Question: The computer output (below) shows a relationship where Y = Sale price for a home, X 1 = living area, and X 2 = #
The computer output (below) shows a relationship where Y = Sale price for a home, X1 = living area, and X2 = # of bedrooms, X3 = # of bathrooms. We would like to predict Price (Y). There are three 2-variable regression relationships shown and one 4-variable multiple regression relationship shown.
Regression Equation
Price | = | 171032 +120420Bathrooms |
Model Summary
S | R-sq | R-sq(adj) | R-sq(pred) |
267458 | 14.27% | 14.17% | 13.82% |
Regression Equation
Price | = | 200274 +113.68LivingArea |
Model Summary
S | R-sq | R-sq(adj) | R-sq(pred) |
268449 | 13.54% | 13.44% | 13.10% |
Regression Equation
Price | = | 338975 +40234Bedrooms |
Model Summary
S | R-sq | R-sq(adj) | R-sq(pred) |
286741 | 1.35% | 1.24% | 0.92% |
**Multiple regression output is below:
Regression Equation
Price | = | 275641 +84.7LivingArea -66797Bedrooms +93925Bathrooms |
Model Summary
S | R-sq | R-sq(adj) | R-sq(pred) |
260320 | 18.97% | 18.69% | 18.21% |
Coefficients
Term | Coef | SE Coef | T-Value | P-Value | VIF |
Constant | 275641 | 40655 | 6.78 | 0.000 |
Final multiple regression relationship:
Coefficients
Term Coef SE Coef T-Value P-Value
Constant 275641 40655 6.78 0.000
Living Area 84.7 13.4 6.33 0.000
Bedrooms -66797 13089 -5.10 0.000
Bathrooms 93925 13660 6.88 0.000
Model Summary
S R-sq R-sq(adj) R-sq(pred)
260320 18.97% 18.69% 18.21%
What is the independent variable that has the most influence in predicting Price? Explain your reasoning.
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