Using data from the 2012 season for MLB a baseball analytics firm wants to determine which variable are important in predicting a teams wins. The data collected in clues wins, earned run average (ERA), and runs scored for the 2012 season. The firm also has each team categorized as being in the 0=American league or 1= National league.

The first thing the firm does is runs a model to predict wins based on ERA and number of runs scored. The Summary output is below.

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.961591
R Square	0.924658
Adjusted R Square	0.919077
Standard Error	3.394783
Observations	30
ANOVA
	df	SS	MS	F	Significance F
Regression	2	3818.837	1909.419	165.6827	6.92E-16
Residual	27	311.1629	11.52455
Total	29	4130
	Coefficients	Standard Error	t Stat	P-value
Intercept	83.04052	9.265717	8.962126	1.41E-09
E.R.A.	-19.0864	1.257472	-15.1783	9.66E-15
Runs Scored	0.106277	0.010911	9.740068	2.49E-10

What is the regression model equation

Group of answer choices

Wins = -19.08 (ERA) + 0.106 (Runs Scored)

Wins = -19.08 (ERA) + 0.106 (Runs Scored) + 83.04

-19.08 (ERA) = 0.106 (Runs Scored)

Wins = 8.96 + -15.18 (ERA) + 9.74 (Runs Scored)