A statistical program is recommended.

Spring is a peak time for selling houses. Suppose the data below contains the selling price, number of bathrooms, square footage, and number of bedrooms of 26 homes sold in Ft. Thomas, Kentucky, in spring 2018.

Selling Price Baths Sq Ft Beds
160,000 1.5 1,786 3
170,000 2 1,768 3
178,000 1 1,219 3
182,500 1 1,578 2
195,100 1.5 1,125 4
212,500 2 1,196 2
245,900 2 2,128 3
250,000 3 1,280 3
255,000 2 1,596 3
258,000 2.5 2,374 4
267,000 2.5 2,439 3
268,000 2 1,470 4
275,000 2 1,688 4
Selling Price Baths Sq Ft Beds
295,000 2.5 1,860 3
325,000 3 2,056 4
325,000 3.5 2,776 4
328,400 2 1,408 4
331,000 1.5 1,972 3
344,500 2.5 1,736 3
365,000 2.5 1,990 4
385,000 2.5 3,640 4
395,000 2.5 1,928 4
399,000 2 2,108 3
430,000 2 2,462 4
430,000 2 2,615 4
454,000 3.5 3,700 4
Consider the estimated regression equation we developed that can be used to predict the selling price given the number of bathrooms, square footage, and number of bedrooms in the house.

(x1 denotes number of bathrooms, x2 denotes square footage, x3 denotes number of bedrooms, and y denotes the selling price.)

ŷ = −1770.46 + 18130.69x1 + 60.00x2 + 40706.14x3

(a)

Does the estimated regression equation provide a good fit to the data? Explain. (Round your answer to two decimal places.)

Since the adjusted R2=

(b)

Consider the estimated regression equation that was developed which predicts selling price given the square footage and number of bedrooms.

(x2 denotes square footage, x3 denotes number of bedrooms, and y denotes the selling price.)

ŷ = 7679.47 + 67.88x2 + 44959.40x3

Compare the fit for this simpler model to that of the model that also includes number of bathrooms as an independent variable. (Round your answer to Three decimal places.)

The adjusted R2 for the simpler model is: