_type": "execute_result"
285 |     }
286 |    ],
287 |    "source": [
288 |     "chisquare (obs, exp)"
289 |    ]
290 |   },
291 |   {
292 |    "cell_type": "code",
293 |    "execution_count": null,
294 |    "id": "84d19fd6",
295 |    "metadata": {},
296 |    "outputs": [],
297 |    "source": []
298 |   }
299 |  ],
300 |  "metadata": {
301 |   "kernelspec": {
302 |    "display_name": "Python 3 (ipykernel)",
303 |    "language": "python",
304 |    "name": "python3"
305 |   },
306 |   "language_info": {
307 |    "codemirror_mode": {
308 |     "name": "ipython",
309 |     "version": 3
310 |    },
311 |    "file_extension": ".py",
312 |    "mimetype": "text/x-python",
313 |    "name": "python",
314 |    "nbconvert_exporter": "python",
315 |    "pygments_lexer": "ipython3",
316 |    "version": "3.11.1"
317 |   }
318 |  },
319 |  "nbformat": 4,
320 |  "nbformat_minor": 5
321 | }
322 | 


--------------------------------------------------------------------------------
/Inferential Stats/Confidence Interval.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "id": "11154bb4",
  6 |    "metadata": {},
  7 |    "source": [
  8 |     "Calculate the 90% confidence Intervals of the data set of size(n=20) using the t Distribution using the  t.interval() function and passing the alpha parameter to 0.90 in the python."
  9 |    ]
 10 |   },
 11 |   {
 12 |    "cell_type": "code",
 13 |    "execution_count": 1,
 14 |    "id": "e3b00d78",
 15 |    "metadata": {},
 16 |    "outputs": [],
 17 |    "source": [
 18 |     "import numpy as np \n",
 19 |     "import scipy.stats as st "
 20 |    ]
 21 |   },
 22 |   {
 23 |    "cell_type": "code",
 24 |    "execution_count": 2,
 25 |    "id": "36f06ed1",
 26 |    "metadata": {},
 27 |    "outputs": [],
 28 |    "source": [
 29 |     "gfg_data = [1, 1, 1, 2, 2, 2, 3, 3, 3, 3,  \n",
 30 |     "            3, 4, 4, 5, 5, 5, 6, 7, 8, 10] "
 31 |    ]
 32 |   },
 33 |   {
 34 |    "cell_type": "code",
 35 |    "execution_count": 3,
 36 |    "id": "1180a155",
 37 |    "metadata": {
 38 |     "scrolled": true
 39 |    },
 40 |    "outputs": [
 41 |     {
 42 |      "data": {
 43 |       "text/plain": [
 44 |        "(2.962098014195961, 4.837901985804038)"
 45 |       ]
 46 |      },
 47 |      "execution_count": 3,
 48 |      "metadata": {},
 49 |      "output_type": "execute_result"
 50 |     }
 51 |    ],
 52 |    "source": [
 53 |     "st.t.interval(confidence = 0.90, df = len(gfg_data)-1,loc = np.mean(gfg_data), scale=st.sem(gfg_data))"
 54 |    ]
 55 |   },
 56 |   {
 57 |    "cell_type": "code",
 58 |    "execution_count": 4,
 59 |    "id": "f2c1c83f",
 60 |    "metadata": {},
 61 |    "outputs": [
 62 |     {
 63 |      "data": {
 64 |       "text/plain": [
 65 |        "3.9"
 66 |       ]
 67 |      },
 68 |      "execution_count": 4,
 69 |      "metadata": {},
 70 |      "output_type": "execute_result"
 71 |     }
 72 |    ],
 73 |    "source": [
 74 |     "np.mean(gfg_data)"
 75 |    ]
 76 |   },
 77 |   {
 78 |    "cell_type": "markdown",
 79 |    "id": "3ddcc3c9",
 80 |    "metadata": {},
 81 |    "source": [
 82 |     "Calculate the 90% confidence Intervals of the random data set of size(n=100) using the norm Distribution using the norm.interval() function and passing the alpha parameter to 0.90 in the python."
 83 |    ]
 84 |   },
 85 |   {
 86 |    "cell_type": "code",
 87 |    "execution_count": 5,
 88 |    "id": "846a0514",
 89 |    "metadata": {},
 90 |    "outputs": [],
 91 |    "source": [
 92 |     "np.random.seed(0)\n",
 93 |     "gfg_data = np.random.randint(5, 10, 100) "
 94 |    ]
 95 |   },
 96 |   {
 97 |    "cell_type": "code",
 98 |    "execution_count": 6,
 99 |    "id": "d6efa71a",
100 |    "metadata": {},
101 |    "outputs": [
102 |     {
103 |      "data": {
104 |       "text/plain": [
105 |        "(6.722821921612098, 7.197178078387902)"
106 |       ]
107 |      },
108 |      "execution_count": 6,
109 |      "metadata": {},
110 |      "output_type": "execute_result"
111 |     }
112 |    ],
113 |    "source": [
114 |     "st.norm.interval(confidence = 0.90,loc = np.mean(gfg_data), scale=st.sem(gfg_data))"
115 |    ]
116 |   },
117 |   {
118 |    "cell_type": "code",
119 |    "execution_count": 11,
120 |    "id": "a75092a1",
121 |    "metadata": {},
122 |    "outputs": [
123 |     {
124 |      "data": {
125 |       "text/plain": [
126 |        "6.96"
127 |       ]
128 |      },
129 |      "execution_count": 11,
130 |      "metadata": {},
131 |      "output_type": "execute_result"
132 |     }
133 |    ],
134 |    "source": [
135 |     "np.mean(gfg_data)"
136 |    ]
137 |   }
138 |  ],
139 |  "metadata": {
140 |   "kernelspec": {
141 |    "display_name": "Python 3 (ipykernel)",
142 |    "language": "python",
143 |    "name": "python3"
144 |   },
145 |   "language_info": {
146 |    "codemirror_mode": {
147 |     "name": "ipython",
148 |     "version": 3
149 |    },
150 |    "file_extension": ".py",
151 |    "mimetype": "text/x-python",
152 |    "name": "python",
153 |    "nbconvert_exporter": "python",
154 |    "pygments_lexer": "ipython3",
155 |    "version": "3.11.1"
156 |   }
157 |  },
158 |  "nbformat": 4,
159 |  "nbformat_minor": 5
160 | }
161 | 


--------------------------------------------------------------------------------
/Inferential Stats/T-Test.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "id": "d2ad7b54",
  7 |    "metadata": {},
  8 |    "outputs": [],
  9 |    "source": [
 10 |     "import numpy as np\n",
 11 |     "from numpy.random import randn\n",
 12 |     "import scipy.stats as stats"
 13 |    ]
 14 |   },
 15 |   {
 16 |    "cell_type": "code",
 17 |    "execution_count": 2,
 18 |    "id": "ed11d353",
 19 |    "metadata": {},
 20 |    "outputs": [],
 21 |    "source": [
 22 |     "sample_mean = 140\n",
 23 |     "sample_sd = 20\n",
 24 |     "data = sample_sd*randn(30)+sample_mean"
 25 |    ]
 26 |   },
 27 |   {
 28 |    "cell_type": "code",
 29 |    "execution_count": 3,
 30 |    "id": "238d1909",
 31 |    "metadata": {},
 32 |    "outputs": [],
 33 |    "source": [
 34 |     "alpha = 0.05"
 35 |    ]
 36 |   },
 37 |   {
 38 |    "cell_type": "code",
 39 |    "execution_count": 4,
 40 |    "id": "c9564642",
 41 |    "metadata": {},
 42 |    "outputs": [
 43 |     {
 44 |      "name": "stdout",
 45 |      "output_type": "stream",
 46 |      "text": [
 47 |       "mean=139.89 stdv=20.96\n"
 48 |      ]
 49 |     }
 50 |    ],
 51 |    "source": [
 52 |     "print('mean=%.2f stdv=%.2f' % (np.mean(data), np.std(data)))"
 53 |    ]
 54 |   },
 55 |   {
 56 |    "cell_type": "code",
 57 |    "execution_count": 5,
 58 |    "id": "1b60b72e",
 59 |    "metadata": {},
 60 |    "outputs": [],
 61 |    "source": [
 62 |     "t_statistic, p_value = stats.ttest_1samp(a=data, popmean=100)"
 63 |    ]
 64 |   },
 65 |   {
 66 |    "cell_type": "code",
 67 |    "execution_count": 6,
 68 |    "id": "dee96c94",
 69 |    "metadata": {},
 70 |    "outputs": [
 71 |     {
 72 |      "data": {
 73 |       "text/plain": [
 74 |        "(10.250576729298452, 3.753118701693896e-11)"
 75 |       ]
 76 |      },
 77 |      "execution_count": 6,
 78 |      "metadata": {},
 79 |      "output_type": "execute_result"
 80 |     }
 81 |    ],
 82 |    "source": [
 83 |     "t_statistic, p_value"
 84 |    ]
 85 |   },
 86 |   {
 87 |    "cell_type": "code",
 88 |    "execution_count": 7,
 89 |    "id": "19c3ff8c",
 90 |    "metadata": {
 91 |     "scrolled": true
 92 |    },
 93 |    "outputs": [
 94 |     {
 95 |      "name": "stdout",
 96 |      "output_type": "stream",
 97 |      "text": [
 98 |       "Reject Null Hypothesis\n"
 99 |      ]
100 |     }
101 |    ],
102 |    "source": [
103 |     "if(p_value <  alpha):\n",
104 |     "  print(\"Reject Null Hypothesis\")\n",
105 |     "else:\n",
106 |     "  print(\"Fail to Reject NUll Hypothesis\")"
107 |    ]
108 |   },
109 |   {
110 |    "cell_type": "code",
111 |    "execution_count": 15,
112 |    "id": "2065dfc1",
113 |    "metadata": {},
