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Question
NARRBEGIN: SA_119_123A realtor in a local area is interested in being able to predict the selling price for a newly listed home or for someone considering listing their home. This realtor would like to attempt to predict the selling price by using the size of the home (
, in hundreds of square feet), the number of rooms (
), the age of the home (
, in years) and if the home has an attached garage (
). Use the output below to determine if this realtor will be able to use this information to predict the selling price (in $1000).| Summary measures | |
| Multiple R | 0.9439 |
| R-Square | 0.891 |
| Adj. R-Square | 0.8474 |
| StErr of Estimate | 22.241 |
| Regression coefficients | ||||
| Coefficient | Std Err | t-value | p-value | |
| Constant | -19.026 | 54.769 | -0.3474 | 0.7355 |
| Size | 7.494 | 1.529 | 4.901 | 0.0006 |
| Number of Rooms | 7.153 | 9.211 | 0.7767 | 0.4553 |
| Age | -0.673 | 0.992 | -0.6789 | 0.5126 |
| Attached Garage | 0.453 | 20.192 | 0.0224 | 0.9826 |
(A) Use the information above to estimate the linear regression model.
(B) Interpret each of the estimated regression coefficients of the regression model in (A).
(C) Would any of the variables in this model be considered a dummy variable? Explain your answer.
(D) Identify and interpret the coefficient of determination (
) and the standard error of the estimate (se) for the model in (A).(E) Use the estimated model in (A) to predict the sales price of a 2500 square feet, 15-year old house that has 5 rooms and an attached garage.
Answer
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