H webassignnet 178,000 325,000 3.5 2,776 4 182,500 1 1,558 2 328,400 2 1,408 4 195,100 1.5 1,125 4 331,000 1.5 1,972 3 212,500 2 1,196 2 344,500 2.5 1,736 3 245,900 2 2,128 3 365,000 2.5 1,990 4 250,000 3 1,280 3 385,000 2.5 3,640 4 255,000 2 1,596 3 395,000 2.5 1,928 4 258,000 3.5 2,374 4 399,000 2 2,108 3 267,000 2.5 2,439 3 430,000 2 2,462 4 268,000 2 1,470 4 430,000 2 2,615 4 275,000 2 1,688 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, )(2 denotes square footage, )(3 denotes number of bedrooms, and y denotes the selling price.) ,7 = 7268.37 + 5262.37x1 + 66.71X2 + 43666.36X3 (a) Does the estimated regression equation provide a good t to the data? Explain. (Round your answer to two decimal places.) Since the adjusted R2 = , the estimated regression equation WSeIect-u a good fit. ' (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 numberof bedrooms, and y denotes the selling price.) i 7 = 8463.93 + 68.18XZ + 44584.43X3 Compare the t 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 R2 for the simpler model is , which is u-Selectu- than the adjusted R2 In part (a). The model from part 2 is preferred H webassignnel 10. [-110 Points] ASWSBE14 15.E.017. A statistical program is recommended. Spring is: peak time for selling houses. Squose 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 018. I 78!!an Price Baths Sq Ft Beds Selling Price Sq Ft 160,000 1.5 1,766 3 295,000 . 1,860 170,000 2 1,768 325,000 2,056 178,000 1,219 325,000 , 2,776 182,500 1,558 328,400 = 1,408 195,100 , 1,125 331,000 . 1,972 212,500 1,196 344,500 . 1,736 245,900 2,128 365,000 . 1,990 250,000 1,280 385,000 . 3,640 255,000I 1,596 395,000 . 1,928 258,000 . 2,374 399,000 2,108 267,000 . 2,439 430,000 2,462 268,000 1,470 430,000 2,615 275,000 1,688 454,000 , 3,700 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.) 7 = 7268.37 + 3262.37X1 + 66.71X2 + 43666.36X3 (a) Does the estimated regression equation provide a good t to the data? Explain. (Round your answer to two decimal places.) Since the adjusted R2 = , the estimated regression equation maelect-u a good fit. ' ~ (b) Consider the estimated regression equation that was developed which predicts selling price given the square footage and number of bedrooms. (x2 denotes square footweggemgiagmg