Real Estate Family Square Feet Income (000s) Family Size Work/home Education Squ
ID: 3060239 • Letter: R
Question
Real Estate Family Square Feet Income (000s) Family Size Work/home Education Square Feet Size of home purchased 1 2,771 46.9 3 0 4 Income (000s) Family Income 2 2,380 68.4 2 0 6 Family Size Number of individuals in the immediate family 3 3,283 104.5 3 1 10 Work/home 1 if breadwinner plans to work from home, 0 if not 4 3,119 94.1 3 0 0 Education Years of education after high school 5 3,217 50.6 3 1 2 6 2,595 114 3 0 8 7 4,480 125.4 6 1 6 8 2,520 83.6 3 0 4 9 4,200 133 5 1 2 10 2,800 95 3 0 6 11 3,876 97.2 4 1 8 12 2,230 58.3 2 0 4 A locel real estate developer wishes to study the relationship between the size of home a client will purchase (in squere feet) and other variables. Possible independent variables include breedwinner to work at home (1 for yes, O for no), and the total yeers of education beyond high school for the husband and wife. The semple information is contained in the file RealEstateChepter14 Mr. Robert Bostick s ea, estate broker who asked you to produce a model to predict the square footage a potential buyer would purchase. Based on the data the fie Real stateChapterl 4 s the overa" egression modes g ificant ifa . 0.057 Use the six-step process 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 Do you calculations in Excel and title the file "YourNameealEstateDeveloper" and submit before midnight on March 13. the family income, family size, whether there plans for theExplanation / Answer
The multiple R squared of this model is 98% which means that these variables can explain about 98% of the variability in the response variable from the predictor variables.
The variable family size and education have a p-value of about 0.004 which is very low than significance level.
Hence, these two variables significantly affect the size of home.
SUMMARY OUTPUT Regression Statistics Multiple R 0.980433958 R Square 0.961250746 Adjusted R Square 0.939108315 Standard Error 179.5372594 Observations 12 ANOVA df SS MS F Significance F Regression 4 5597325.524 1399331.4 43.41216 4.9985E-05 Residual 7 225635.3925 32233.628 Total 11 5822960.917 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 1562.054947 218.782745 7.1397539 0.000187 1044.71597 2079.393929 Income (000s) 2.171827375 2.987339985 0.7270104 0.490812 -4.8921092 9.235763905 Family Size 370.1428652 89.30585453 4.1446652 0.004324 158.968077 581.3176532 Work/home 607.2850118 149.2543133 4.0687937 0.004756 254.354645 960.2153783 Education -24.0304359 21.21999509 -1.132443 0.294743 -74.207751 26.14687879Related Questions
Navigate
Integrity-first tutoring: explanations and feedback only — we do not complete graded work. Learn more.