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Oscar Alexander Tobar
Course: CAP4936-2253-4282
Activity 12, 13 and 14
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getwd()[1] "C:/Users/OAT meal/Documents"How many observations do we have in the training dataset?
NBA = read.csv("NBA_train.csv")
NBAThis much
View(NBA)Is there any chance that a team winning 38 games can make it to the playoffs? Why?
There is, as this may depends on the performance to the competition within each conference being east and west, finishing the regular season with 38 wins total isn’t too far fetched to get 38 wins to get a play in tournament in order to get into the playoffs, the odds are low, but never 0.
# VIDEO 2
# How many wins to make the playoffs?
table(NBA$W, NBA$Playoffs)
0 1
11 2 0
12 2 0
13 2 0
14 2 0
15 10 0
16 2 0
17 11 0
18 5 0
19 10 0
20 10 0
21 12 0
22 11 0
23 11 0
24 18 0
25 11 0
26 17 0
27 10 0
28 18 0
29 12 0
30 19 1
31 15 1
32 12 0
33 17 0
34 16 0
35 13 3
36 17 4
37 15 4
38 8 7
39 10 10
40 9 13
41 11 26
42 8 29
43 2 18
44 2 27
45 3 22
46 1 15
47 0 28
48 1 14
49 0 17
50 0 32
51 0 12
52 0 20
53 0 17
54 0 18
55 0 24
56 0 16
57 0 23
58 0 13
59 0 14
60 0 8
61 0 10
62 0 13
63 0 7
64 0 3
65 0 3
66 0 2
67 0 4
69 0 1
72 0 1# Compute Points Difference
NBA$PTSdiff = NBA$PTS - NBA$oppPTSCan you determine (visually) if there is any relationship between the points difference (PTSdiff) and the number of wins (W)?Explain.
# Check for linear relationship
plot(NBA$PTSdiff, NBA$W)
In this example has that Points difference provides a cushion for a lead in a game in order to win in said game, having a substantial lead can provide a, this alone for a window wide open enough for a win.
What is the number of wins that can guarantee for any team a presence in the playoffs based on historical data? Mostly within a season having 50 and above wins can guarantee you a playoff birth.
# Linear regression model for wins
WinsReg = lm(W ~ PTSdiff, data=NBA)
summary(WinsReg)
Call:
lm(formula = W ~ PTSdiff, data = NBA)
Residuals:
Min 1Q Median 3Q Max
-9.7393 -2.1018 -0.0672 2.0265 10.6026
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.100e+01 1.059e-01 387.0 <2e-16 ***
PTSdiff 3.259e-02 2.793e-04 116.7 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.061 on 833 degrees of freedom
Multiple R-squared: 0.9423, Adjusted R-squared: 0.9423
F-statistic: 1.361e+04 on 1 and 833 DF, p-value: < 2.2e-16We also built a linear model to predict the number of points as a function of some aspects of the game. Is the number of blocks (BLK) significant at a 5% significance level?