114 |    "outputs": [],
115 |    "source": [
116 |     "sample_mean = 24\n",
117 |     "sample_sd = 0.7\n",
118 |     "data2 = sample_sd*randn(20)+sample_mean"
119 |    ]
120 |   },
121 |   {
122 |    "cell_type": "code",
123 |    "execution_count": 16,
124 |    "id": "e9e3b150",
125 |    "metadata": {
126 |     "scrolled": false
127 |    },
128 |    "outputs": [
129 |     {
130 |      "name": "stdout",
131 |      "output_type": "stream",
132 |      "text": [
133 |       "mean=23.83 stdv=0.81\n"
134 |      ]
135 |     }
136 |    ],
137 |    "source": [
138 |     "print('mean=%.2f stdv=%.2f' % (np.mean(data2), np.std(data2)))"
139 |    ]
140 |   },
141 |   {
142 |    "cell_type": "code",
143 |    "execution_count": 17,
144 |    "id": "d3026729",
145 |    "metadata": {},
146 |    "outputs": [],
147 |    "source": [
148 |     "t_statistic, p_value = stats.ttest_1samp(a=data2, popmean=25)"
149 |    ]
150 |   },
151 |   {
152 |    "cell_type": "code",
153 |    "execution_count": 18,
154 |    "id": "dda7be32",
155 |    "metadata": {},
156 |    "outputs": [
157 |     {
158 |      "data": {
159 |       "text/plain": [
160 |        "(-6.303910919091638, 4.738238730383527e-06)"
161 |       ]
162 |      },
163 |      "execution_count": 18,
164 |      "metadata": {},
165 |      "output_type": "execute_result"
166 |     }
167 |    ],
168 |    "source": [
169 |     "t_statistic, p_value"
170 |    ]
171 |   },
172 |   {
173 |    "cell_type": "code",
174 |    "execution_count": 12,
175 |    "id": "1dadcb05",
176 |    "metadata": {},
177 |    "outputs": [
178 |     {
179 |      "name": "stdout",
180 |      "output_type": "stream",
181 |      "text": [
182 |       "Reject Null Hypothesis\n"
183 |      ]
184 |     }
185 |    ],
186 |    "source": [
187 |     "if(p_value <  alpha):\n",
188 |     "  print(\"Reject Null Hypothesis\")\n",
189 |     "else:\n",
190 |     "  print(\"Fail to Reject NUll Hypothesis\")"
191 |    ]
192 |   },
193 |   {
194 |    "cell_type": "code",
195 |    "execution_count": null,
196 |    "id": "fb3be60f",
197 |    "metadata": {},
198 |    "outputs": [],
199 |    "source": []
200 |   }
201 |  ],
202 |  "metadata": {
203 |   "kernelspec": {
204 |    "display_name": "Python 3 (ipykernel)",
205 |    "language": "python",
206 |    "name": "python3"
207 |   },
208 |   "language_info": {
209 |    "codemirror_mode": {
210 |     "name": "ipython",
211 |     "version": 3
212 |    },
213 |    "file_extension": ".py",
214 |    "mimetype": "text/x-python",
215 |    "name": "python",
216 |    "nbconvert_exporter": "python",
217 |    "pygments_lexer": "ipython3",
218 |    "version": "3.11.1"
219 |   }
220 |  },
221 |  "nbformat": 4,
222 |  "nbformat_minor": 5
223 | }
224 | 


--------------------------------------------------------------------------------
/Inferential Stats/Z test.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 89,
  6 |    "id": "5af96aa2",
  7 |    "metadata": {},
  8 |    "outputs": [],
  9 |    "source": [
 10 |     "import math\n",
 11 |     "import numpy as np\n",
 12 |     "import scipy"
 13 |    ]
 14 |   },
 15 |   {
 16 |    "cell_type": "code",
 17 |    "execution_count": 88,
 18 |    "id": "6bfc3f54",
 19 |    "metadata": {},
 20 |    "outputs": [],
 21 |    "source": [
 22 |     "alpha = 0.05"
 23 |    ]
 24 |   },
 25 |   {
 26 |    "cell_type": "code",
 27 |    "execution_count": 87,
 28 |    "id": "bc95ebe3",
 29 |    "metadata": {},
 30 |    "outputs": [],
 31 |    "source": [
 32 |     "def z_test(n, sample_mean,null_mean, sd,alternative):\n",
 33 |     "    z_score = (sample_mean-null_mean)/(sd/math.sqrt(n))\n",
 34 |     "    p_value = scipy.stats.norm.sf(z_score)\n",
 35 |     "    print(z_score)\n",
 36 |     "    print(p_value)\n",
 37 |     "    if(alternative == \"larger\" or alternative == \"smaller\"):\n",
 38 |     "        if(p_value <  alpha):\n",
 39 |     "            print(\"Reject Null Hypothesis\")\n",
 40 |     "        else:\n",
 41 |     "            print(\"Fail to Reject NUll Hypothesis\")\n",
 42 |     "    elif(alternative == \"two-sided\"):\n",
 43 |     "        if(p_value <  alpha/2):\n",
 44 |     "            print(\"Reject Null Hypothesis\")\n",
 45 |     "        else:\n",
 46 |     "            print(\"Fail to Reject NUll Hypothesis\")"
 47 |    ]
 48 |   },
 49 |   {
 50 |    "cell_type": "code",
 51 |    "execution_count": 84,
 52 |    "id": "75b8dcea",
 53 |    "metadata": {},
 54 |    "outputs": [
 55 |     {
 56 |      "name": "stdout",
 57 |      "output_type": "stream",
 58 |      "text": [
 59 |       "4.714045207910317\n",
 60 |       "1.2142337364879197e-06\n",
 61 |       "Reject Null Hypothesis\n"
 62 |      ]
 63 |     }
 64 |    ],
 65 |    "source": [
 66 |     "z_test(50,110, 100,15, \"larger\")"
 67 |    ]
 68 |   },
 69 |   {
 70 |    "cell_type": "code",
 71 |    "execution_count": 36,
 72 |    "id": "afe4e1d5",
 73 |    "metadata": {
 74 |     "scrolled": true
 75 |    },
 76 |    "outputs": [],
 77 |    "source": [
 78 |     "mean_iq = 90\n",
 79 |     "sd_iq = 20/math.sqrt(81)\n",
 80 |     "data2 = sd_iq*randn(81)+mean_iq"
 81 |    ]
 82 |   },
 83 |   {
 84 |    "cell_type": "code",
 85 |    "execution_count": 85,
 86 |    "id": "1f43eeac",
 87 |    "metadata": {},
 88 |    "outputs": [
 89 |     {
 90 |      "name": "stdout",
 91 |      "output_type": "stream",
 92 |      "text": [
 93 |       "3.5999999999999996\n",
 94 |       "0.00015910859015753396\n",
 95 |       "Reject Null Hypothesis\n"
 96 |      ]
 97 |     }
 98 |    ],
 99 |    "source": [
100 |     "z_test(81,90,82,20,\"larger\")"
101 |    ]
102 |   },
103 |   {
104 |    "cell_type": "code",
105 |    "execution_count": 38,
106 |    "id": "c6bbea08",
107 |    "metadata": {},
108 |    "outputs": [],
109 |    "source": [
110 |     " ztest_score, p_value = ztest(data2,value = null_mean, alternative='larger')"
111 |    ]
112 |   },
113 |   {
114 |    "cell_type": "code",
115 |    "execution_count": 39,
116 |    "id": "6a95ea9f",
117 |    "metadata": {},
118 |    "outputs": [
119 |     {
120 |      "name": "stdout",
121 |      "output_type": "stream",
122 |      "text": [
123 |       "Reject Null Hypothesis\n"
124 |      ]
125 |     }
126 |    ],
127 |    "source": [
128 |     "if(p_value <  alpha):\n",
129 |     "  print(\"Reject Null Hypothesis\")\n",
130 |     "else:\n",
131 |     "  print(\"Fail to Reject NUll Hypothesis\")"
132 |    ]
133 |   }
134 |  ],
135 |  "metadata": {
136 |   "kernelspec": {
137 |    "display_name": "Python 3 (ipykernel)",
138 |    "language": "python",
139 |    "name": "python3"
140 |   },
141 |   "language_info": {
142 |    "codemirror_mode": {
143 |     "name": "ipython",
144 |     "version": 3
145 |    },
146 |    "file_extension": ".py",
147 |    "mimetype": "text/x-python",
148 |    "name": "python",
149 |    "nbconvert_exporter": "python",
150 |    "pygments_lexer": "ipython3",
151 |    "version": "3.11.1"
152 |   }
153 |  },
154 |  "nbformat": 4,
155 |  "nbformat_minor": 5
156 | }
157 | 


--------------------------------------------------------------------------------
/Measure of Central Tendency/Measure of central Tendency.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "id": "ff8c0258",
  6 |    "metadata": {},
  7 |    "source": [
  8 |     "The mean (or average), the median, and the mode are usually the initial things data analysts look at in any sample data when trying to find the information about the data."
  9 |    ]
 10 |   },
 11 |   {
 12 |    "cell_type": "markdown",
 13 |    "id": "f94b730e",
 14 |    "metadata": {},
 15 |    "source": [
 16 |     "# Mean"
 17 |    ]
 18 |   },
 19 |   {
 20 |    "cell_type": "markdown",
 21 |    "id": "c5b6394d",
 22 |    "metadata": {},
 23 |    "source": [
 24 |     "Mean is average for a given set of data.\n",
 25 |     "\n",
 26 |     "To calculate the arithmetic mean of a set of data we must first add up (sum) all of the data values\n",
 27 |     "(x) and then divide the result by the number of values (n)."
