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 xcel 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 Total 49 4820.0000 Coeff 0,4485 StdErrorS 0.7798 0.0003 0.0001 0.1383 Intecept 1.6333 3.8078-0.281 Size School 4.2615 0.6517 0.1137 0.8062 0.4319 1.50%9 3,9545 5.286 At the 0.01 level of significance, what conclusion should the builder reach regarding the inclusion of Income in the regression model? o Income is significant in explaining house size and should be included in the model because its p-value is more than 0.01 Income is significant in explaining house size and should be included in the model because its p-value is less than 0.01 Income is not significant in explaining house size and should not be included in the model because its p-value is less than 0.01. Income is not significant in explaining house size and should not be included in the model because its p-value is more than 0.01.

Explanation / Answer

The significance of a variable in the regression analysis is determined by determining the p-value of the given variable in the regression. Here clearly against the variable Income, the p-value is given to be 0.0003 which is way less than 0.01 which is the level of significance here.

This shows that the variable is significant at the 1% level of significance as the p-value is less than 0.01.

Therefore b) option is the correct answer here.

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