Question

complete a multiple regression and select the best mix of IV to explain game attendance. Once you have the best mix of variables, accept or reject the nulls, create the formula for predicting the regression, lower 95% Confidence Interval and upper 95% Confidence Interval

Game Number	Game Attendance	Team Win/Loss Percentage	Opponent Win/Loss Percentage	Games Played	Temperature
1	14,502	33.3	80	6	47
2	12,459	25	50	4	56
3	15,600	80	66.6	5	55
4	16,780	75	100	8	60
5	14,600	60	80	10	55
6	19,300	100	60	10	49
7	14,603	66.6	25	3	67
8	15,789	50	50	6	55
9	17,800	80	40	10	53
10	19,450	75	100	8	48
11	13,890	20	75	5	65
12	15,097	70	70	10	56
13	17,666	83.3	66.6	6	60
14	12,500	20	20	5	59
15	16,780	80	100	8	46
16	17,543	80	70	10	50

Football Attendance at State University
	File: Football.xlsx
			Column A: Game Number
			Column B: Game Attendance
			Column C: Win/Loss Percentage
			Column D: Opponent Win/Loss Percentage
			Column E: Games Played
			Column F: Temperature

Answer 1

The best model is:

	r²	0.720
	r	0.849
	Std. Error	1170.998
	n	16
	k	1
	Dep. Var.	Game Attendance
ANOVA table
Source	SS	df	MS	F	p-value
Regression	4,94,56,399.4686	1	4,94,56,399.4686	36.07	3.22E-05
Residual	1,91,97,324.4689	14	13,71,237.4621
Total	6,86,53,723.9375	15
Regression output				confidence interval
variables	coefficients	std. error	t (df=14)	p-value	95% lower	95% upper
Intercept	11,392.0432
Team Win/Loss Percentage	72.2163	12.0249	6.006	3.22E-05	46.4255	98.0071

The regression equation is:

Game Attendance = 11,392.0432 + 72.2163*Team Win/Loss Percentage

lower 95% Confidence Interval = 46.4255

upper 95% Confidence Interval = 98.0071