 28 |    ]
 29 |   },
 30 |   {
 31 |    "cell_type": "code",
 32 |    "execution_count": 58,
 33 |    "id": "1c24685e",
 34 |    "metadata": {},
 35 |    "outputs": [],
 36 |    "source": [
 37 |     "import pandas as pd\n",
 38 |     "import numpy as np\n",
 39 |     "import seaborn as sns\n",
 40 |     "import statistics"
 41 |    ]
 42 |   },
 43 |   {
 44 |    "cell_type": "code",
 45 |    "execution_count": 41,
 46 |    "id": "272583c8",
 47 |    "metadata": {},
 48 |    "outputs": [],
 49 |    "source": [
 50 |     "data =  [1, 2, 3, 4, 4, 4, 4, 5, 6, 7, 7, 7, 8]"
 51 |    ]
 52 |   },
 53 |   {
 54 |    "cell_type": "code",
 55 |    "execution_count": 46,
 56 |    "id": "6b2f57ac",
 57 |    "metadata": {},
 58 |    "outputs": [
 59 |     {
 60 |      "data": {
 61 |       "text/plain": [
 62 |        "62"
 63 |       ]
 64 |      },
 65 |      "execution_count": 46,
 66 |      "metadata": {},
 67 |      "output_type": "execute_result"
 68 |     }
 69 |    ],
 70 |    "source": [
 71 |     "1+2+3+4+4+4+4+5+6+7+7+7+8"
 72 |    ]
 73 |   },
 74 |   {
 75 |    "cell_type": "code",
 76 |    "execution_count": 47,
 77 |    "id": "41cb2d79",
 78 |    "metadata": {},
 79 |    "outputs": [
 80 |     {
 81 |      "data": {
 82 |       "text/plain": [
 83 |        "4.769230769230769"
 84 |       ]
 85 |      },
 86 |      "execution_count": 47,
 87 |      "metadata": {},
 88 |      "output_type": "execute_result"
 89 |     }
 90 |    ],
 91 |    "source": [
 92 |     "62/13"
 93 |    ]
 94 |   },
 95 |   {
 96 |    "cell_type": "code",
 97 |    "execution_count": 43,
 98 |    "id": "fbbd597e",
 99 |    "metadata": {},
100 |    "outputs": [
101 |     {
102 |      "data": {
103 |       "text/plain": [
104 |        "4.769230769230769"
105 |       ]
106 |      },
107 |      "execution_count": 43,
108 |      "metadata": {},
109 |      "output_type": "execute_result"
110 |     }
111 |    ],
112 |    "source": [
113 |     "statistics.mean(data)"
114 |    ]
115 |   },
116 |   {
117 |    "cell_type": "code",
118 |    "execution_count": 3,
119 |    "id": "9a32bfc9",
120 |    "metadata": {},
121 |    "outputs": [],
122 |    "source": [
123 |     "df = sns.load_dataset(\"mpg\")"
124 |    ]
125 |   },
126 |   {
127 |    "cell_type": "code",
128 |    "execution_count": 4,
129 |    "id": "4abbf356",
130 |    "metadata": {},
131 |    "outputs": [
132 |     {
133 |      "data": {
134 |       "text/html": [
135 |        "<div>\n",
136 |        "<style scoped>\n",
137 |        "    .dataframe tbody tr th:only-of-type {\n",
138 |        "        vertical-align: middle;\n",
139 |        "    }\n",
140 |        "\n",
141 |        "    .dataframe tbody tr th {\n",
142 |        "        vertical-align: top;\n",
143 |        "    }\n",
144 |        "\n",
145 |        "    .dataframe thead th {\n",
146 |        "        text-align: right;\n",
147 |        "    }\n",
148 |        "</style>\n",
149 |        "<table border=\"1\" class=\"dataframe\">\n",
150 |        "  <thead>\n",
151 |        "    <tr style=\"text-align: right;\">\n",
152 |        "      <th></th>\n",
153 |        "      <th>mpg</th>\n",
154 |        "      <th>cylinders</th>\n",
155 |        "      <th>displacement</th>\n",
156 |        "      <th>horsepower</th>\n",
157 |        "      <th>weight</th>\n",
158 |        "      <th>acceleration</th>\n",
159 |        "      <th>model_year</th>\n",
160 |        "      <th>origin</th>\n",
161 |        "      <th>name</th>\n",
162 |        "    </tr>\n",
163 |        "  </thead>\n",
164 |        "  <tbody>\n",
165 |        "    <tr>\n",
166 |        "      <th>0</th>\n",
167 |        "      <td>18.0</td>\n",
168 |        "      <td>8</td>\n",
169 |        "      <td>307.0</td>\n",
170 |        "      <td>130.0</td>\n",
171 |        "      <td>3504</td>\n",
172 |        "      <td>12.0</td>\n",
173 |        "      <td>70</td>\n",
174 |        "      <td>usa</td>\n",
175 |        "      <td>chevrolet chevelle malibu</td>\n",
176 |        "    </tr>\n",
177 |        "    <tr>\n",
178 |        "      <th>1</th>\n",
179 |        "      <td>15.0</td>\n",
180 |        "      <td>8</td>\n",
181 |        "      <td>350.0</td>\n",
182 |        "      <td>165.0</td>\n",
183 |        "      <td>3693</td>\n",
184 |        "      <td>11.5</td>\n",
185 |        "      <td>70</td>\n",
186 |        "      <td>usa</td>\n",
187 |        "      <td>buick skylark 320</td>\n",
188 |        "    </tr>\n",
189 |        "  </tbody>\n",
190 |        "</table>\n",
191 |        "</div>"
192 |       ],
193 |       "text/plain": [
194 |        "    mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
195 |        "0  18.0          8         307.0       130.0    3504          12.0   \n",
196 |        "1  15.0          8         350.0       165.0    3693          11.5   \n",
197 |        "\n",
198 |        "   model_year origin                       name  \n",
199 |        "0          70    usa  chevrolet chevelle malibu  \n",
200 |        "1          70    usa          buick skylark 320  "
201 |       ]
202 |      },
203 |      "execution_count": 4,
204 |      "metadata": {},
205 |      "output_type": "execute_result"
206 |     }
207 |    ],
208 |    "source": [
209 |     "df.head(2)"
210 |    ]
211 |   },
212 |   {
213 |    "cell_type": "code",
214 |    "execution_count": 5,
215 |    "id": "1b158e47",
216 |    "metadata": {},
217 |    "outputs": [
218 |     {
219 |      "data": {
220 |       "text/plain": [
221 |        "2970.424623115578"
222 |       ]
223 |      },
224 |      "execution_count": 5,
225 |      "metadata": {},
226 |      "output_type": "execute_result"
227 |     }
228 |    ],
229 |    "source": [
230 |     "df[\"weight\"].mean()"
231 |    ]
232 |   },
233 |   {
234 |    "cell_type": "code",
235 |    "execution_count": 6,
236 |    "id": "aa911757",
237 |    "metadata": {},
238 |    "outputs": [
239 |     {
240 |      "data": {
241 |       "text/plain": [
242 |        "15.568090452261307"
243 |       ]
244 |      },
245 |      "execution_count": 6,
246 |      "metadata": {},
247 |      "output_type": "execute_result"
248 |     }
249 |    ],
250 |    "source": [
251 |     "df[\"acceleration\"].mean()"
252 |    ]
253 |   },
254 |   {
255 |    "cell_type": "code",
256 |    "execution_count": 8,
257 |    "id": "f10b70cd",
258 |    "metadata": {},
259 |    "outputs": [
260 |     {
261 |      "data": {
262 |       "text/plain": [
263 |        "193.42587939698493"
264 |       ]
265 |      },
266 |      "execution_count": 8,
267 |      "metadata": {},
268 |      "output_type": "execute_result"
269 |     }
270 |    ],
271 |    "source": [
272 |     "df[\"displacement\"].mean()"
273 |    ]
274 |   },
275 |   {
276 |    "cell_type": "code",
277 |    "execution_count": 9,
278 |    "id": "0422c22f",
279 |    "metadata": {
280 |     "scrolled": false
281 |    },
282 |    "outputs": [
283 |     {
284 |      "name": "stderr",
285 |      "output_type": "stream",
286 |      "text": [
287 |       "C:\\Users\\GFG19189\\AppData\\Local\\Temp\\ipykernel_12056\\3698961737.py:1: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
288 |       "  df.mean()\n"
289 |      ]
290 |     },
291 |     {
292 |      "data": {
293 |       "text/plain": [
294 |        "mpg               23.514573\n",
295 |        "cylinders          5.454774\n",
296 |        "displacement     193.425879\n",
297 |        "horsepower       104.469388\n",
298 |        "weight          2970.424623\n",
299 |        "acceleration      15.568090\n",
300 |        "model_year        76.010050\n",
301 |        "dtype: float64"
302 |       ]
303 |      },
304 |      "execution_count": 9,
305 |      "metadata": {},
306 |      "output_type": "execute_result"
307 |     }
308 |    ],
309 |    "source": [
310 |     "df.mean()"
311 |    ]
312 |   },
313 |   {
314 |    "cell_type": "markdown",
315 |    "id": "505395a1",
316 |    "metadata": {},
317 |    "source": [
318 |     "# Median"
319 |    ]
320 |   },
321 |   {
322 |    "cell_type": "markdown",
323 |    "id": "a1b303b1",
324 |    "metadata": {},
325 |    "source": [
326 |     "When the values in a set of data are arranged in ascending or descending order, then the value of the middle term in that set is known as the median of the set.\n",
327 |     "\n",
328 |     "The median is a better measure of the middle when there are extreme measures in the data set."
329 |    ]
330 |   },
331 |   {
332 |    "cell_type": "code",
333 |    "execution_count": 48,
334 |    "id": "ebbe0612",
335 |    "metadata": {},
336 |    "outputs": [
337 |     {
338 |      "data": {
339 |       "text/plain": [
340 |        "[1, 2, 3, 4, 4, 4, 4, 5, 6, 7, 7, 7, 8]"
341 |       ]
342 |      },
343 |      "execution_count": 48,
344 |      "metadata": {},
345 |      "output_type": "execute_result"
346 |     }
347 |    ],
348 |    "source": [
349 |     "data"
350 |    ]
351 |   },
352 |   {
353 |    "cell_type": "markdown",
354 |    "id": "ee4b75de",
355 |    "metadata": {},
356 |    "source": [
357 |     "median = 4"
358 |    ]
359 |   },
360 |   {
361 |    "cell_type": "code",
362 |    "execution_count": 49,
363 |    "id": "36fb2b5f",
364 |    "metadata": {},
365 |    "outputs": [
366 |     {
367 |      "data": {
368 |       "text/plain": [
369 |        "4"
370 |       ]
371 |      },
372 |      "execution_count": 49,
373 |      "metadata": {},
374 |      "output_type": "execute_result"
375 |     }
376 |    ],
377 |    "source": [
378 |     "statistics.median(data)"
379 |    ]
380 |   },
381 |   {
382 |    "cell_type": "code",
383 |    "execution_count": 11,
384 |    "id": "98aee804",
385 |    "metadata": {},
386 |    "outputs": [
387 |     {
388 |      "data": {
389 |       "text/plain": [
390 |        "2803.5"
391 |       ]
392 |      },
393 |      "execution_count": 11,
394 |      "metadata": {},
395 |      "output_type": "execute_result"
396 |     }
397 |    ],
398 |    "source": [
399 |     "df[\"weight\"].median()"
400 |    ]
401 |   },
402 |   {
403 |    "cell_type": "code",
404 |    "execution_count": 12,
405 |    "id": "e53b87d7",
406 |    "metadata": {},
407 |    "outputs": [
408 |     {
409 |      "data": {
410 |       "text/plain": [
411 |        "93.5"
412 |       ]
413 |      },
414 |      "execution_count": 12,
415 |      "metadata": {},
416 |      "output_type": "execute_result"
417 |     }
418 |    ],
419 |    "source": [
420 |     "df[\"horsepower\"].median()"
421 |    ]
422 |   },
423 |   {
424 |    "cell_type": "code",
425 |    "execution_count": 13,
426 |    "id": "0bc4e059",
427 |    "metadata": {},
428 |    "outputs": [
429 |     {
430 |      "data": {
431 |       "text/plain": [
432 |        "15.5"
433 |       ]
434 |      },
435 |      "execution_count": 13,
436 |      "metadata": {},
437 |      "output_type": "execute_result"
438 |     }
439 |    ],
440 |    "source": [
441 |     "df[\"acceleration\"].median()"
442 |    ]
443 |   },
444 |   {
445 |    "cell_type": "code",
446 |    "execution_count": 14,
447 |    "id": "c3a47baa",
448 |    "metadata": {
449 |     "scrolled": true
450 |    },
451 |    "outputs": [
452 |     {
453 |      "name": "stderr",
454 |      "output_type": "stream",
455 |      "text": [
456 |       "C:\\Users\\GFG19189\\AppData\\Local\\Temp\\ipykernel_12056\\530051474.py:1: FutureWarning: The default value of numeric_only in DataFrame.median is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
457 |       "  df.median()\n"
458 |      ]
459 |     },
460 |     {
461 |      "data": {
462 |       "text/plain": [
463 |        "mpg               23.0\n",
464 |        "cylinders          4.0\n",
465 |        "displacement     148.5\n",
466 |        "horsepower        93.5\n",
467 |        "weight          2803.5\n",
468 |        "acceleration      15.5\n",
469 |        "model_year        76.0\n",
470 |        "dtype: float64"
471 |       ]
472 |      },
473 |      "execution_count": 14,
474 |      "metadata": {},
475 |      "output_type": "execute_result"
476 |     }
477 |    ],
478 |    "source": [
479 |     "df.median()"
480 |    ]
481 |   },
482 |   {
483 |    "cell_type": "markdown",
484 |    "id": "37744fa2",
485 |    "metadata": {},
486 |    "source": [
487 |     "The median uses the position rather than the specific value of each data entry.\n",
488 |     "If the extreme values of a data set change, the median usually does not change.\n",
489 |     "\n",
490 |     "This is why the median is often used as the average for house prices. If one mansion costing several million dollars sells in a community of much-lower-priced\n",
491 |     "homes, the median selling price for houses in the community would be affected\n",
492 |     "very little, if at all."
493 |    ]
494 |   },
495 |   {
496 |    "cell_type": "markdown",
497 |    "id": "7eeb19ef",
498 |    "metadata": {},
499 |    "source": [
500 |     "# Mode"
501 |    ]
502 |   },
503 |   {
504 |    "cell_type": "markdown",
505 |    "id": "6896cb16",
506 |    "metadata": {},
507 |    "source": [
508 |     "The mode of a data set is the value that occurs most frequently."
