# A multiple regression model for predicting total number of runs scored by a Major League Baseball…

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**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 period (a sample of n-234), the results in the following table were obtained.

Independent Variable |
Beta estimate |
Standard Error |
t |

Constant | 3.70 | 15.00 | 0.25 |

Walks (x1) | 0.34 | 0.02 | ???? |

Singles (x2) | 0.49 | 0.03 | 16.33 |

Doubles (x3) | 0.72 | 0.05 | 14.40 |

Triples (x4) | ???? | 0.19 | 6.00 |

Home runs (x5) | 1.51 | 0.05 | 30.20 |

Stolen bases (x6) | 0.26 | 0.05 | 5.20 |

Caught stealing (x7) | -0.14 | 0.08 | -1.75 |

Strikeouts (x8) | -0.10 | 0.01 | -10.00 |

Outs (x9) | 0.10 | 0.01 | 10.00 |

**Answer the following rounding off your answers to 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 = IS and 2.00 = ISN‘T):

________,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 predicator at alpha

**__**

________**Click the button below to view answer!
**

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