A multiple regression model for predicting total number of runs scored by a Majo

Question

A multiple regression model for predicting total number of runs scored by a Major League Baseball team during a season. Using data on all teams over a 9-year perlod a sample of n-234), the results in the following table were obtained. Independent Varlable Beta estimate Standard Ermor t Constant 3.70 15.00 0.25 Walks (x1) 0.34 0.02 Singles (x2) Doubles (3) Triples (x4) 0.49 0.03 16.33 0.72 0.05 14.40 0,19 6.00 Home runs (x5) Stolen bases (x6) Caught stealing(7)-0.14 Strikeouts (x8) Outs (x9) 1.51 0.05 30.20 0.26 0.05 5.20 0.08 1.75 0.10 0.01 10.00 0.10 0.01 10.00 Answer the following rounding off your answers ta two decimal digits The T-test for significance of x1 is equal to Beta4 is equal to The rejection region to test the significance of individual predictors at alpha 0.05 is equal to therefore (use 1.00-15 and 2.00 = ISNT): x2 a good predictor at alpha 0.05 Moreover x3 a good predictor at alpha 0.001 x8 a good predictor at alpha 0.005 x7 is a good predictor at alpha

Explanation / Answer

We are allowed to do 4 subparts question at a time. Post again for more subparts of question.

1) t test equal to = 0.34/0.02 = 17

2) B4 = 6 * 0.19 = 1.14

3) rejection region, p < 0.00001. is significant

4) x2 is a good predictor

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