10 (14 points)

A data analyst used the correlation and regression analysis routines of Excel to develop a multiple regression model for the variable Monthly_Amount* with the independent variables Age, Gender (Female = 1, Other = 0) and Salary (in $).

*Monthly_Amount is the amount a person is willing to pay in order to have net zero electricity bills through renewable energy (e.g. Solar panels with battery)

Excel's regression output is reproduced below.

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.8241
R Square	0.6791
Adjusted R Square	0.6767
Standard Error	176.2108
Observations	400
ANOVA
	df	SS	MS	F	Significance F
Regression	3	26021860.22	8673953.406	279.3521969	0.0000
Residual	396	12295895.96	31050.24232
Total	399	38317756.18
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
Intercept	91.3564	35.5927	2.5667	0.0106	21.3820	161.3308
Age	-2.0757	0.7979	-2.6014	0.0096	-3.6443	-0.5070
Gender	5.6544	17.8697	0.3164	0.7518	-29.4769	40.7857
Salary	0.0024	0.0002	28.7764	0.0000	0.0021	0.0027

1. How well does this model do in explaining variation in Monthly_Payment? Explain in detail.
2. Write down the regression equation.
3. Use your equation from (b) to predict the amount (in $) a person would be willing to pay per month for renewable energy in order to have zero electricity bills, if the person was 50 years of age, Female, and had a Salary of $80,000. Show your calculations.
4. Interpret the coefficient for "Gender" from a practical point of view.
5. Are any of the three independent variables in the model insignificant at the 5% level of significance? Explain your reasoning