A real estate builder wishes to determine how house size (House) is influenced b

Question

A real estate builder wishes to determine how house size (House) is influenced by family income (Income), family size (Size), and education of the head of household (School). House size is measured in hundreds of square feet, income is measured in thousands of dollars, and education is in years. The builder randomly selected 50 families and ran the multiple regression. MicrosoftG Excel output is provided below: SUMMARY OUTPUT Regression Statisties Multiple R R Square Adjusted R Square 0.726 Standard Error 0.865 0.748 .195 50 ANOVA Signif F 0,0000 MS 3605.7736 1201.9245 1214.2264 26.3962 Residual Total 49 4820.0000 StdErrorS 5.8078 0.281 0.1137 0.8062 0.4319 1.50%9 P-valwe 0.7798 0.0003 0.0001 0.1383 Coeff Intercept Income Size School - 0.4485 4.2615 -0.6517 3,9545 5.286 At the 0.01 level of significance, what conclusion should the builder draw regarding the inclusion of School in the regression model? C School is signifcant in explaining house size and should be included in the model because its p-value is more than 0.01 C School is not significant in explaining house size and should not be included in the model because its p-value is more than 0.01 C School is signifcant in explaining house size and should be included in the model because its p-value is less than 0.01 C School is not significant in explaining house size and should not be included in the model because its p-value is less than 0.01

Explanation / Answer

To check the significance of a variable in the regression analysis, we analyze the p-value against that variable. The p-value for the School variable here is given to be 0.1383 which is way greater than 0.01.
This shows that the variable is not significance at the 1% level of significance.

Therefore school is not a significant in explaining the house size and should not be included in the model because its p-value is more than 0.01.

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