A baseball analyst would like to develop a model to predict the number of wins during

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A baseball analyst would like to develop a model to predict the number of wins during the 2014 baseball season. The analyst collected several variables: Wins, ERA, Runs Scored, Hits and Saves from 27 professional baseball teams. The data is shows below.

Regression Statistics
Multiple R	0.9232
Observations	27

ANOVA

	df	SS	MS	F	Significance F
Regression	??	469.348	??	??	0.000145539
Residual	??	??	??
Total	??	746.297

Variable	Coefficient	Standard Error	T value	P value	Lower 95%	Upper 95%
Intercept	72.456	14.417	5.02573	0.00002	??	??
ERA	-10.234	3.3201	??	0.0007	-17.1194	-3.348
Runs Scored	0.0723	0.0086	8.40698	0.00123	0.05446	0.09013
Hits	-0.0174	0.01132	??	0.0457	-0.04087	0.00607
Saves	2.215	1.218	??	0.06234	??	??

1) Fill in the tables with the missing values? (You should have 14 different values you need to find)

2) What is the coefficient of determination?

3) What is the adjusted r2?

4) What is the standard error of the estimated regression line?

5) What is the estimated regression equation?

6) Use the estimated regression equation to predict Wins when ERA 6.6, Runs Score is 925, Hits Allowed is 1523 and Saves are 33.

7) Calculate the residual for the observed described in number 6), if the original Wins for that observation were 109.

8) What is the hypothesis scenario?

9) Is there evidence of a significant relationship between the OVERALL Wins and the independent variables? Use alpha = .05. (State the p-value and the conclusion)

10) Which of the independent variables are significant predictors of Wins? Which ones do you want to include in your model? Use alpha = .05

Hint

Statistics df SS MS F Significance F Regression 4 469.348 117.337 9.320900238 0.000145539 Residual 22 276.949 12.58859091 Total 26 ...

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