A sports analyst for Major League Baseball wonders whether there is a relationship between a pitcher's salary (in $ millions) and his earned run average (ERA). The accompanying table lists a portion of the data that she collected for 10 pitchers. A Click herefor the Excel Data File a-1. Estimate the model: Salary =0+1 ERA +e1 (Negative values should be indicated by a minus sign. Enter your answers, in millions, rounded to 2 decimal places.) a-2. Interpret the coefficient of ERA. A one-unit increase in ERA, predicted salary decreases by $3.82 million. A one-unit increase in ERA, predicted salary increases by $3.82 million. A one-unit increase in ERA, predicted salary decreases by $14.61 million, A one-unit increose in ERA, predicted salary increases by $14.61 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 = 2.53. (Do not round intermediate calculations. Round your final answers (in millions) to 2 decimal places.) c. Derive the corresponding residuals for Player 1 and Player 2. (Negative values should be indicated by a minus sign. Round your final answers (in millions) to 2 decimal places.)