Exercise 14-29 Algo

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.

Pitcher	Salary	ERA
1	10.0	2.51
2	3.0	2.52
10	0.4	2.83

Excel Data File

Pitcher	Salary	ERA
1	10	2.51
2	3	2.52
3	0.2	2.27
4	10	2.13
5	9	2.12
6	4.9	2.56
7	6.8	2.87
8	7	2.88
9	10	2.57
10	0.4	2.83

a-1.Estimate the model:Salary =₀+₁ERA +.(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.

multiple choice

• A one-unit increase in ERA, predicted salary decreases by $3.16 million.
• A one-unit increase in ERA, predicted salary increases by $3.16 million.
• A one-unit increase in ERA, predicted salary decreases by $10.96 million.
• A one-unit increase in ERA, predicted salary increases by $10.96 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 J. Santana with ERA = 2.51.(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.)