) State the multiple regression equation. (b) Interpret the meaning of the slopes b1 and b2 for this problem. (c) Interpret the meaning of Y-intercept b0 for this problem. (d) Predict the average market value of a home with house age equal to 30 years and house size equal to 1600 square feet assuming that these numbers are within the data range. (e) Determine the coefficient of multiple determination and interpret its meaning. (f) Determine the adjusted   and interpret its meaning.

MATLAB: An Introduction with Applications
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Author:Amos Gilat
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Chapter1: Starting With Matlab
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Following are the regression results for a database of home market values in a locality with market value of the house (in dollars) as the dependent variable y and the age of the house (in years) and the size of the house (in square feet) as the two independent x variables.

 

 

(a) State the multiple regression equation.
(b) Interpret the meaning of the slopes b1 and b2 for this problem.
(c) Interpret the meaning of Y-intercept b0 for this problem.
(d) Predict the average market value of a home with house age equal to 30 years and house size equal to 1600 square feet assuming that these numbers are within the data range.
(e) Determine the coefficient of multiple determination and interpret its meaning.
(f) Determine the adjusted   and interpret its meaning.

 

SUMMARY OUTPUT
Regression Statistics
Multiple R
R Square
Adjusted R Square
Standard Error
Observations
ANOVA
Regression
Residual
Total
Intercept
House Age
Square Feet
0.899842191
0.809715969
0.794493247
4704.715377
df
28
SS
MS
F
Significance F
2 2354708116 1.177E+09 53.19127 9.82945E-10
25
553358669.4 22134347
27
2908066786
Coefficients Standard Error 1 Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
23558.7777 13138.09826 1.793165 0.085055 -3499.642181 50617.1976 -3499.64218 50617.19757
-25.06560949 507.1207835 -0.049427 0.960972 -1069.500414 1019.3692 -1069.50041 1019.369195
39.92650627 4.631701332 8.6202679 5.86E-09 30.38733881 49.4656737 30.38733881 49.46567373
Transcribed Image Text:SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept House Age Square Feet 0.899842191 0.809715969 0.794493247 4704.715377 df 28 SS MS F Significance F 2 2354708116 1.177E+09 53.19127 9.82945E-10 25 553358669.4 22134347 27 2908066786 Coefficients Standard Error 1 Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% 23558.7777 13138.09826 1.793165 0.085055 -3499.642181 50617.1976 -3499.64218 50617.19757 -25.06560949 507.1207835 -0.049427 0.960972 -1069.500414 1019.3692 -1069.50041 1019.369195 39.92650627 4.631701332 8.6202679 5.86E-09 30.38733881 49.4656737 30.38733881 49.46567373
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