509 |    ]
510 |   },
511 |   {
512 |    "cell_type": "code",
513 |    "execution_count": 42,
514 |    "id": "4c11555f",
515 |    "metadata": {},
516 |    "outputs": [
517 |     {
518 |      "data": {
519 |       "text/plain": [
520 |        "[1, 2, 3, 4, 4, 4, 4, 5, 6, 7, 7, 7, 8]"
521 |       ]
522 |      },
523 |      "execution_count": 42,
524 |      "metadata": {},
525 |      "output_type": "execute_result"
526 |     }
527 |    ],
528 |    "source": [
529 |     "data "
530 |    ]
531 |   },
532 |   {
533 |    "cell_type": "code",
534 |    "execution_count": 40,
535 |    "id": "a4de4859",
536 |    "metadata": {},
537 |    "outputs": [
538 |     {
539 |      "data": {
540 |       "text/plain": [
541 |        "4"
542 |       ]
543 |      },
544 |      "execution_count": 40,
545 |      "metadata": {},
546 |      "output_type": "execute_result"
547 |     }
548 |    ],
549 |    "source": [
550 |     "statistics.mode(data)"
551 |    ]
552 |   },
553 |   {
554 |    "cell_type": "code",
555 |    "execution_count": 17,
556 |    "id": "ea401955",
557 |    "metadata": {},
558 |    "outputs": [
559 |     {
560 |      "data": {
561 |       "text/plain": [
562 |        "0    4\n",
563 |        "Name: cylinders, dtype: int64"
564 |       ]
565 |      },
566 |      "execution_count": 17,
567 |      "metadata": {},
568 |      "output_type": "execute_result"
569 |     }
570 |    ],
571 |    "source": [
572 |     "df[\"cylinders\"].mode()"
573 |    ]
574 |   },
575 |   {
576 |    "cell_type": "code",
577 |    "execution_count": 51,
578 |    "id": "fea0bddb",
579 |    "metadata": {
580 |     "scrolled": true
581 |    },
582 |    "outputs": [
583 |     {
584 |      "data": {
585 |       "text/html": [
586 |        "<div>\n",
587 |        "<style scoped>\n",
588 |        "    .dataframe tbody tr th:only-of-type {\n",
589 |        "        vertical-align: middle;\n",
590 |        "    }\n",
591 |        "\n",
592 |        "    .dataframe tbody tr th {\n",
593 |        "        vertical-align: top;\n",
594 |        "    }\n",
595 |        "\n",
596 |        "    .dataframe thead th {\n",
597 |        "        text-align: right;\n",
598 |        "    }\n",
599 |        "</style>\n",
600 |        "<table border=\"1\" class=\"dataframe\">\n",
601 |        "  <thead>\n",
602 |        "    <tr style=\"text-align: right;\">\n",
603 |        "      <th></th>\n",
604 |        "      <th>mpg</th>\n",
605 |        "      <th>cylinders</th>\n",
606 |        "      <th>displacement</th>\n",
607 |        "      <th>horsepower</th>\n",
608 |        "      <th>weight</th>\n",
609 |        "      <th>acceleration</th>\n",
610 |        "      <th>model_year</th>\n",
611 |        "      <th>origin</th>\n",
612 |        "      <th>name</th>\n",
613 |        "    </tr>\n",
614 |        "  </thead>\n",
615 |        "  <tbody>\n",
616 |        "    <tr>\n",
617 |        "      <th>0</th>\n",
618 |        "      <td>13.0</td>\n",
619 |        "      <td>4.0</td>\n",
620 |        "      <td>97.0</td>\n",
621 |        "      <td>150.0</td>\n",
622 |        "      <td>1985</td>\n",
623 |        "      <td>14.5</td>\n",
624 |        "      <td>73.0</td>\n",
625 |        "      <td>usa</td>\n",
626 |        "      <td>ford pinto</td>\n",
627 |        "    </tr>\n",
628 |        "    <tr>\n",
629 |        "      <th>1</th>\n",
630 |        "      <td>NaN</td>\n",
631 |        "      <td>NaN</td>\n",
632 |        "      <td>NaN</td>\n",
633 |        "      <td>NaN</td>\n",
634 |        "      <td>2130</td>\n",
635 |        "      <td>NaN</td>\n",
636 |        "      <td>NaN</td>\n",
637 |        "      <td>NaN</td>\n",
638 |        "      <td>NaN</td>\n",
639 |        "    </tr>\n",
640 |        "  </tbody>\n",
641 |        "</table>\n",
642 |        "</div>"
643 |       ],
644 |       "text/plain": [
645 |        "    mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
646 |        "0  13.0        4.0          97.0       150.0    1985          14.5   \n",
647 |        "1   NaN        NaN           NaN         NaN    2130           NaN   \n",
648 |        "\n",
649 |        "   model_year origin        name  \n",
650 |        "0        73.0    usa  ford pinto  \n",
651 |        "1         NaN    NaN         NaN  "
652 |       ]
653 |      },
654 |      "execution_count": 51,
655 |      "metadata": {},
656 |      "output_type": "execute_result"
657 |     }
658 |    ],
659 |    "source": [
660 |     "df.mode(axis = 0, dropna = False)"
661 |    ]
662 |   },
663 |   {
664 |    "cell_type": "code",
665 |    "execution_count": 35,
666 |    "id": "e46f3549",
667 |    "metadata": {
668 |     "scrolled": true
669 |    },
670 |    "outputs": [
671 |     {
672 |      "data": {
673 |       "text/plain": [
674 |        "1"
675 |       ]
676 |      },
677 |      "execution_count": 35,
678 |      "metadata": {},
679 |      "output_type": "execute_result"
680 |     }
681 |    ],
682 |    "source": [
683 |     "len(df[df[\"cylinders\"]==4])"
684 |    ]
685 |   },
686 |   {
687 |    "cell_type": "markdown",
688 |    "id": "721a301c",
689 |    "metadata": {},
690 |    "source": [
691 |     " Changing just one number in a data set can change the mode dramatically"
692 |    ]
693 |   },
694 |   {
695 |    "cell_type": "code",
696 |    "execution_count": 52,
697 |    "id": "5bb08b58",
698 |    "metadata": {},
699 |    "outputs": [
700 |     {
701 |      "data": {
702 |       "text/plain": [
703 |        "4"
704 |       ]
705 |      },
706 |      "execution_count": 52,
707 |      "metadata": {},
708 |      "output_type": "execute_result"
709 |     }
710 |    ],
711 |    "source": [
712 |     "len(df[df[\"weight\"]==1985])"
713 |    ]
714 |   },
715 |   {
716 |    "cell_type": "code",
717 |    "execution_count": 53,
718 |    "id": "7bcfb506",
719 |    "metadata": {},
720 |    "outputs": [
721 |     {
722 |      "data": {
723 |       "text/plain": [
724 |        "4"
725 |       ]
726 |      },
727 |      "execution_count": 53,
728 |      "metadata": {},
729 |      "output_type": "execute_result"
730 |     }
731 |    ],
732 |    "source": [
733 |     "len(df[df[\"weight\"]==2130])"
734 |    ]
735 |   },
736 |   {
737 |    "cell_type": "code",
738 |    "execution_count": 56,
739 |    "id": "a9241e9e",
740 |    "metadata": {},
741 |    "outputs": [
742 |     {
743 |      "data": {
744 |       "text/html": [
745 |        "<div>\n",
746 |        "<style scoped>\n",
747 |        "    .dataframe tbody tr th:only-of-type {\n",
748 |        "        vertical-align: middle;\n",
749 |        "    }\n",
750 |        "\n",
751 |        "    .dataframe tbody tr th {\n",
752 |        "        vertical-align: top;\n",
753 |        "    }\n",
754 |        "\n",
755 |        "    .dataframe thead th {\n",
756 |        "        text-align: right;\n",
757 |        "    }\n",
758 |        "</style>\n",
759 |        "<table border=\"1\" class=\"dataframe\">\n",
760 |        "  <thead>\n",
761 |        "    <tr style=\"text-align: right;\">\n",
762 |        "      <th></th>\n",
763 |        "      <th>mpg</th>\n",
764 |        "      <th>cylinders</th>\n",
765 |        "      <th>displacement</th>\n",
766 |        "      <th>horsepower</th>\n",
767 |        "      <th>weight</th>\n",
768 |        "      <th>acceleration</th>\n",
769 |        "      <th>model_year</th>\n",
770 |        "    </tr>\n",
771 |        "  </thead>\n",
772 |        "  <tbody>\n",
773 |        "    <tr>\n",
774 |        "      <th>count</th>\n",
775 |        "      <td>398.000000</td>\n",
776 |        "      <td>398.000000</td>\n",
777 |        "      <td>398.000000</td>\n",
778 |        "      <td>392.000000</td>\n",
779 |        "      <td>398.000000</td>\n",
780 |        "      <td>398.000000</td>\n",
781 |        "      <td>398.000000</td>\n",
782 |        "    </tr>\n",
783 |        "    <tr>\n",
784 |        "      <th>mean</th>\n",
785 |        "      <td>23.514573</td>\n",
786 |        "      <td>5.454774</td>\n",
787 |        "      <td>193.425879</td>\n",
788 |        "      <td>104.469388</td>\n",
789 |        "      <td>2970.424623</td>\n",
790 |        "      <td>15.568090</td>\n",
791 |        "      <td>76.010050</td>\n",
792 |        "    </tr>\n",
793 |        "    <tr>\n",
794 |        "      <th>std</th>\n",
795 |        "      <td>7.815984</td>\n",
796 |        "      <td>1.701004</td>\n",
797 |        "      <td>104.269838</td>\n",
798 |        "      <td>38.491160</td>\n",
799 |        "      <td>846.841774</td>\n",
800 |        "      <td>2.757689</td>\n",
801 |        "      <td>3.697627</td>\n",
802 |        "    </tr>\n",
803 |        "    <tr>\n",
804 |        "      <th>min</th>\n",
805 |        "      <td>9.000000</td>\n",
806 |        "      <td>3.000000</td>\n",
807 |        "      <td>68.000000</td>\n",
808 |        "      <td>46.000000</td>\n",
809 |        "      <td>1613.000000</td>\n",
810 |        "      <td>8.000000</td>\n",
811 |        "      <td>70.000000</td>\n",
812 |        "    </tr>\n",
813 |        "    <tr>\n",
814 |        "      <th>25%</th>\n",
815 |        "      <td>17.500000</td>\n",
816 |        "      <td>4.000000</td>\n",
817 |        "      <td>104.250000</td>\n",
818 |        "      <td>75.000000</td>\n",
819 |        "      <td>2223.750000</td>\n",
820 |        "      <td>13.825000</td>\n",
821 |        "      <td>73.000000</td>\n",
822 |        "    </tr>\n",
823 |        "    <tr>\n",
824 |        "      <th>50%</th>\n",
825 |        "      <td>23.000000</td>\n",
826 |        "      <td>4.000000</td>\n",
827 |        "      <td>148.500000</td>\n",
828 |        "      <td>93.500000</td>\n",
829 |        "      <td>2803.500000</td>\n",
830 |        "      <td>15.500000</td>\n",
831 |        "      <td>76.000000</td>\n",
832 |        "    </tr>\n",
833 |        "    <tr>\n",
834 |        "      <th>75%</th>\n",
835 |        "      <td>29.000000</td>\n",
836 |        "      <td>8.000000</td>\n",
837 |        "      <td>262.000000</td>\n",
838 |        "      <td>126.000000</td>\n",
839 |        "      <td>3608.000000</td>\n",
840 |        "      <td>17.175000</td>\n",
841 |        "      <td>79.000000</td>\n",
