Spring is a peak time for selling houses. Suppose the data below contains the selling price,...

 

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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 Sq Ft Beds Selling Price Baths Beds 160,000 1.5 1,786 3 295,000 2.5 1,860 3 170,000 1,768 325,000 3 2,056 178,000 1 1,219 3 325,000 3.5 2,776 4 182,500 1,578 328,400 2 1,408 195,100 1.5 1,125 3 331,000 1.5 1,972 3 212,500 1,196 2 344,500 2.5 1,736 245,900 2 2,128 3 365,000 2.5 1,990 250,000 1,280 385,000 2.5 3,640 255,000 2 1,596 3 395,000 2.5 1,928 4 258,000 3.5 2,374 4 399,000 2 2,108 267,000 2,439 430,000 2 2,462 2.5 3 4 268,000 1,470 4 430,000 2 2,615 275,000 2 1,668 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. (x, denotes number of bathrooms, x2 denotes square footage, x3 denotes number of bedrooms, and y denotes the selling price.) ŷ = -5937.26 + -1321.24x, + 60.20x, + 54909.37x3 (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 = 0.48 |×, the estimated regression equation provides a good fit. (b) Consider the estimated regression equation that was developed which predicts selling price given the square footage and number of bedrooms. (x, denotes square footage, x, denotes number of bedrooms, and y denotes the selling price.) ŷ = -6269.36 + 59.68x, + 54442.69x3 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 two decimal places.) The adjusted R? for the simpler model is which is larger than the adjusted R2 in part (a). The model from part b: is preferred. 4. 3. 4. 3. 3. 2. 3. 2. 2. 3. 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 Sq Ft Beds Selling Price Baths Beds 160,000 1.5 1,786 3 295,000 2.5 1,860 3 170,000 1,768 325,000 3 2,056 178,000 1 1,219 3 325,000 3.5 2,776 4 182,500 1,578 328,400 2 1,408 195,100 1.5 1,125 3 331,000 1.5 1,972 3 212,500 1,196 2 344,500 2.5 1,736 245,900 2 2,128 3 365,000 2.5 1,990 250,000 1,280 385,000 2.5 3,640 255,000 2 1,596 3 395,000 2.5 1,928 4 258,000 3.5 2,374 4 399,000 2 2,108 267,000 2,439 430,000 2 2,462 2.5 3 4 268,000 1,470 4 430,000 2 2,615 275,000 2 1,668 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. (x, denotes number of bathrooms, x2 denotes square footage, x3 denotes number of bedrooms, and y denotes the selling price.) ŷ = -5937.26 + -1321.24x, + 60.20x, + 54909.37x3 (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 = 0.48 |×, the estimated regression equation provides a good fit. (b) Consider the estimated regression equation that was developed which predicts selling price given the square footage and number of bedrooms. (x, denotes square footage, x, denotes number of bedrooms, and y denotes the selling price.) ŷ = -6269.36 + 59.68x, + 54442.69x3 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 two decimal places.) The adjusted R? for the simpler model is which is larger than the adjusted R2 in part (a). The model from part b: is preferred. 4. 3. 4. 3. 3. 2. 3. 2. 2. 3.

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