A local real estate developer wishes to study the relationship between the size
ID: 3130247 • Letter: A
Question
A local real estate developer wishes to study the relationship between the size of home a client will purchase (in square feet) and other variables. Possible independent variables include the family income, family size, whether there is a senior adult parent living with the family (1 for yes, 0 for no), and the total years of education beyond high school for the husband and wife. Mr. Robert Bostick is a real estate broker who asked you to produce a model to predict the square footage a potential buyer would purchase. Based on the data in the file RealEstateChapter14, is the overall regression model significant if = 0.05? Write a memorandum to Mr. Bostick explaining if the model is significant and if so, which variables make a difference in the size of home a client might purchase. Please show calculations in Excel.
6
Square Feet Size of home purchased Income (000s) Family Income Family Size Number of individuals in the immediate family Senior Parent 1 if a senior parent lives in the household, 0 if not Education Years of education after high schoolExplanation / Answer
Dear Mr. Bostick explaining,
After running the multiple linear regression on the data provided by you, it turns out to be that the model is significant. Please have a look at the following output.
Based on this output we can write the regression model as -
Square Foot = 1650.15 + 4.46(Income) + 431.33 (Family Size) -156.14 (Senior Parent) - 67.07(Education)
The regression is overall significant as the F value is much higher than the critical F-value for the overall significance test of model (it is there in the output table).
The R-square value is approximately 0.93, which indicates this is a good model.
The variable "Family Size" is most effective in deciding the area of house a person would be looking for which is also logical. And I think that the presence of senior parent doesn't affect much on the area of house to be purchased. And of course a person with high income will look for larger area.
SUMMARY OUTPUT Regression Statistics Multiple R 0.966550317 R Square 0.934219516 Adjusted R Square 0.881595129 Standard Error 242.1415833 Observations 10 ANOVA df SS MS F Significance F Regression 4 4163519.768 1040879.942 17.75259659 0.003701774 Residual 5 293162.7317 58632.54635 Total 9 4456682.5 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 1650.146024 342.2615545 4.821301144 0.004792953 770.3346891 2529.957359 Income (000s) 4.464850481 4.564738239 0.978117528 0.372937182 -7.269182722 16.19888368 Family Size 431.3283167 125.6296288 3.433332731 0.018568565 108.3870748 754.2695586 Senior Parent -156.1360569 212.3396625 -0.73531273 0.495170804 -701.9725363 389.7004225 Education -67.07422456 29.46766479 -2.276197488 0.071879163 -142.8232684 8.674819286Related Questions
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