# VIDEO 3
# Linear regression model for points scored
PointsReg = lm(PTS ~ X2PA + X3PA + FTA + AST + ORB + DRB + TOV + STL + BLK, data=NBA)
summary(PointsReg)
Call:
lm(formula = PTS ~ X2PA + X3PA + FTA + AST + ORB + DRB + TOV +
STL + BLK, data = NBA)
Residuals:
Min 1Q Median 3Q Max
-527.40 -119.83 7.83 120.67 564.71
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.051e+03 2.035e+02 -10.078 <2e-16 ***
X2PA 1.043e+00 2.957e-02 35.274 <2e-16 ***
X3PA 1.259e+00 3.843e-02 32.747 <2e-16 ***
FTA 1.128e+00 3.373e-02 33.440 <2e-16 ***
AST 8.858e-01 4.396e-02 20.150 <2e-16 ***
ORB -9.554e-01 7.792e-02 -12.261 <2e-16 ***
DRB 3.883e-02 6.157e-02 0.631 0.5285
TOV -2.475e-02 6.118e-02 -0.405 0.6859
STL -1.992e-01 9.181e-02 -2.169 0.0303 *
BLK -5.576e-02 8.782e-02 -0.635 0.5256
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 185.5 on 825 degrees of freedom
Multiple R-squared: 0.8992, Adjusted R-squared: 0.8981
F-statistic: 817.3 on 9 and 825 DF, p-value: < 2.2e-16# Sum of Squared Errors
PointsReg$residuals 1 2 3 4 5 6 7
38.5722713 142.8720040 -92.8957180 -8.3913473 -258.4705615 171.4608325 150.4081623
8 9 10 11 12 13 14
169.3811429 40.7756197 -75.3256614 444.9088743 94.3864704 -205.6809050 113.5969040
15 16 17 18 19 20 21
64.1993998 -76.5711999 249.4888007 28.0363236 329.4487991 96.3248342 349.2067913
22 23 24 25 26 27 28
-284.3765225 196.1611379 198.2493104 445.4100295 93.8946072 -316.2962802 -166.1909668
29 30 31 32 33 34 35
-5.8446359 211.2301997 155.7426615 -23.9248929 -77.9070033 218.9449693 164.1368602
36 37 38 39 40 41 42
-177.6479438 66.9205988 162.7892553 23.5961895 93.9839603 185.7015113 -50.2507837
43 44 45 46 47 48 49
-90.1181969 139.6866673 -231.1772776 111.2200135 185.9069491 210.6753018 -47.9420913
50 51 52 53 54 55 56
-257.8213675 225.7399197 70.4925628 432.6468031 187.4169561 -34.3947653 112.9305359
57 58 59 60 61 62 63
334.4717296 222.4169937 17.6755711 165.4512882 207.9970351 56.8277093 214.6051983
64 65 66 67 68 69 70
-23.0235142 341.7509536 -48.3807695 304.9203623 -36.7878762 -31.0357805 61.8847883
71 72 73 74 75 76 77
-153.0322403 121.7423324 -61.1581185 -47.9906548 -120.3599484 245.7621368 -264.3876116
78 79 80 81 82 83 84
161.1110819 87.3192423 426.2098591 -4.7790973 126.8613801 -97.5009340 329.9773912
85 86 87 88 89 90 91
-16.2338716 7.8513505 191.9280982 87.0090318 -142.5397602 -216.2264974 -199.6293933
92 93 94 95 96 97 98
71.0810742 257.3751407 -227.1203824 -61.4866232 71.3329444 -233.2637272 -34.7860771
99 100 101 102 103 104 105
84.9503466 108.6553543 -84.8168235 -90.0423121 341.2144522 52.8507112 47.8978397
106 107 108 109 110 111 112
181.0574099 160.7203318 237.0174702 314.9609845 51.9650831 300.2035074 -148.0931149
113 114 115 116 117 118 119
-13.3592416 -161.6184704 82.1172789 277.6080699 233.4334153 -225.7299932 69.0259972
120 121 122 123 124 125 126
37.3407430 18.2709681 121.8125335 217.9464858 -74.8210467 36.2611001 356.2366230
127 128 129 130 131 132 133
439.4127892 111.0266627 72.1377278 -6.1141295 331.6249450 -158.3642350 94.9048994
134 135 136 137 138 139 140
151.3242943 -284.7768411 -184.0287416 -103.9972773 54.1758237 139.3176593 125.3796164
141 142 143 144 145 146 147