842 |        "    </tr>\n",
843 |        "    <tr>\n",
844 |        "      <th>max</th>\n",
845 |        "      <td>46.600000</td>\n",
846 |        "      <td>8.000000</td>\n",
847 |        "      <td>455.000000</td>\n",
848 |        "      <td>230.000000</td>\n",
849 |        "      <td>5140.000000</td>\n",
850 |        "      <td>24.800000</td>\n",
851 |        "      <td>82.000000</td>\n",
852 |        "    </tr>\n",
853 |        "  </tbody>\n",
854 |        "</table>\n",
855 |        "</div>"
856 |       ],
857 |       "text/plain": [
858 |        "              mpg   cylinders  displacement  horsepower       weight  \\\n",
859 |        "count  398.000000  398.000000    398.000000  392.000000   398.000000   \n",
860 |        "mean    23.514573    5.454774    193.425879  104.469388  2970.424623   \n",
861 |        "std      7.815984    1.701004    104.269838   38.491160   846.841774   \n",
862 |        "min      9.000000    3.000000     68.000000   46.000000  1613.000000   \n",
863 |        "25%     17.500000    4.000000    104.250000   75.000000  2223.750000   \n",
864 |        "50%     23.000000    4.000000    148.500000   93.500000  2803.500000   \n",
865 |        "75%     29.000000    8.000000    262.000000  126.000000  3608.000000   \n",
866 |        "max     46.600000    8.000000    455.000000  230.000000  5140.000000   \n",
867 |        "\n",
868 |        "       acceleration  model_year  \n",
869 |        "count    398.000000  398.000000  \n",
870 |        "mean      15.568090   76.010050  \n",
871 |        "std        2.757689    3.697627  \n",
872 |        "min        8.000000   70.000000  \n",
873 |        "25%       13.825000   73.000000  \n",
874 |        "50%       15.500000   76.000000  \n",
875 |        "75%       17.175000   79.000000  \n",
876 |        "max       24.800000   82.000000  "
877 |       ]
878 |      },
879 |      "execution_count": 56,
880 |      "metadata": {},
881 |      "output_type": "execute_result"
882 |     }
883 |    ],
884 |    "source": [
885 |     "df.describe()"
886 |    ]
887 |   },
888 |   {
889 |    "cell_type": "code",
890 |    "execution_count": null,
891 |    "id": "d97c9fec",
892 |    "metadata": {},
893 |    "outputs": [],
894 |    "source": []
895 |   }
896 |  ],
897 |  "metadata": {
898 |   "kernelspec": {
899 |    "display_name": "Python 3 (ipykernel)",
900 |    "language": "python",
901 |    "name": "python3"
902 |   },
903 |   "language_info": {
904 |    "codemirror_mode": {
905 |     "name": "ipython",
906 |     "version": 3
907 |    },
908 |    "file_extension": ".py",
909 |    "mimetype": "text/x-python",
910 |    "name": "python",
911 |    "nbconvert_exporter": "python",
912 |    "pygments_lexer": "ipython3",
913 |    "version": "3.11.1"
914 |   }
915 |  },
916 |  "nbformat": 4,
917 |  "nbformat_minor": 5
918 | }
919 | 


--------------------------------------------------------------------------------
/Measure of Dispersion/Measure of Dispersion.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 2,
  6 |    "id": "142b7ea6",
  7 |    "metadata": {},
  8 |    "outputs": [],
  9 |    "source": [
 10 |     "import pandas as pd\n",
 11 |     "import statistics\n",
 12 |     "import seaborn as sns"
 13 |    ]
 14 |   },
 15 |   {
 16 |    "cell_type": "code",
 17 |    "execution_count": 3,
 18 |    "id": "95ddd3a4",
 19 |    "metadata": {},
 20 |    "outputs": [],
 21 |    "source": [
 22 |     "data1 = [45,42,42,41,40]\n",
 23 |     "data2 =[54,48,42,33,33]"
 24 |    ]
 25 |   },
 26 |   {
 27 |    "cell_type": "code",
 28 |    "execution_count": 4,
 29 |    "id": "04fd00a7",
 30 |    "metadata": {},
 31 |    "outputs": [
 32 |     {
 33 |      "data": {
 34 |       "text/plain": [
 35 |        "42"
 36 |       ]
 37 |      },
 38 |      "execution_count": 4,
 39 |      "metadata": {},
 40 |      "output_type": "execute_result"
 41 |     }
 42 |    ],
 43 |    "source": [
 44 |     "statistics.mean(data1)"
 45 |    ]
 46 |   },
 47 |   {
 48 |    "cell_type": "code",
 49 |    "execution_count": 5,
 50 |    "id": "0d142bc2",
 51 |    "metadata": {},
 52 |    "outputs": [
 53 |     {
 54 |      "data": {
 55 |       "text/plain": [
 56 |        "42"
 57 |       ]
 58 |      },
 59 |      "execution_count": 5,
 60 |      "metadata": {},
 61 |      "output_type": "execute_result"
 62 |     }
 63 |    ],
 64 |    "source": [
 65 |     "statistics.mean(data2)"
 66 |    ]
 67 |   },
 68 |   {
 69 |    "cell_type": "code",
 70 |    "execution_count": 6,
 71 |    "id": "c75178ff",
 72 |    "metadata": {},
 73 |    "outputs": [
 74 |     {
 75 |      "data": {
 76 |       "text/plain": [
 77 |        "3.5"
 78 |       ]
 79 |      },
 80 |      "execution_count": 6,
 81 |      "metadata": {},
 82 |      "output_type": "execute_result"
 83 |     }
 84 |    ],
 85 |    "source": [
 86 |     "statistics.variance(data1)"
 87 |    ]
 88 |   },
 89 |   {
 90 |    "cell_type": "code",
 91 |    "execution_count": 7,
 92 |    "id": "9f0068be",
 93 |    "metadata": {},
 94 |    "outputs": [
 95 |     {
 96 |      "data": {
 97 |       "text/plain": [
 98 |        "1.6733200530681511"
 99 |       ]
100 |      },
101 |      "execution_count": 7,
102 |      "metadata": {},
103 |      "output_type": "execute_result"
104 |     }
105 |    ],
106 |    "source": [
107 |     "statistics.pstdev(data1)"
108 |    ]
109 |   },
110 |   {
111 |    "cell_type": "code",
112 |    "execution_count": 8,
113 |    "id": "8334e792",
114 |    "metadata": {},
115 |    "outputs": [],
116 |    "source": [
117 |     "df = sns.load_dataset(\"mpg\")"
118 |    ]
119 |   },
120 |   {
121 |    "cell_type": "code",
122 |    "execution_count": 9,
123 |    "id": "bff64009",
124 |    "metadata": {
125 |     "scrolled": true
126 |    },
127 |    "outputs": [
128 |     {
129 |      "data": {
130 |       "text/html": [
131 |        "<div>\n",
132 |        "<style scoped>\n",
133 |        "    .dataframe tbody tr th:only-of-type {\n",
134 |        "        vertical-align: middle;\n",
135 |        "    }\n",
136 |        "\n",
137 |        "    .dataframe tbody tr th {\n",
138 |        "        vertical-align: top;\n",
139 |        "    }\n",
140 |        "\n",
141 |        "    .dataframe thead th {\n",
142 |        "        text-align: right;\n",
143 |        "    }\n",
144 |        "</style>\n",
145 |        "<table border=\"1\" class=\"dataframe\">\n",
146 |        "  <thead>\n",
147 |        "    <tr style=\"text-align: right;\">\n",
148 |        "      <th></th>\n",
149 |        "      <th>mpg</th>\n",
150 |        "      <th>cylinders</th>\n",
151 |        "      <th>displacement</th>\n",
152 |        "      <th>horsepower</th>\n",
153 |        "      <th>weight</th>\n",
154 |        "      <th>acceleration</th>\n",
155 |        "      <th>model_year</th>\n",
156 |        "      <th>origin</th>\n",
157 |        "      <th>name</th>\n",
158 |        "    </tr>\n",
159 |        "  </thead>\n",
160 |        "  <tbody>\n",
161 |        "    <tr>\n",
162 |        "      <th>0</th>\n",
163 |        "      <td>18.0</td>\n",
164 |        "      <td>8</td>\n",
165 |        "      <td>307.0</td>\n",
166 |        "      <td>130.0</td>\n",
167 |        "      <td>3504</td>\n",
168 |        "      <td>12.0</td>\n",
169 |        "      <td>70</td>\n",
170 |        "      <td>usa</td>\n",
171 |        "      <td>chevrolet chevelle malibu</td>\n",
172 |        "    </tr>\n",
173 |        "    <tr>\n",
174 |        "      <th>1</th>\n",
175 |        "      <td>15.0</td>\n",
176 |        "      <td>8</td>\n",
177 |        "      <td>350.0</td>\n",
178 |        "      <td>165.0</td>\n",
179 |        "      <td>3693</td>\n",
180 |        "      <td>11.5</td>\n",
181 |        "      <td>70</td>\n",
182 |        "      <td>usa</td>\n",
183 |        "      <td>buick skylark 320</td>\n",
184 |        "    </tr>\n",
185 |        "    <tr>\n",
186 |        "      <th>2</th>\n",
187 |        "      <td>18.0</td>\n",
188 |        "      <td>8</td>\n",
189 |        "      <td>318.0</td>\n",
190 |        "      <td>150.0</td>\n",
191 |        "      <td>3436</td>\n",
192 |        "      <td>11.0</td>\n",
193 |        "      <td>70</td>\n",
194 |        "      <td>usa</td>\n",
195 |        "      <td>plymouth satellite</td>\n",
196 |        "    </tr>\n",
197 |        "    <tr>\n",
198 |        "      <th>3</th>\n",
199 |        "      <td>16.0</td>\n",
200 |        "      <td>8</td>\n",
201 |        "      <td>304.0</td>\n",
202 |        "      <td>150.0</td>\n",
203 |        "      <td>3433</td>\n",
204 |        "      <td>12.0</td>\n",
205 |        "      <td>70</td>\n",
206 |        "      <td>usa</td>\n",
207 |        "      <td>amc rebel sst</td>\n",
208 |        "    </tr>\n",
209 |        "    <tr>\n",
210 |        "      <th>4</th>\n",
211 |        "      <td>17.0</td>\n",
212 |        "      <td>8</td>\n",
213 |        "      <td>302.0</td>\n",
214 |        "      <td>140.0</td>\n",
215 |        "      <td>3449</td>\n",
216 |        "      <td>10.5</td>\n",
217 |        "      <td>70</td>\n",
218 |        "      <td>usa</td>\n",
219 |        "      <td>ford torino</td>\n",
220 |        "    </tr>\n",
221 |        "  </tbody>\n",
222 |        "</table>\n",
223 |        "</div>"
224 |       ],
225 |       "text/plain": [
226 |        "    mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
227 |        "0  18.0          8         307.0       130.0    3504          12.0   \n",
228 |        "1  15.0          8         350.0       165.0    3693          11.5   \n",
229 |        "2  18.0          8         318.0       150.0    3436          11.0   \n",
230 |        "3  16.0          8         304.0       150.0    3433          12.0   \n",
231 |        "4  17.0          8         302.0       140.0    3449          10.5   \n",
232 |        "\n",
233 |        "   model_year origin                       name  \n",
234 |        "0          70    usa  chevrolet chevelle malibu  \n",
235 |        "1          70    usa          buick skylark 320  \n",
236 |        "2          70    usa         plymouth satellite  \n",
237 |        "3          70    usa              amc rebel sst  \n",
