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 How well does this model do in explaining variation in Monthly_Payment? Explain in detail. Write down the regression equation. 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. Interpret the coefficient for "Gender" from a practical point of view. Are any of the three independent variables in the model insignificant at the 5% level of significance? Explain your reasoning