In Exercise 9.63 we look at predicting the price (in $1000s) of New York homes based on the size (in thousands of square feet), using the data in HomesForSaleNY. Two other variables in the dataset are the number of bedrooms and the number of bathrooms. Use technology to create a multiple regression model to predict price based on all three variables: size, number of bedrooms, and number of bathrooms. (In Exercise 10.23, we investigate a similar model using homes from more states.)
(a) Which predictors are significant at a 5% level? Which variable is the most significant?
(b) Interpret the two coefficients for Beds and Baths. Do they both make sense?
(c) What price does the model predict for a 1500 square foot (Size = 1.5) New York home with 3 bedrooms and 2 bathrooms?

Exercise 9.63

People in real estate are interested in predicting the price of a house by the square footage, and predictions will vary based on geographic area. We look at predicting prices (in $1000s) of houses in New York state based on the size (in thousands of square feet).Arandom sample of 30 houses for sale in New York state is given in the dataset HomesForSaleNY.

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.)

Transcribed Image Text:

The regression equation is Price = - 217 + 0.331 SizeSqFt - 135 Beds + 200 Baths SE Coef Predictor Coef т Constant -217.0 145.9 -1.49 0.140 SizeSqFt Beds 4.55 0.000 0.33058 0.07262 -134.52 57.03 -2.36 0.020 Baths 200.03 78.94 2.53 0.013 S= 507.706 R-Sq = 46.7% R-Sq(adj) = 45.3% Analysis of Variance Source DF MS Regression 3 26203954 8734651 33.89 0.000 Residual Error 116 29900797 257765 Total 119 56104751