238 |        "4          70    usa                ford torino  "
239 |       ]
240 |      },
241 |      "execution_count": 9,
242 |      "metadata": {},
243 |      "output_type": "execute_result"
244 |     }
245 |    ],
246 |    "source": [
247 |     "df.head()"
248 |    ]
249 |   },
250 |   {
251 |    "cell_type": "code",
252 |    "execution_count": 10,
253 |    "id": "eadf7b26",
254 |    "metadata": {},
255 |    "outputs": [],
256 |    "source": [
257 |     "data =  [1, 2, 3, 4, 4, 4, 4, 5, 6, 7, 7, 7, 8]"
258 |    ]
259 |   },
260 |   {
261 |    "cell_type": "code",
262 |    "execution_count": 11,
263 |    "id": "a1754a55",
264 |    "metadata": {},
265 |    "outputs": [
266 |     {
267 |      "data": {
268 |       "text/plain": [
269 |        "7"
270 |       ]
271 |      },
272 |      "execution_count": 11,
273 |      "metadata": {},
274 |      "output_type": "execute_result"
275 |     }
276 |    ],
277 |    "source": [
278 |     "8-1"
279 |    ]
280 |   },
281 |   {
282 |    "cell_type": "code",
283 |    "execution_count": 12,
284 |    "id": "2511f30f",
285 |    "metadata": {},
286 |    "outputs": [
287 |     {
288 |      "data": {
289 |       "text/plain": [
290 |        "4.769230769230769"
291 |       ]
292 |      },
293 |      "execution_count": 12,
294 |      "metadata": {},
295 |      "output_type": "execute_result"
296 |     }
297 |    ],
298 |    "source": [
299 |     "statistics.mean(data)"
300 |    ]
301 |   },
302 |   {
303 |    "cell_type": "code",
304 |    "execution_count": 13,
305 |    "id": "f82a663e",
306 |    "metadata": {},
307 |    "outputs": [
308 |     {
309 |      "data": {
310 |       "text/plain": [
311 |        "2.0438969623979113"
312 |       ]
313 |      },
314 |      "execution_count": 13,
315 |      "metadata": {},
316 |      "output_type": "execute_result"
317 |     }
318 |    ],
319 |    "source": [
320 |     "statistics.pstdev(data)"
321 |    ]
322 |   },
323 |   {
324 |    "cell_type": "code",
325 |    "execution_count": 14,
326 |    "id": "575f8bd3",
327 |    "metadata": {
328 |     "scrolled": true
329 |    },
330 |    "outputs": [
331 |     {
332 |      "data": {
333 |       "text/plain": [
334 |        "37.6"
335 |       ]
336 |      },
337 |      "execution_count": 14,
338 |      "metadata": {},
339 |      "output_type": "execute_result"
340 |     }
341 |    ],
342 |    "source": [
343 |     "df[\"mpg\"].max()-df[\"mpg\"].min()"
344 |    ]
345 |   },
346 |   {
347 |    "cell_type": "code",
348 |    "execution_count": 15,
349 |    "id": "2fabd3b2",
350 |    "metadata": {
351 |     "scrolled": true
352 |    },
353 |    "outputs": [
354 |     {
355 |      "data": {
356 |       "text/plain": [
357 |        "3527"
358 |       ]
359 |      },
360 |      "execution_count": 15,
361 |      "metadata": {},
362 |      "output_type": "execute_result"
363 |     }
364 |    ],
365 |    "source": [
366 |     "df[\"weight\"].max()-df[\"weight\"].min()"
367 |    ]
368 |   },
369 |   {
370 |    "cell_type": "code",
371 |    "execution_count": 16,
372 |    "id": "e18187df",
373 |    "metadata": {},
374 |    "outputs": [
375 |     {
376 |      "data": {
377 |       "text/plain": [
378 |        "61.0896107742744"
379 |       ]
380 |      },
381 |      "execution_count": 16,
382 |      "metadata": {},
383 |      "output_type": "execute_result"
384 |     }
385 |    ],
386 |    "source": [
387 |     "statistics.variance(df[\"mpg\"])"
388 |    ]
389 |   },
390 |   {
391 |    "cell_type": "code",
392 |    "execution_count": 17,
393 |    "id": "34ee0060",
394 |    "metadata": {},
395 |    "outputs": [
396 |     {
397 |      "data": {
398 |       "text/plain": [
399 |        "7.806159061274432"
400 |       ]
401 |      },
402 |      "execution_count": 17,
403 |      "metadata": {},
404 |      "output_type": "execute_result"
405 |     }
406 |    ],
407 |    "source": [
408 |     "statistics.pstdev(df[\"mpg\"])"
409 |    ]
410 |   },
411 |   {
412 |    "cell_type": "code",
413 |    "execution_count": 18,
414 |    "id": "b144a54e",
415 |    "metadata": {},
416 |    "outputs": [
417 |     {
418 |      "data": {
419 |       "text/html": [
420 |        "<div>\n",
421 |        "<style scoped>\n",
422 |        "    .dataframe tbody tr th:only-of-type {\n",
423 |        "        vertical-align: middle;\n",
424 |        "    }\n",
425 |        "\n",
426 |        "    .dataframe tbody tr th {\n",
427 |        "        vertical-align: top;\n",
428 |        "    }\n",
429 |        "\n",
430 |        "    .dataframe thead th {\n",
431 |        "        text-align: right;\n",
432 |        "    }\n",
433 |        "</style>\n",
434 |        "<table border=\"1\" class=\"dataframe\">\n",
435 |        "  <thead>\n",
436 |        "    <tr style=\"text-align: right;\">\n",
437 |        "      <th></th>\n",
438 |        "      <th>mpg</th>\n",
439 |        "      <th>cylinders</th>\n",
440 |        "      <th>displacement</th>\n",
441 |        "      <th>horsepower</th>\n",
442 |        "      <th>weight</th>\n",
443 |        "      <th>acceleration</th>\n",
444 |        "      <th>model_year</th>\n",
445 |        "    </tr>\n",
446 |        "  </thead>\n",
447 |        "  <tbody>\n",
448 |        "    <tr>\n",
449 |        "      <th>count</th>\n",
450 |        "      <td>398.000000</td>\n",
451 |        "      <td>398.000000</td>\n",
452 |        "      <td>398.000000</td>\n",
453 |        "      <td>392.000000</td>\n",
454 |        "      <td>398.000000</td>\n",
455 |        "      <td>398.000000</td>\n",
456 |        "      <td>398.000000</td>\n",
457 |        "    </tr>\n",
458 |        "    <tr>\n",
459 |        "      <th>mean</th>\n",
460 |        "      <td>23.514573</td>\n",
461 |        "      <td>5.454774</td>\n",
462 |        "      <td>193.425879</td>\n",
463 |        "      <td>104.469388</td>\n",
464 |        "      <td>2970.424623</td>\n",
465 |        "      <td>15.568090</td>\n",
466 |        "      <td>76.010050</td>\n",
467 |        "    </tr>\n",
468 |        "    <tr>\n",
469 |        "      <th>std</th>\n",
470 |        "      <td>7.815984</td>\n",
471 |        "      <td>1.701004</td>\n",
472 |        "      <td>104.269838</td>\n",
473 |        "      <td>38.491160</td>\n",
474 |        "      <td>846.841774</td>\n",
475 |        "      <td>2.757689</td>\n",
476 |        "      <td>3.697627</td>\n",
477 |        "    </tr>\n",
478 |        "    <tr>\n",
479 |        "      <th>min</th>\n",
480 |        "      <td>9.000000</td>\n",
481 |        "      <td>3.000000</td>\n",
482 |        "      <td>68.000000</td>\n",
483 |        "      <td>46.000000</td>\n",
484 |        "      <td>1613.000000</td>\n",
485 |        "      <td>8.000000</td>\n",
486 |        "      <td>70.000000</td>\n",
487 |        "    </tr>\n",
488 |        "    <tr>\n",
489 |        "      <th>25%</th>\n",
490 |        "      <td>17.500000</td>\n",
491 |        "      <td>4.000000</td>\n",
492 |        "      <td>104.250000</td>\n",
493 |        "      <td>75.000000</td>\n",
494 |        "      <td>2223.750000</td>\n",
495 |        "      <td>13.825000</td>\n",
496 |        "      <td>73.000000</td>\n",
497 |        "    </tr>\n",
498 |        "    <tr>\n",
499 |        "      <th>50%</th>\n",
500 |        "      <td>23.000000</td>\n",
501 |        "      <td>4.000000</td>\n",
502 |        "      <td>148.500000</td>\n",
503 |        "      <td>93.500000</td>\n",
504 |        "      <td>2803.500000</td>\n",
505 |        "      <td>15.500000</td>\n",
506 |        "      <td>76.000000</td>\n",
507 |        "    </tr>\n",
508 |        "    <tr>\n",
509 |        "      <th>75%</th>\n",
510 |        "      <td>29.000000</td>\n",
511 |        "      <td>8.000000</td>\n",
512 |        "      <td>262.000000</td>\n",
513 |        "      <td>126.000000</td>\n",
514 |        "      <td>3608.000000</td>\n",
515 |        "      <td>17.175000</td>\n",
516 |        "      <td>79.000000</td>\n",
517 |        "    </tr>\n",
518 |        "    <tr>\n",
519 |        "      <th>max</th>\n",
520 |        "      <td>46.600000</td>\n",
521 |        "      <td>8.000000</td>\n",
522 |        "      <td>455.000000</td>\n",
523 |        "      <td>230.000000</td>\n",
524 |        "      <td>5140.000000</td>\n",
525 |        "      <td>24.800000</td>\n",
526 |        "      <td>82.000000</td>\n",
527 |        "    </tr>\n",
528 |        "  </tbody>\n",
529 |        "</table>\n",
530 |        "</div>"
531 |       ],
532 |       "text/plain": [
533 |        "              mpg   cylinders  displacement  horsepower       weight  \\\n",
534 |        "count  398.000000  398.000000    398.000000  392.000000   398.000000   \n",
535 |        "mean    23.514573    5.454774    193.425879  104.469388  2970.424623   \n",
536 |        "std      7.815984    1.701004    104.269838   38.491160   846.841774   \n",
537 |        "min      9.000000    3.000000     68.000000   46.000000  1613.000000   \n",
538 |        "25%     17.500000    4.000000    104.250000   75.000000  2223.750000   \n",
539 |        "50%     23.000000    4.000000    148.500000   93.500000  2803.500000   \n",
540 |        "75%     29.000000    8.000000    262.000000  126.000000  3608.000000   \n",
541 |        "max     46.600000    8.000000    455.000000  230.000000  5140.000000   \n",
542 |        "\n",
543 |        "       acceleration  model_year  \n",
544 |        "count    398.000000  398.000000  \n",
545 |        "mean      15.568090   76.010050  \n",
546 |        "std        2.757689    3.697627  \n",
547 |        "min        8.000000   70.000000  \n",
548 |        "25%       13.825000   73.000000  \n",
549 |        "50%       15.500000   76.000000  \n",
550 |        "75%       17.175000   79.000000  \n",
551 |        "max       24.800000   82.000000  "
552 |       ]
553 |      },
554 |      "execution_count": 18,
555 |      "metadata": {},
556 |      "output_type": "execute_result"
557 |     }
558 |    ],
559 |    "source": [
560 |     "df.describe()"
561 |    ]
562 |   },
563 |   {
564 |    "cell_type": "code",
565 |    "execution_count": 19,
566 |    "id": "c407205d",
567 |    "metadata": {},