-71.4407602 83.4742245 -131.6383234 -33.5752771 98.9460909 -59.8760139 -116.6711077
148 149 150 151 152 153 154
-110.4055752 290.8888709 38.5758792 -6.8265554 -284.8106013 149.5419209 -185.9270381
155 156 157 158 159 160 161
-13.5712897 -90.2301662 21.0080300 14.5295957 -346.4091267 -54.7198161 87.6823846
162 163 164 165 166 167 168
203.7903006 -30.7131853 -153.9699795 194.6791232 -357.4466727 133.8696823 -21.6271760
169 170 171 172 173 174 175
-220.4987354 -153.7269937 -383.7168614 212.2104185 -100.3118791 -30.5085767 -57.7910608
176 177 178 179 180 181 182
205.9463003 -124.1358862 -61.2169391 -93.9538879 -135.6180284 69.1245169 -435.5355494
183 184 185 186 187 188 189
-47.8153585 115.1051439 222.5411686 104.6516380 7.8335700 178.0759383 -185.3383423
190 191 192 193 194 195 196
122.0537263 -29.4729351 27.1344203 189.2078833 -429.5919872 57.2397301 -170.2701567
197 198 199 200 201 202 203
-14.0836520 21.0147294 49.6548689 -127.4633821 -87.4084020 -77.6940715 -155.2913076
204 205 206 207 208 209 210
8.4930328 -232.7210528 35.3384277 151.1394532 119.4563308 -416.3088878 134.8599211
211 212 213 214 215 216 217
33.3825347 48.4541197 -269.8021487 214.9045443 88.1318416 -24.0318730 188.2281015
218 219 220 221 222 223 224
-249.1537666 157.9872056 -146.6803006 72.9077663 31.1747176 337.2185582 69.7227713
225 226 227 228 229 230 231
-2.7440511 -55.2845827 -84.6255409 -151.4858821 234.7432200 -165.3909069 -172.9288404
232 233 234 235 236 237 238
386.6402387 34.4884530 -368.0387956 304.8349400 -173.0591889 168.9365987 -327.6509605
239 240 241 242 243 244 245
95.0370278 -75.5698743 -74.9702316 290.0371682 -21.8628806 72.5362398 -144.3565453
246 247 248 249 250 251 252
-44.7765529 -155.4752429 -114.0232742 82.8841506 -306.5759686 256.9630856 75.4312937
253 254 255 256 257 258 259
-108.9852622 -160.6985087 -1.0708625 389.4834173 48.4039145 -173.2376267 102.4859575
260 261 262 263 264 265 266
564.7127452 -135.6781765 435.5847710 -238.8763852 93.4120332 -346.4790813 84.2266238
267 268 269 270 271 272 273
124.2627684 157.9013909 90.9742388 -319.7738668 111.6330940 -136.0189613 179.6895020
274 275 276 277 278 279 280
-139.8481361 -60.2214721 21.1448936 -102.4930752 87.4261255 -2.2833983 -33.1839059
281 282 283 284 285 286 287
-313.4181662 -9.7903234 365.0041757 -170.9089658 -203.2682115 -59.0783300 344.4592952
288 289 290 291 292 293 294
-177.2934555 278.4424923 31.1539516 -19.4217087 146.9309508 49.6437593 323.4485389
295 296 297 298 299 300 301
47.1034178 3.9718411 -111.0589062 -40.0036081 187.1994351 134.5701059 -130.3795390
302 303 304 305 306 307 308
227.3624370 16.4481298 -91.2556101 215.9887998 70.7747666 50.5357552 -86.7616664
309 310 311 312 313 314 315
66.3006293 348.5847817 69.7928527 -144.9174008 48.2485248 262.5189212 -11.0182067
316 317 318 319 320 321 322
276.2567984 40.2609782 -235.0009787 91.8230888 -36.7029055 66.1862316 127.1446887
323 324 325 326 327 328 329
34.6306466 -89.1508242 -38.0350890 74.6959695 -24.6713632 -139.6322463 120.5781319
330 331 332 333 334 335 336
-256.3194253 35.3325803 -238.1863124 204.2701943 -231.4333870 -242.0178081 27.3589769
337 338 339 340 341 342 343
