Question: Find the best multiple regression model for predicting the number of wins having only significant independent variables. How good is your model? Does it use
Find the best multiple regression model for predicting the number of wins having only significant independent variables. How good is your model? Does it use the same variables you thought were appropriate in part b? Use a level of significance of 0.05.Determine the best multiple regression model. Let
X1
represent Runs, let
X2
represent Hits, let
X3
represent Earned Run Average, let
X4
represent Strike Outs, and let
X5
represent Walks. Enter the terms of the equation so that the
Xk-values
are in ascending numeral order by base. Select the correct choice below and fill in the answer boxes within your choice.(Type integers or decimals rounded to three decimal places as needed.)
A.
Won=enter your response here+enter your response hereXenter your response here+enter your response hereXenter your response here+enter your response hereXenter your response here
B.
Won=enter your response here+enter your response hereXenter your response here
C.
Won=63.57663.576+0.0410.041X44+0.1230.123X55
D.
Won=enter your response here+enter your response hereXenter your response here+enter your response hereXenter your response here+enter your response hereXenter your response here+enter your response hereXenter your response here
E.
Won=enter your response here+enter your response hereXenter your response here+enter your response hereXenter your response here+enter your response hereXenter your response here+enter your response hereXenter your response here+enter your response here
Data Table:
| Won | Runs | Hits | Earned Run Average | Strike Outs | Walks |
| 65 | 713 | 1366 | 4.81 | 1519 | 589 |
| 91 | 738 | 1411 | 3.56 | 1140 | 634 |
| 66 | 613 | 1440 | 4.59 | 1056 | 424 |
| 89 | 819 | 1511 | 4.19 | 1140 | 587 |
| 75 | 685 | 1414 | 4.18 | 1226 | 479 |
| 88 | 752 | 1467 | 4.09 | 922 | 467 |
| 91 | 791 | 1515 | 4.01 | 1218 | 522 |
| 69 | 646 | 1362 | 4.3 | 1184 | 545 |
| 83 | 770 | 1452 | 4.14 | 1274 | 585 |
| 81 | 751 | 1515 | 4.3 | 1147 | 546 |
| 80 | 719 | 1403 | 4.08 | 1375 | 514 |
| 76 | 611 | 1348 | 4.09 | 1025 | 415 |
| 67 | 676 | 1534 | 4.97 | 905 | 471 |
| 80 | 681 | 1363 | 4.04 | 1070 | 466 |
| 80 | 667 | 1368 | 4.01 | 1184 | 533 |
| 77 | 750 | 1471 | 4.58 | 1226 | 546 |
| 94 | 782 | 1521 | 3.95 | 967 | 559 |
| 79 | 656 | 1361 | 3.7 | 1095 | 502 |
| 95 | 858 | 1485 | 4.06 | 1136 | 662 |
| 81 | 663 | 1396 | 3.56 | 1061 | 527 |
| 97 | 772 | 1451 | 3.67 | 1064 | 560 |
| 57 | 587 | 1303 | 5 | 1207 | 463 |
| 90 | 665 | 1338 | 3.39 | 1183 | 538 |
| 92 | 697 | 1411 | 3.36 | 1099 | 487 |
| 61 | 513 | 1274 | 3.93 | 1184 | 459 |
| 86 | 736 | 1456 | 3.57 | 1027 | 541 |
| 96 | 800 | 1343 | 3.78 | 1282 | 672 |
| 90 | 787 | 1556 | 3.93 | 986 | 511 |
| 85 | 755 | 1364 | 4.22 | 1164 | 471 |
| 69 | 655 | 1355 | 4.13 | 1230 | 503 |
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