568 |    "outputs": [
569 |     {
570 |      "data": {
571 |       "text/plain": [
572 |        "0.3319711187745463"
573 |       ]
574 |      },
575 |      "execution_count": 19,
576 |      "metadata": {},
577 |      "output_type": "execute_result"
578 |     }
579 |    ],
580 |    "source": [
581 |     "statistics.pstdev(df[\"mpg\"])/statistics.mean(df[\"mpg\"])"
582 |    ]
583 |   },
584 |   {
585 |    "cell_type": "code",
586 |    "execution_count": 20,
587 |    "id": "c9700a5c",
588 |    "metadata": {},
589 |    "outputs": [
590 |     {
591 |      "data": {
592 |       "text/plain": [
593 |        "0.2847327708429478"
594 |       ]
595 |      },
596 |      "execution_count": 20,
597 |      "metadata": {},
598 |      "output_type": "execute_result"
599 |     }
600 |    ],
601 |    "source": [
602 |     "statistics.pstdev(df[\"weight\"])/statistics.mean(df[\"weight\"])"
603 |    ]
604 |   },
605 |   {
606 |    "cell_type": "code",
607 |    "execution_count": 22,
608 |    "id": "1e1d5ffb",
609 |    "metadata": {},
610 |    "outputs": [
611 |     {
612 |      "data": {
613 |       "text/html": [
614 |        "<div>\n",
615 |        "<style scoped>\n",
616 |        "    .dataframe tbody tr th:only-of-type {\n",
617 |        "        vertical-align: middle;\n",
618 |        "    }\n",
619 |        "\n",
620 |        "    .dataframe tbody tr th {\n",
621 |        "        vertical-align: top;\n",
622 |        "    }\n",
623 |        "\n",
624 |        "    .dataframe thead th {\n",
625 |        "        text-align: right;\n",
626 |        "    }\n",
627 |        "</style>\n",
628 |        "<table border=\"1\" class=\"dataframe\">\n",
629 |        "  <thead>\n",
630 |        "    <tr style=\"text-align: right;\">\n",
631 |        "      <th></th>\n",
632 |        "      <th>mpg</th>\n",
633 |        "      <th>cylinders</th>\n",
634 |        "      <th>displacement</th>\n",
635 |        "      <th>horsepower</th>\n",
636 |        "      <th>weight</th>\n",
637 |        "      <th>acceleration</th>\n",
638 |        "      <th>model_year</th>\n",
639 |        "    </tr>\n",
640 |        "  </thead>\n",
641 |        "  <tbody>\n",
642 |        "    <tr>\n",
643 |        "      <th>count</th>\n",
644 |        "      <td>398.000000</td>\n",
645 |        "      <td>398.000000</td>\n",
646 |        "      <td>398.000000</td>\n",
647 |        "      <td>392.000000</td>\n",
648 |        "      <td>398.000000</td>\n",
649 |        "      <td>398.000000</td>\n",
650 |        "      <td>398.000000</td>\n",
651 |        "    </tr>\n",
652 |        "    <tr>\n",
653 |        "      <th>mean</th>\n",
654 |        "      <td>23.514573</td>\n",
655 |        "      <td>5.454774</td>\n",
656 |        "      <td>193.425879</td>\n",
657 |        "      <td>104.469388</td>\n",
658 |        "      <td>2970.424623</td>\n",
659 |        "      <td>15.568090</td>\n",
660 |        "      <td>76.010050</td>\n",
661 |        "    </tr>\n",
662 |        "    <tr>\n",
663 |        "      <th>std</th>\n",
664 |        "      <td>7.815984</td>\n",
665 |        "      <td>1.701004</td>\n",
666 |        "      <td>104.269838</td>\n",
667 |        "      <td>38.491160</td>\n",
668 |        "      <td>846.841774</td>\n",
669 |        "      <td>2.757689</td>\n",
670 |        "      <td>3.697627</td>\n",
671 |        "    </tr>\n",
672 |        "    <tr>\n",
673 |        "      <th>min</th>\n",
674 |        "      <td>9.000000</td>\n",
675 |        "      <td>3.000000</td>\n",
676 |        "      <td>68.000000</td>\n",
677 |        "      <td>46.000000</td>\n",
678 |        "      <td>1613.000000</td>\n",
679 |        "      <td>8.000000</td>\n",
680 |        "      <td>70.000000</td>\n",
681 |        "    </tr>\n",
682 |        "    <tr>\n",
683 |        "      <th>25%</th>\n",
684 |        "      <td>17.500000</td>\n",
685 |        "      <td>4.000000</td>\n",
686 |        "      <td>104.250000</td>\n",
687 |        "      <td>75.000000</td>\n",
688 |        "      <td>2223.750000</td>\n",
689 |        "      <td>13.825000</td>\n",
690 |        "      <td>73.000000</td>\n",
691 |        "    </tr>\n",
692 |        "    <tr>\n",
693 |        "      <th>50%</th>\n",
694 |        "      <td>23.000000</td>\n",
695 |        "      <td>4.000000</td>\n",
696 |        "      <td>148.500000</td>\n",
697 |        "      <td>93.500000</td>\n",
698 |        "      <td>2803.500000</td>\n",
699 |        "      <td>15.500000</td>\n",
700 |        "      <td>76.000000</td>\n",
701 |        "    </tr>\n",
702 |        "    <tr>\n",
703 |        "      <th>75%</th>\n",
704 |        "      <td>29.000000</td>\n",
705 |        "      <td>8.000000</td>\n",
706 |        "      <td>262.000000</td>\n",
707 |        "      <td>126.000000</td>\n",
708 |        "      <td>3608.000000</td>\n",
709 |        "      <td>17.175000</td>\n",
710 |        "      <td>79.000000</td>\n",
711 |        "    </tr>\n",
712 |        "    <tr>\n",
713 |        "      <th>max</th>\n",
714 |        "      <td>46.600000</td>\n",
715 |        "      <td>8.000000</td>\n",
716 |        "      <td>455.000000</td>\n",
717 |        "      <td>230.000000</td>\n",
718 |        "      <td>5140.000000</td>\n",
719 |        "      <td>24.800000</td>\n",
720 |        "      <td>82.000000</td>\n",
721 |        "    </tr>\n",
722 |        "  </tbody>\n",
723 |        "</table>\n",
724 |        "</div>"
725 |       ],
726 |       "text/plain": [
727 |        "              mpg   cylinders  displacement  horsepower       weight  \\\n",
728 |        "count  398.000000  398.000000    398.000000  392.000000   398.000000   \n",
729 |        "mean    23.514573    5.454774    193.425879  104.469388  2970.424623   \n",
730 |        "std      7.815984    1.701004    104.269838   38.491160   846.841774   \n",
731 |        "min      9.000000    3.000000     68.000000   46.000000  1613.000000   \n",
732 |        "25%     17.500000    4.000000    104.250000   75.000000  2223.750000   \n",
733 |        "50%     23.000000    4.000000    148.500000   93.500000  2803.500000   \n",
734 |        "75%     29.000000    8.000000    262.000000  126.000000  3608.000000   \n",
735 |        "max     46.600000    8.000000    455.000000  230.000000  5140.000000   \n",
736 |        "\n",
737 |        "       acceleration  model_year  \n",
738 |        "count    398.000000  398.000000  \n",
739 |        "mean      15.568090   76.010050  \n",
740 |        "std        2.757689    3.697627  \n",
741 |        "min        8.000000   70.000000  \n",
742 |        "25%       13.825000   73.000000  \n",
743 |        "50%       15.500000   76.000000  \n",
744 |        "75%       17.175000   79.000000  \n",
745 |        "max       24.800000   82.000000  "
746 |       ]
747 |      },
748 |      "execution_count": 22,
749 |      "metadata": {},
750 |      "output_type": "execute_result"
751 |     }
752 |    ],
753 |    "source": [
754 |     "df.describe()"
755 |    ]
756 |   },
757 |   {
758 |    "cell_type": "code",
759 |    "execution_count": 25,
760 |    "id": "de1108c8",
761 |    "metadata": {},
762 |    "outputs": [
763 |     {
764 |      "data": {
765 |       "text/plain": [
766 |        "0.03984095364447979"
767 |       ]
768 |      },
769 |      "execution_count": 25,
770 |      "metadata": {},
771 |      "output_type": "execute_result"
772 |     }
773 |    ],
774 |    "source": [
775 |     "statistics.pstdev(data1)/statistics.mean(data1)"
776 |    ]
777 |   },
778 |   {
779 |    "cell_type": "code",
780 |    "execution_count": 26,
781 |    "id": "e74acab0",
782 |    "metadata": {},
783 |    "outputs": [
784 |     {
785 |      "data": {
786 |       "text/plain": [
787 |        "0.19691498217271744"
788 |       ]
789 |      },
790 |      "execution_count": 26,
791 |      "metadata": {},
792 |      "output_type": "execute_result"
793 |     }
794 |    ],
795 |    "source": [
796 |     "statistics.pstdev(data2)/statistics.mean(data2)"
797 |    ]
798 |   }
799 |  ],
800 |  "metadata": {
801 |   "kernelspec": {
802 |    "display_name": "Python 3 (ipykernel)",
803 |    "language": "python",
804 |    "name": "python3"
805 |   },
806 |   "language_info": {
807 |    "codemirror_mode": {
808 |     "name": "ipython",
809 |     "version": 3
810 |    },
811 |    "file_extension": ".py",
812 |    "mimetype": "text/x-python",
813 |    "name": "python",
814 |    "nbconvert_exporter": "python",
815 |    "pygments_lexer": "ipython3",
816 |    "version": "3.11.1"
817 |   }
818 |  },
819 |  "nbformat": 4,
820 |  "nbformat_minor": 5
821 | }
822 | 


--------------------------------------------------------------------------------
/Measure of Shape/Measure of Shape.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 2,
  6 |    "id": "fcd711f3",
  7 |    "metadata": {},
  8 |    "outputs": [],
  9 |    "source": [
 10 |     "import pandas as pd\n",
 11 |     "import seaborn as sns"
 12 |    ]
 13 |   },
 14 |   {
 15 |    "cell_type": "code",
 16 |    "execution_count": 3,
 17 |    "id": "ffac47a7",
 18 |    "metadata": {},
 19 |    "outputs": [],
 20 |    "source": [
 21 |     "df = sns.load_dataset(\"mpg\")"
 22 |    ]
 23 |   },
 24 |   {
 25 |    "cell_type": "code",
 26 |    "execution_count": 4,
 27 |    "id": "5e87c097",
 28 |    "metadata": {},
 29 |    "outputs": [
 30 |     {
 31 |      "data": {
 32 |       "text/html": [
 33 |        "<div>\n",
 34 |        "<style scoped>\n",
 35 |        "    .dataframe tbody tr th:only-of-type {\n",
 36 |        "        vertical-align: middle;\n",
 37 |        "    }\n",
 38 |        "\n",
 39 |        "    .dataframe tbody tr th {\n",
 40 |        "        vertical-align: top;\n",
 41 |        "    }\n",
 42 |        "\n",
 43 |        "    .dataframe thead th {\n",
 44 |        "        text-align: right;\n",
 45 |        "    }\n",
 46 |        "</style>\n",
 47 |        "<table border=\"1\" class=\"dataframe\">\n",
 48 |        "  <thead>\n",
 49 |        "    <tr style=\"text-align: right;\">\n",
 50 |        "      <th></th>\n",
 51 |        "      <th>mpg</th>\n",
 52 |        "      <th>cylinders</th>\n",
 53 |        "      <th>displacement</th>\n",