442.7697537 -90.3428846 -252.6536092 31.2460678 -24.0030042 -113.6697991 74.2030422
344 345 346 347 348 349 350
-63.3601223 13.1314540 -58.4065092 16.5093336 -26.4233092 -49.9197611 102.5295504
351 352 353 354 355 356 357
-276.0762358 -171.2605451 235.4118705 -295.3696087 -259.1915277 -209.8493128 -60.3803252
358 359 360 361 362 363 364
40.8738668 -162.3559100 -3.1584146 -252.6683460 -359.6072976 219.8480950 107.9177034
365 366 367 368 369 370 371
-228.4285961 77.5838841 77.6092501 176.9728823 21.0277939 225.7947949 90.6177409
372 373 374 375 376 377 378
-95.0387148 243.8004275 63.7765295 -135.7112041 127.9942080 208.5134149 -226.2507886
379 380 381 382 383 384 385
-27.4427262 215.5791874 70.0554598 -220.3324085 -252.5213694 -117.0224660 36.9146043
386 387 388 389 390 391 392
188.5932206 -12.6241171 24.1401960 39.4113815 130.8261623 194.8028770 140.1603242
393 394 395 396 397 398 399
100.4917058 367.8120506 -77.1138759 190.1907177 430.4505906 243.1092461 -220.7690501
400 401 402 403 404 405 406
-135.3500281 182.9169784 58.1314347 -10.3705665 134.0505987 333.4363828 110.9704334
407 408 409 410 411 412 413
37.1431301 188.8559358 -88.4445131 -165.3268990 148.8624801 -4.7914163 -114.6045335
414 415 416 417 418 419 420
-90.1562962 -65.1353805 9.9207366 -20.2393315 147.7163583 153.4474395 95.5889698
421 422 423 424 425 426 427
-329.6439893 323.3019593 345.3838501 -148.5288812 166.9648145 277.3541861 162.6383840
428 429 430 431 432 433 434
-78.9033000 -176.7932426 365.3962572 132.7242544 85.6582953 -19.3417988 95.4767236
435 436 437 438 439 440 441
-102.8199452 111.8183778 299.2808339 -124.0889739 -37.3805041 118.5055640 38.2173450
442 443 444 445 446 447 448
-122.8141423 -84.3447659 154.5643586 42.6355711 54.7178397 102.9846564 32.6861086
449 450 451 452 453 454 455
112.7943954 -163.3563028 150.7521084 217.5877806 -96.7133626 13.7243484 -33.1690450
456 457 458 459 460 461 462
-112.2550008 -15.7083565 -224.4198990 18.2593593 -393.0403979 49.2945267 52.0947949
463 464 465 466 467 468 469
43.2496203 -149.1223107 75.6856970 170.8878792 -257.6364448 51.6854016 11.8121415
470 471 472 473 474 475 476
-176.9048352 -149.5317630 -64.1990241 -71.3105611 -317.9190063 -65.8451642 97.8497015
477 478 479 480 481 482 483
-103.1692986 3.0848318 -104.6823532 -234.7534874 50.5295490 -75.4835788 -526.1468848
484 485 486 487 488 489 490
-393.9784124 -360.8366411 116.7193515 -321.3756304 -28.1090479 -508.3250405 -39.9958738
491 492 493 494 495 496 497
67.9854387 -97.4641720 -268.8364479 -26.0249946 188.1881640 -127.9366821 -86.3440758
498 499 500 501 502 503 504
133.8144538 29.4480488 -292.9821609 -124.9408024 101.3655240 -186.5181083 -63.5389375
505 506 507 508 509 510 511
-212.2015589 -323.1476886 -125.6610320 56.9083106 -39.0559074 -1.9339391 -319.9727619
512 513 514 515 516 517 518
-433.1243358 -431.1346590 -95.8909016 120.6089792 -409.7409083 -352.9341830 -527.3988939
519 520 521 522 523 524 525
110.6694955 -193.5043557 -92.6385367 -143.5858243 -189.7838251 172.1977457 -80.8020663
526 527 528 529 530 531 532
-342.9141699 124.8700974 -226.9524006 -73.5173798 -388.4868649 82.9536394 -96.7444961
533 534 535 536 537 538 539
