Consider a multiple regression model for predicting the total number of runs scored by a Major LeagueBaseball (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 for total number of runs scored (y) was fit. The selected results are shown in the SAS printout below. Parameter Estimates Parameter Standard Variable DF Estimate t Value Pr > |t| Error Intercept 1 -580.76485 96.97966 -5.99 <.0001 WALKS 1 0.44168 0.07910 5.58 <.0001 SINGLES 1 0.65536 0.08143 8.05 <.0001 DOUBLES 1 0.70413 0.16510 4.26 0.0003 TRIPLES 1 0.79719 0.50228 1.59 0.1256 HOMERUNS 1 1.44574 0.17178 8.42 <.0001 Find B, and interpret it to the context. Estimate is Interpretation: 2. [1 Construct a 95% confidence interval for ß4.

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