A sports analyst for Major League Baseball wonders whether there is a relationship between a pitcher's salary (in S millions) and his earned run average (ERA). The accompanying table lists a portion of the data that she collected for 10 pitchers: a-1. Estimate the model: salary =0+1kA+. (Negotive volues should be indicated by o minus sign. Enter your answers, in millions, rounded to 2 decimal ploces.) 0-2. Interpret the coefficient of ERA. A one-unit increase in ERA, predicted salary decreases by $226 million A one-unit increase in ERA, predicted salary increases by $226 milion. A one-unit increase in ERA, predicted salary decreases by $975 milion. A one-unit increase in ERA, predicted salary increases by $9.75 million. b. Use the estimated model to predict salary for Player 1 and Player 2 For example, use the sample regression equation to predict the salary for Pitcher 1 with ERA =2.28 (Do not round intermediote colculations. Round your final onswers (in millions) to 2 decimal ploces.) b. Use the estimated model to predict salary for Player 1 and Player 2 . For example, use the sample regression equation to predict the salary for Pitcher 1 with ERA =228. (Do not round intermediote calculations. Round your final answers (in millions) to 2 decimal places:) c. Derive the corresponding residuats for Player 1 and Player 2 (Negative values should be indicoted by a minus sign. Round your final answers (in millions) to 2 decimal places.) \begin{tabular}{|l|l|l|l|} \hline & \multicolumn{1}{c|}{ A } & \multicolumn{1}{c|}{ B } & \\ \hline 1 & Pitcher & Salary & ERA \\ \hline 2 & 1 & 14 & 2.28 \\ \hline 3 & 2 & 2 & 2.43 \\ \hline 4 & 3 & 0.4 & 2.58 \\ \hline 5 & 4 & 7 & 2.63 \\ \hline 6 & 5 & 9 & 2.57 \\ 7 & 6 & 6.2 & 2.26 \\ \hline 8 & 7 & 6.8 & 2.51 \\ 9 & 8 & 6.3 & 2.81 \\ \hline 10 & 9 & 10.3 & 2.85 \\ 11 & 10 & 0.4 & 2.61 \end{tabular}.

1. A sports analyst for Major League Baseball wonders whether there is a relationship between a
pitcher's salary (in S millions) and his earned run average (ERA). The accompanying table lists a
portion of the data that she collected for 10 pitchers: a-1. Estimate the model: salary =0+1kA+.
(Negotive volues should be indicated by o minus sign. Enter your answers, in millions, rounded
to 2 decimal ploces.) 0-2. Interpret the coefficient of ERA. A one-unit increase in ERA,
predicted salary decreases by $226 million A one-unit increase in ERA, predicted salary
increases by $226 milion. A one-unit increase in ERA, predicted salary decreases by $975
milion. A one-unit increase in ERA, predicted salary increases by $9.75 million. b. Use the
estimated model to predict salary for Player 1 and Player 2 For example, use the sample
regression equation to predict the salary for Pitcher 1 with ERA =2.28 (Do not round
intermediote colculations. Round your final onswers (in millions) to 2 decimal ploces.)
b. Use the estimated model to predict salary for Player 1 and Player 2 . For example, use the
sample regression equation to predict the salary for Pitcher 1 with ERA =228. (Do not round
intermediote calculations. Round your final answers (in millions) to 2 decimal places:) c. Derive
the corresponding residuats for Player 1 and Player 2 (Negative values should be indicoted by a
minus sign. Round your final answers (in millions) to 2 decimal places.)
begin{tabular}{|l|l|l|l|} hline & multicolumn{1}{c|}{ A } & multicolumn{1}{c|}{ B } &
hline 1 & Pitcher & Salary & ERA hline 2 & 1 & 14 & 2.28 hline 3 & 2 & 2 & 2.43
hline 4 & 3 & 0.4 & 2.58 hline 5 & 4 & 7 & 2.63 hline 6 & 5 & 9 & 2.57 7 & 6 & 6.2 &
2.26 hline 8 & 7 & 6.8 & 2.51 9 & 8 & 6.3 & 2.81 hline 10 & 9 & 10.3 & 2.85 11 & 10
& 0.4 & 2.61 end{tabular}