-114.0835553 60.0566113 -332.3804023 -175.5276633 -338.7116370 -148.1422366 -45.2258816
540 541 542 543 544 545 546
-270.5159099 -159.8389177 -420.4637398 -133.0466450 183.8988039 -267.0297916 -5.2562902
547 548 549 550 551 552 553
-228.0471046 -11.6818058 -255.6786897 -7.7244412 -115.5357863 -298.4118693 -122.2961876
554 555 556 557 558 559 560
90.2924072 111.3930340 -245.4519945 -164.6445508 -29.3651223 -41.9781581 33.4260937
561 562 563 564 565 566 567
15.1663563 -29.4557965 44.0659204 247.9836928 -57.4318280 -238.6989443 -8.7249850
568 569 570 571 572 573 574
30.9454288 -343.6175905 -207.4418486 -306.4223254 157.4538406 -502.4785715 -126.1415717
575 576 577 578 579 580 581
48.8616098 143.9835801 -344.7694076 -116.5012114 -142.7898454 -127.9612584 -226.7659179
582 583 584 585 586 587 588
67.1679765 -94.0443422 -326.2414346 -84.6517620 4.5942017 -89.9757406 -97.0958454
589 590 591 592 593 594 595
-34.6927947 40.9701699 -88.3066869 126.5679875 -128.7529512 -166.6757304 -208.2444446
596 597 598 599 600 601 602
-105.4053449 -69.9961388 -104.0297252 -475.1678378 -290.6421238 195.4801727 -116.0865727
603 604 605 606 607 608 609
-136.0505114 -118.3811054 125.8235124 -145.2484421 -144.5655628 -435.6270621 -230.6201428
610 611 612 613 614 615 616
-112.7403208 -243.8883351 13.9124625 -392.1393056 -233.5727670 88.6125994 -203.7574893
617 618 619 620 621 622 623
-207.3393547 36.7326516 71.7237279 -110.6124268 -151.5524839 95.2365977 -227.3589026
624 625 626 627 628 629 630
-98.5962165 -210.8715081 -53.6787512 33.2644764 -380.2334407 -217.0512157 -135.7283167
631 632 633 634 635 636 637
208.5947156 -198.2473902 -147.6362401 -282.5390059 -55.4726214 3.0618526 -118.7764165
638 639 640 641 642 643 644
-15.9756605 1.5396468 2.2068206 -78.5559489 20.5194552 -376.9064555 -367.5790965
645 646 647 648 649 650 651
78.4730898 88.0528050 -178.9859105 283.6342652 18.0639226 1.4275017 -22.1910648
652 653 654 655 656 657 658
334.1581029 -44.6704981 -166.2133428 -112.8182784 175.7515262 60.9355144 -331.2815975
659 660 661 662 663 664 665
-175.1322112 34.9727118 430.8913232 -260.7815266 -99.5985786 -306.5331420 -144.2463445
666 667 668 669 670 671 672
-71.9561309 40.4095734 -9.9170555 9.7141807 72.8730721 -61.2840291 -51.9936086
673 674 675 676 677 678 679
-452.8596863 -81.9437393 69.2906290 254.7395766 -22.9459505 215.8931262 -16.9537293
680 681 682 683 684 685 686
-107.9068394 202.3017464 287.5765859 180.7757394 -305.5932029 56.2240459 4.5320328
687 688 689 690 691 692 693
-44.0648823 -278.0391307 -13.3280981 -112.7276708 422.1750569 -131.0023955 51.4971549
694 695 696 697 698 699 700
-86.9745423 28.8396258 -107.9302127 -55.3683153 -16.7225380 60.3453436 3.3520616
701 702 703 704 705 706 707
140.9429255 -17.9219329 -296.8381962 136.2394242 106.7244264 168.2861008 26.7860625
708 709 710 711 712 713 714
339.8954937 187.8922770 -202.6392008 148.7995083 268.8921528 0.6597544 -119.2916116
715 716 717 718 719 720 721
-23.0549542 -28.1758366 206.7679556 -138.5838793 -210.7824121 -29.6626073 210.3268820
722 723 724 725 726 727 728
-212.8798945 88.1962039 129.1032851 11.9530477 -166.3796048 -372.3297260 67.5130804
729 730 731 732 733 734 735
