It is not graded question please do it Consider a multiple regression model for predicting the total number of runs scored by a MajorLeagueBaseball (MLB) team during a season. Using data on number of walks (x1), singles (x2), doubles (x3),triples (x4), and home runs (x5) for each of the 30 teams during the 2017 MLB season, a first-order model fortotal number of runs scored (y) was fit.Conduct the global F-test of model usefulness at the a = 0.01 level of significance.The selected results are shown in the SAS printout below.Analysis of VarianceSum ofMeanSourceDFSquaresSquareF ValuePr>FModelError477.21362Corrected Total123264Conduct the global F-test of model usefulness at the a = 0.01 level of significance.1.int] Hypotheses• Null hypothesis Hois stated as- Alternative hypothesis His stated asRound to TWO decimal places.2. [] The F value equals to3. [Degrees of freedom 3.] Degrees of freedom• Numerator DF =• Denominator DF =Reject region isWe(reject or fail to reject) the null hypothesis at a = 0.01 level of significance.(useful or not useful) at at a = 0.01 level ofThe multiple regression model is evident to besignificance.

Name: It is not graded question please do it Consider a multiple regression
Uploaded: 2024-03-20T07:07:37.000Z
Channel: Bartleby
Description: It is not graded question please do it Consider a multiple regression model for predicting the total number of runs scored by a MajorLeagueBaseball (MLB) team during a season. Using data on number of walks (x1), singles (x2), doubles (x3),triples (x4