 54 |        "      <th>horsepower</th>\n",
 55 |        "      <th>weight</th>\n",
 56 |        "      <th>acceleration</th>\n",
 57 |        "      <th>model_year</th>\n",
 58 |        "      <th>origin</th>\n",
 59 |        "      <th>name</th>\n",
 60 |        "    </tr>\n",
 61 |        "  </thead>\n",
 62 |        "  <tbody>\n",
 63 |        "    <tr>\n",
 64 |        "      <th>0</th>\n",
 65 |        "      <td>18.0</td>\n",
 66 |        "      <td>8</td>\n",
 67 |        "      <td>307.0</td>\n",
 68 |        "      <td>130.0</td>\n",
 69 |        "      <td>3504</td>\n",
 70 |        "      <td>12.0</td>\n",
 71 |        "      <td>70</td>\n",
 72 |        "      <td>usa</td>\n",
 73 |        "      <td>chevrolet chevelle malibu</td>\n",
 74 |        "    </tr>\n",
 75 |        "    <tr>\n",
 76 |        "      <th>1</th>\n",
 77 |        "      <td>15.0</td>\n",
 78 |        "      <td>8</td>\n",
 79 |        "      <td>350.0</td>\n",
 80 |        "      <td>165.0</td>\n",
 81 |        "      <td>3693</td>\n",
 82 |        "      <td>11.5</td>\n",
 83 |        "      <td>70</td>\n",
 84 |        "      <td>usa</td>\n",
 85 |        "      <td>buick skylark 320</td>\n",
 86 |        "    </tr>\n",
 87 |        "    <tr>\n",
 88 |        "      <th>2</th>\n",
 89 |        "      <td>18.0</td>\n",
 90 |        "      <td>8</td>\n",
 91 |        "      <td>318.0</td>\n",
 92 |        "      <td>150.0</td>\n",
 93 |        "      <td>3436</td>\n",
 94 |        "      <td>11.0</td>\n",
 95 |        "      <td>70</td>\n",
 96 |        "      <td>usa</td>\n",
 97 |        "      <td>plymouth satellite</td>\n",
 98 |        "    </tr>\n",
 99 |        "    <tr>\n",
100 |        "      <th>3</th>\n",
101 |        "      <td>16.0</td>\n",
102 |        "      <td>8</td>\n",
103 |        "      <td>304.0</td>\n",
104 |        "      <td>150.0</td>\n",
105 |        "      <td>3433</td>\n",
106 |        "      <td>12.0</td>\n",
107 |        "      <td>70</td>\n",
108 |        "      <td>usa</td>\n",
109 |        "      <td>amc rebel sst</td>\n",
110 |        "    </tr>\n",
111 |        "    <tr>\n",
112 |        "      <th>4</th>\n",
113 |        "      <td>17.0</td>\n",
114 |        "      <td>8</td>\n",
115 |        "      <td>302.0</td>\n",
116 |        "      <td>140.0</td>\n",
117 |        "      <td>3449</td>\n",
118 |        "      <td>10.5</td>\n",
119 |        "      <td>70</td>\n",
120 |        "      <td>usa</td>\n",
121 |        "      <td>ford torino</td>\n",
122 |        "    </tr>\n",
123 |        "  </tbody>\n",
124 |        "</table>\n",
125 |        "</div>"
126 |       ],
127 |       "text/plain": [
128 |        "    mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
129 |        "0  18.0          8         307.0       130.0    3504          12.0   \n",
130 |        "1  15.0          8         350.0       165.0    3693          11.5   \n",
131 |        "2  18.0          8         318.0       150.0    3436          11.0   \n",
132 |        "3  16.0          8         304.0       150.0    3433          12.0   \n",
133 |        "4  17.0          8         302.0       140.0    3449          10.5   \n",
134 |        "\n",
135 |        "   model_year origin                       name  \n",
136 |        "0          70    usa  chevrolet chevelle malibu  \n",
137 |        "1          70    usa          buick skylark 320  \n",
138 |        "2          70    usa         plymouth satellite  \n",
139 |        "3          70    usa              amc rebel sst  \n",
140 |        "4          70    usa                ford torino  "
141 |       ]
142 |      },
143 |      "execution_count": 4,
144 |      "metadata": {},
145 |      "output_type": "execute_result"
146 |     }
147 |    ],
148 |    "source": [
149 |     "df.head()"
150 |    ]
151 |   },
152 |   {
153 |    "cell_type": "code",
154 |    "execution_count": 5,
155 |    "id": "ec1df159",
156 |    "metadata": {},
157 |    "outputs": [
158 |     {
159 |      "name": "stderr",
160 |      "output_type": "stream",
161 |      "text": [
162 |       "C:\\Users\\GFG19189\\AppData\\Local\\Temp\\ipykernel_5308\\1665899112.py:1: FutureWarning: The default value of numeric_only in DataFrame.skew is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
163 |       "  df.skew()\n"
164 |      ]
165 |     },
166 |     {
167 |      "data": {
168 |       "text/plain": [
169 |        "mpg             0.457066\n",
170 |        "cylinders       0.526922\n",
171 |        "displacement    0.719645\n",
172 |        "horsepower      1.087326\n",
173 |        "weight          0.531063\n",
174 |        "acceleration    0.278777\n",
175 |        "model_year      0.011535\n",
176 |        "dtype: float64"
177 |       ]
178 |      },
179 |      "execution_count": 5,
180 |      "metadata": {},
181 |      "output_type": "execute_result"
182 |     }
183 |    ],
184 |    "source": [
185 |     "df.skew()"
186 |    ]
187 |   },
188 |   {
189 |    "cell_type": "code",
190 |    "execution_count": 6,
191 |    "id": "08d2f9b4",
192 |    "metadata": {},
193 |    "outputs": [],
194 |    "source": [
195 |     "df[\"horsepower\"]= df[\"horsepower\"]**(1/3)"
196 |    ]
197 |   },
198 |   {
199 |    "cell_type": "code",
200 |    "execution_count": 7,
201 |    "id": "7ba7e304",
202 |    "metadata": {},
203 |    "outputs": [
204 |     {
205 |      "data": {
206 |       "text/plain": [
207 |        "0.6132504951168221"
208 |       ]
209 |      },
210 |      "execution_count": 7,
211 |      "metadata": {},
212 |      "output_type": "execute_result"
213 |     }
214 |    ],
215 |    "source": [
216 |     "df[\"horsepower\"].skew()"
217 |    ]
218 |   },
219 |   {
220 |    "cell_type": "code",
221 |    "execution_count": 12,
222 |    "id": "bf4043fe",
223 |    "metadata": {},
224 |    "outputs": [
225 |     {
226 |      "data": {
227 |       "text/plain": [
228 |        "<AxesSubplot: >"
229 |       ]
230 |      },
231 |      "execution_count": 12,
232 |      "metadata": {},
233 |      "output_type": "execute_result"
234 |     },
235 |     {
236 |      "data": {
237 |       "image/png": 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\n",
238 |       "text/plain": [
239 |        "<Figure size 640x480 with 1 Axes>"
240 |       ]
241 |      },
242 |      "metadata": {},
243 |      "output_type": "display_data"
244 |     }
245 |    ],
246 |    "source": [
247 |     "df[\"weight\"].plot(kind = \"bar\")"
248 |    ]
249 |   },
250 |   {
251 |    "cell_type": "code",
252 |    "execution_count": 9,
253 |    "id": "c6029939",
254 |    "metadata": {},
255 |    "outputs": [
256 |     {
257 |      "name": "stderr",
258 |      "output_type": "stream",
259 |      "text": [
260 |       "C:\\Users\\GFG19189\\AppData\\Local\\Temp\\ipykernel_5308\\615572439.py:1: FutureWarning: The default value of numeric_only in DataFrame.kurt is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
261 |       "  df.kurtosis()\n"
262 |      ]
263 |     },
264 |     {
265 |      "data": {
266 |       "text/plain": [
267 |        "mpg            -0.510781\n",
268 |        "cylinders      -1.376662\n",
269 |        "displacement   -0.746597\n",
270 |        "horsepower     -0.211118\n",
271 |        "weight         -0.785529\n",
272 |        "acceleration    0.419497\n",
273 |        "model_year     -1.181232\n",
274 |        "dtype: float64"
275 |       ]
276 |      },
277 |      "execution_count": 9,
278 |      "metadata": {},
279 |      "output_type": "execute_result"
280 |     }
281 |    ],
282 |    "source": [
283 |     "df.kurtosis()"
284 |    ]
285 |   }
286 |  ],
287 |  "metadata": {
288 |   "kernelspec": {
289 |    "display_name": "Python 3 (ipykernel)",
290 |    "language": "python",
291 |    "name": "python3"
292 |   },
293 |   "language_info": {
294 |    "codemirror_mode": {
295 |     "name": "ipython",
296 |     "version": 3
297 |    },
298 |    "file_extension": ".py",
299 |    "mimetype": "text/x-python",
300 |    "name": "python",
301 |    "nbconvert_exporter": "python",
302 |    "pygments_lexer": "ipython3",
303 |    "version": "3.11.1"
304 |   }
305 |  },
306 |  "nbformat": 4,
307 |  "nbformat_minor": 5
308 | }
309 | 


--------------------------------------------------------------------------------
/README.md:
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 1 | # Statistics Course
 2 | 
 3 | ## Overview
 4 | 
 5 | Welcome to the Statistics course! Throughout this course, you will learn essential statistical tools that are widely used in data analysis and decision-making processes.
 6 | 
 7 | ## Course Content
 8 | 
 9 | This course consists of the following modules:
10 | 
11 | 1. **Data Preprocessing:** Explore the steps involved in data preprocessing.
12 | 
13 | 2. **Frequency Distribution:** Learn how to create and analyze frequency distributions to summarize categorical and numerical data.
14 | 
15 | 3. **Measure of Central Tendency:** Understand various measures of central tendency, such as mean, median, and mode, to describe the central value of a dataset.
16 | 
17 | 4. **Measure of Shape:** Explore different measures of shape, like skewness and kurtosis, to understand the distribution of data.
18 | 
19 | 5. **Measure of Dispersion:** Learn about measures of dispersion, such as range, variance, and standard deviation, to assess the spread of data.
20 | 
21 | 6. **Inferential Statistics:** Dive into inferential statistics, covering topics like sampling techniques, the central limit theorem, confidence intervals, hypothesis testing (Z-test and T-test), Chi-Square test, one-way ANOVA, and two-way ANOVA.
22 | 
23 | ## Requirements
24 | 
25 | Before you begin the course, ensure you have the following:
26 | 
27 | - Basic understanding of mathematics and algebra.
28 | - Familiarity with programming fundamentals, preferably using Python.
29 | 
30 | 
31 | ## Support
32 | 
33 | If you have any questions or encounter any challenges while working through the course content or code files, feel free to reach out to us through linkedin[www.linkedin.com/in/sakshi-singhal-bb0b3213a].
34 | 
35 |  
36 | 


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