1.7122210 -179.0745146 -28.4404659 151.2765881 -425.3360446 344.3671825 -47.2592021
736 737 738 739 740 741 742
136.9801455 63.4427397 203.2044716 27.7908779 251.4279736 84.5817590 -155.6577645
743 744 745 746 747 748 749
150.3787715 138.7921016 198.4699948 101.8590582 345.8144412 35.1336113 169.1641149
750 751 752 753 754 755 756
354.9998851 251.7571721 47.8412497 77.9677328 66.2799291 216.7990909 155.1577399
757 758 759 760 761 762 763
-131.2437994 230.2449071 218.7156645 116.0349148 -78.5937100 -23.1321308 99.7713990
764 765 766 767 768 769 770
280.2227149 40.8527845 19.4188914 72.9388151 120.7266716 439.1035137 456.0100354
771 772 773 774 775 776 777
47.3239201 186.1096824 31.7505381 -54.0912550 73.0035369 234.4761589 27.9146721
778 779 780 781 782 783 784
-21.6493313 -75.0167664 148.4251726 106.3308316 76.0196340 37.3592068 56.5562663
785 786 787 788 789 790 791
-41.8917486 -200.7598142 -55.5159544 109.1518868 321.3239680 219.8866600 -73.6034103
792 793 794 795 796 797 798
3.1961900 -171.1408177 190.8979178 101.1845265 253.1734885 263.7840087 199.5924560
799 800 801 802 803 804 805
463.8379676 219.1540922 52.3032317 140.7498122 195.8267787 -55.3103142 153.8564182
806 807 808 809 810 811 812
61.1275837 92.8158603 -108.8302808 73.3423661 -360.6001538 134.1518035 73.3435884
813 814 815 816 817 818 819
141.0017271 272.8259956 -33.1611977 19.7818711 -149.9998706 190.0065593 261.3992751
820 821 822 823 824 825 826
308.7602526 -135.4172110 108.2677094 -171.3410196 102.4439076 156.0829202 210.0521687
827 828 829 830 831 832 833
109.4908936 -20.5354175 59.2845716 175.9235274 30.6531825 262.6728011 70.0671862
834 835
-17.5789419 -8.3393046 SSE = sum(PointsReg$residuals^2)
SSE[1] 28394314What is the meaning of the RMSE(Root mean squared error) in the PointsReg model? Are you satisfied with this value?
# Root mean squared error
RMSE = sqrt(SSE/nrow(NBA))
RMSE[1] 184.4049# Average number of points in a season
mean(NBA$PTS)[1] 8370.24# Remove insignifcant variables
summary(PointsReg)
Call:
lm(formula = PTS ~ X2PA + X3PA + FTA + AST + ORB + DRB + TOV +
STL + BLK, data = NBA)
Residuals:
Min 1Q Median 3Q Max
-527.40 -119.83 7.83 120.67 564.71
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.051e+03 2.035e+02 -10.078 <2e-16 ***
X2PA 1.043e+00 2.957e-02 35.274 <2e-16 ***
X3PA 1.259e+00 3.843e-02 32.747 <2e-16 ***
FTA 1.128e+00 3.373e-02 33.440 <2e-16 ***
AST 8.858e-01 4.396e-02 20.150 <2e-16 ***
ORB -9.554e-01 7.792e-02 -12.261 <2e-16 ***
DRB 3.883e-02 6.157e-02 0.631 0.5285
TOV -2.475e-02 6.118e-02 -0.405 0.6859
STL -1.992e-01 9.181e-02 -2.169 0.0303 *
BLK -5.576e-02 8.782e-02 -0.635 0.5256
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 185.5 on 825 degrees of freedom
Multiple R-squared: 0.8992, Adjusted R-squared: 0.8981
F-statistic: 817.3 on 9 and 825 DF, p-value: < 2.2e-16PointsReg2 = lm(PTS ~ X2PA + X3PA + FTA + AST + ORB + DRB + STL + BLK, data=NBA)
summary(PointsReg2)
Call:
lm(formula = PTS ~ X2PA + X3PA + FTA + AST + ORB + DRB + STL +
BLK, data = NBA)
Residuals:
Min 1Q Median 3Q Max
-526.79 -121.09 6.37 120.74 565.94
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.077e+03 1.931e+02 -10.755 <2e-16 ***
