In Exercise 10.23 on page 569 we fit a model predicting the price of a home (in
Question:
Residual standard error: 507.7 on 116 degrees of freedom
Multiple R-squared: 0.4671, Adjusted R-squared: 0.4533
F-statistic: 33.89 on 3 and 116 DF, p-value: 8.383e-16
(a) Compare this output to the regression output in Exercise 10.23 and comment on how the coefficient, standard error, and t-statistic for Size change when we code the variable in 1000s of square feet rather than square feet as in SizeSqFt.
(b) Interpret the coefficient for Beds, the number of bedrooms in this fitted model.
(c) An architect (who has not taken statistics) sees this output and decides to build houses with fewer bedrooms so they will sell for more money. As someone who has taken statistics, help him to correctly interpret this output.
Exercise 10.23
Here is some output for fitting a model to predict the price of a home (in $1000s) using size (in square feet, SizeSqFt, different units than the variable Size in HomesForSale), number of bedrooms, and number of bathrooms. (The data are based indirectly on information in the HomesForSale dataset.)
Step by Step Answer:
Statistics Unlocking The Power Of Data
ISBN: 9780470601877
1st Edition
Authors: Robin H. Lock, Patti Frazer Lock, Kari Lock Morgan, Eric F. Lock, Dennis F. Lock