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 + 120420 Bathrooms Model Summary S R-sq R-sq(adj) R-sq(pred) 267458 14.27% 14.17% 13.82% Regression Equation Price = 200274 + 113.68 Living Area Model Summary S R-sq R-sq(adj) R-sq(pred) 268449 13.54% 13.44% 13.10% Regression Equation Price = 338975 + 40234 Bedrooms 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 + 84.7 Living Area - 66797 Bedrooms + 93925 Bathrooms 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.
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 + 120420 Bathrooms |
Model Summary
|
S |
R-sq |
R-sq(adj) |
R-sq(pred) |
|
267458 |
14.27% |
14.17% |
13.82% |
Regression Equation
|
Price |
= |
200274 + 113.68 Living Area |
Model Summary
|
S |
R-sq |
R-sq(adj) |
R-sq(pred) |
|
268449 |
13.54% |
13.44% |
13.10% |
Regression Equation
|
Price |
= |
338975 + 40234 Bedrooms |
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 + 84.7 Living Area - 66797 Bedrooms + 93925 Bathrooms |
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.
Step by step
Solved in 2 steps with 2 images