X2PA 1.044e+00 2.951e-02 35.366 <2e-16 ***
X3PA 1.263e+00 3.703e-02 34.099 <2e-16 ***
FTA 1.125e+00 3.308e-02 34.023 <2e-16 ***
AST 8.861e-01 4.393e-02 20.173 <2e-16 ***
ORB -9.581e-01 7.758e-02 -12.350 <2e-16 ***
DRB 3.892e-02 6.154e-02 0.632 0.5273
STL -2.068e-01 8.984e-02 -2.301 0.0216 *
BLK -5.863e-02 8.749e-02 -0.670 0.5029
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 185.4 on 826 degrees of freedom
Multiple R-squared: 0.8991, Adjusted R-squared: 0.8982
F-statistic: 920.4 on 8 and 826 DF, p-value: < 2.2e-16PointsReg3 = lm(PTS ~ X2PA + X3PA + FTA + AST + ORB + STL + BLK, data=NBA)
summary(PointsReg3)
Call:
lm(formula = PTS ~ X2PA + X3PA + FTA + AST + ORB + STL + BLK,
data = NBA)
Residuals:
Min 1Q Median 3Q Max
-523.79 -121.64 6.07 120.81 573.64
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.015e+03 1.670e+02 -12.068 < 2e-16 ***
X2PA 1.048e+00 2.852e-02 36.753 < 2e-16 ***
X3PA 1.271e+00 3.475e-02 36.568 < 2e-16 ***
FTA 1.128e+00 3.270e-02 34.506 < 2e-16 ***
AST 8.909e-01 4.326e-02 20.597 < 2e-16 ***
ORB -9.702e-01 7.519e-02 -12.903 < 2e-16 ***
STL -2.276e-01 8.356e-02 -2.724 0.00659 **
BLK -3.882e-02 8.165e-02 -0.475 0.63462
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 185.4 on 827 degrees of freedom
Multiple R-squared: 0.8991, Adjusted R-squared: 0.8982
F-statistic: 1053 on 7 and 827 DF, p-value: < 2.2e-16PointsReg4 = lm(PTS ~ X2PA + X3PA + FTA + AST + ORB + STL, data=NBA)
summary(PointsReg4)
Call:
lm(formula = PTS ~ X2PA + X3PA + FTA + AST + ORB + STL, data = NBA)
Residuals:
Min 1Q Median 3Q Max
-523.33 -122.02 6.93 120.68 568.26
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.033e+03 1.629e+02 -12.475 < 2e-16 ***
X2PA 1.050e+00 2.829e-02 37.117 < 2e-16 ***
X3PA 1.273e+00 3.441e-02 37.001 < 2e-16 ***
FTA 1.127e+00 3.260e-02 34.581 < 2e-16 ***
AST 8.884e-01 4.292e-02 20.701 < 2e-16 ***
ORB -9.743e-01 7.465e-02 -13.051 < 2e-16 ***
STL -2.268e-01 8.350e-02 -2.717 0.00673 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 185.3 on 828 degrees of freedom
Multiple R-squared: 0.8991, Adjusted R-squared: 0.8983
F-statistic: 1229 on 6 and 828 DF, p-value: < 2.2e-16What has been the maximum number of points in a season?
# Compute SSE and RMSE for new model
SSE_4 = sum(PointsReg4$residuals^2)
RMSE_4 = sqrt(SSE_4/nrow(NBA))
SSE_4[1] 28421465RMSE_4[1] 184.493# VIDEO 4
# Read in test set
NBA_test = read.csv("NBA_test.csv")# Make predictions on test set
PointsPredictions = predict(PointsReg4, newdata=NBA_test)How well did your predictions work on the testing dataset? Report the new R2 and RMSE.
# Compute out-of-sample R^2
SSE = sum((PointsPredictions - NBA_test$PTS)^2)
SST = sum((mean(NBA$PTS) - NBA_test$PTS)^2)
R2 = 1 - SSE/SST
R2[1] 0.8127142# Compute the RMSE
RMSE = sqrt(SSE/nrow(NBA_test))
RMSE [1] 196.3723Activity 13
#WinReg = lm(W ~ PTSsdiff, data=NBA)
#49+41=0.0326
x_1 = (49-41)/0.0326
x_1[1] 245.3988mean(Three_pts_pct)[1] 0.4896465Activity 14
Threepts_made <-c(4, 5, 3, 6, 7)
Threepts_attempt <-c(9, 10, 8, 11, 12)Three_pts_pct = Threepts_made/Threepts_attempt
Three_pts_pct[1] 0.4444444 0.5000000 0.3750000 0.5454545 0.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