The regression model presented below was developed to help a realtor better estimate the value of houses in the immediate area.
R Square 0.83685
Adjusted R Square 0.79236
Standard Error 2,195.96551
Total Number Of Cases 15
d.f. SS MS F
Regression 3 272091090.1513 90697030.0504 18.80797
Residual 11 53044909.8487 4822264.5317
Total 14 325136000.0000
Coefficients Standard Error t Stat p-level
Intercept 165,456.71 5834.6162 28.3578 0.0000
Living Area (Square Feet) 10.57 3.0589 3.4543 0.0054
Age (Years) -309.64 89.9571 -3.4421 0.0055
# of Bedrooms -597.65 715.3044 -0.8355 0.4212
Based on these results, and assuming the realtor wished to estimate the value of a home with 2,334 square feet of living area is 39 years old and has 4 bedrooms, which of the following statements is correct?
a. The estimated value of the home is $204,585
b. 91.48% of the change in the value of the home can be explained by changes in the living
area, age of the home and the number of bedrooms
c. This model cannot be used to estimate the value of a home as there are t-stat values
that are less than 2.0
d. The estimated value of the home is $175,661
e. The estimated value of the home is $190,118
Y = 165,456.71 + 10.57* square feet - 309.64* age - ...
This solution assists with the statistics problems related to management accounting.