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The accompanying Major League Baseball data provide data for one season Use the data to build a multiple regression model that predicts the number of wins Complete parts through Click the icon to view the Major League Baseball data . Construct and camine the contation matris multicolinearity a potential problem? Wor Earned Run Strike Runs Hits Average Outs Works Won Runs His Earned Run Average Strike Out Walks Type integers or decimals founded to three decir al places as needed Is multicollinearity a potential problem? Yes, because at least one pair of independent variables have correlation coefficients greater than 0.7 cind the best mulle regression model de pedicting the number of being an independent variables How good as your mou Does to the lies you how to prepare a person vel of sigilance of 05 Determine the best multiple regression model tot, runs tres traduntX, SOX Whem of the that the values are incending rumonder by hase Select the come dice and is the box win your Type wagens ordecimal rounded to the decimal places ded) A wong 1. Won 1+Xg(X. *EXyX(IDX OC - OD X3 (EXg. DX(X OD WOD The models R vahel This means that of the number of wine can be predicted by these variaties. This model (Round the value to three decimal places as needed Round the percentage to one decimal place as needed) use the same variables as part X 1* > 1 G 821 |8| F E D C A B Won Runs Hits Earned Run Averag Strike Outs Walks 1528 589 65 4.81 713 1366 1140 634 91 3.56 738 1411 424 66 613 1056 4.59 1440 1511 1140 4.19 89 587 1235 479 4.18 685 1414 3 75 467 27 922 88 752 4.09 1467 B 91 794 1515 4.01 522 1218 9 69 646 1362 4.3 1184 545 10 83 770 1452 585 4.14 1274 71 81 751 1515 4.3 1147 546 12 80 719 1403 4.08 1375 514 13 76 611 1348 4.09 1025 415 14 67 676 1534 4.97 905 471 15 80 681 1363 4.04 1070 466 16 667 1368 4.01 1184 533 17 77 750 1471 4.58 1215 546 18 94 782 1521 3.95 967 559 19 79 656 1361 3.7 1095 502 20 95 853 1485 4.06 1136 662 21 81 663 1396 3.56 1061 527 22 97 772 1451 3.67 1064 560 23 57 587 1303 5 1207 463 24 665 1338 3.39 1183 538 25 92 697 1411 3.36 1099 487 26 61 513 1274 3.93 1184 459 27 86 736 1456 3.57 1027 28 96 800 1343 3.78 1294 672 29 90 1556 3.93 986 511 30 85 755 1364 4.22 11641 471 31 69 655 1355 4.131 1222 503 32 33 541 787