a. What proportion of variance in the price of a house can be explained by the combination of all these explanatory variables? Is this proportion of variance statistically significant at 05 level of significance? In other words, is the overall model statistically significant at .05 level of significance? Justify your answer. (3p) R2=.534=53.4% - P - value .00010.05 we reject Ho so we can conclude that the overall model is 53.4% statistically significant b. Find the corresponding regression equation. Does each variable (SqFt, Bed3, Bh and B2) significantly predict the price of the house? Justify your answer and report coefficients and p values for each variable. (14 p ) - Price =56337.077+22.018SqFt6090.532D_Bed+14444.185Bh+ 16004.492B2 - SqEt: P - value is.04438 .05 we reject Ho and we can conclude that the Sqft is statistically significant. Coeff: 22.018 - D bed: P - value is .32589.05 we fail to reject Ho we can conclude that D bed is not statistically significant. Coeff: -6090.532 - Bh: P- value is .01488.05 we reject Hn and we can conclude that the Bh is statistically significant. Coeff: 14444.185 - B2 P- value is .00549.05 we reject Ho and we can conclude that the B2 is statistically significant. Coeff: 16004.492 c. Is there any variable in the data that does not significantly contribute to the model? If so, explain the reason for that and exclude the nonsignificant variable from the model and re-run the multiple regression analysis. (4 p) - Yes, D Bed because usually a house that has more bedrooms have more SqEt and baths causing them to be highly correlated between the two causing a multicollinearity. d. Do you observe any changes after excluding the variable in the model? Does the overall model fit the data well? Justify your answer and report the coefficient of determination for the model and the coefficient for each variable. Comment on the statistical significance of each variable. (9p) 5. Run the multiple regression analysis including all explanatory variables: Square Feet, the dummy variable Bed3, the dummy variables Bh and B2. Excel Output (5p) * Normal summak oution 5 *** The Output Below Excludes Bedroom a. What proportion of variance in the price of a house can be explained by the combination of all these explanatory variables? Is this proportion of variance statistically significant at 05 level of significance? In other words, is the overall model statistically significant at .05 level of significance? Justify your answer. (3p) R2=.534=53.4% - P - value .00010.05 we reject Ho so we can conclude that the overall model is 53.4% statistically significant b. Find the corresponding regression equation. Does each variable (SqFt, Bed3, Bh and B2) significantly predict the price of the house? Justify your answer and report coefficients and p values for each variable. (14 p ) - Price =56337.077+22.018SqFt6090.532D_Bed+14444.185Bh+ 16004.492B2 - SqEt: P - value is.04438 .05 we reject Ho and we can conclude that the Sqft is statistically significant. Coeff: 22.018 - D bed: P - value is .32589.05 we fail to reject Ho we can conclude that D bed is not statistically significant. Coeff: -6090.532 - Bh: P- value is .01488.05 we reject Hn and we can conclude that the Bh is statistically significant. Coeff: 14444.185 - B2 P- value is .00549.05 we reject Ho and we can conclude that the B2 is statistically significant. Coeff: 16004.492 c. Is there any variable in the data that does not significantly contribute to the model? If so, explain the reason for that and exclude the nonsignificant variable from the model and re-run the multiple regression analysis. (4 p) - Yes, D Bed because usually a house that has more bedrooms have more SqEt and baths causing them to be highly correlated between the two causing a multicollinearity. d. Do you observe any changes after excluding the variable in the model? Does the overall model fit the data well? Justify your answer and report the coefficient of determination for the model and the coefficient for each variable. Comment on the statistical significance of each variable. (9p) 5. Run the multiple regression analysis including all explanatory variables: Square Feet, the dummy variable Bed3, the dummy variables Bh and B2. Excel Output (5p) * Normal summak oution 5 *** The Output Below Excludes Bedroom
