(Solved): Consider a multiple-regression model for predicting the total number of runs scored by a baseball t ...

Consider a multiple-regression model for predicting the total number of runs scored by a baseball team during a season. Using data on number of walks \( \left(x_{1}\right) \), singles \( \left(x_{2}\right) \), doubles \( \left(x_{3}\right) \), triples \( \left(x_{4}\right) \), home runs \( \left(x_{5}\right) \), stolen bases \( \left(x_{6}\right) \), times caught stealing \( \left(x_{7}\right) \), strike outs \( \left(x_{8}\right) \), and ground outs \( \left(x_{9}\right) \) for each of the 30 teams in the league, a 1st-order model for total number of runs scored (y) was fit. The results are shown in the accompanying technology printout. Complete parts a through e below. Click the icon to view the technology printout. a. Write the least squares prediction equation for \( y= \) total number of runs scored by a team in a season. \[ \hat{y}=+1\left|x_{1}+1\right| x_{2}+1\left|x_{3}+1\right| x_{4}+1 \quad\left|x_{5}+\right| \quad x_{6}+1\left|x_{7}+\right| \quad\left|x_{8}+\right| \mid x_{9} \] Technology Printout The regression equation is \[ \begin{aligned} \text { Runs }=& 47+0.236 \text { Walks }+0.362 \text { Singles }+0.843 \text { Doubles }+1.68 \text { Triples } \\ &+1.10 \text { HomeRuns }+0.057 \text { StolenBases }-0.055 \text { CaughtStealing } \\ &-0.0872 \text { Strikeouts }-0.119 \text { GroundOuts } \end{aligned} \] Analysis of Variance