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 160,000 170,000 178,000 182,500 195,100 212,500 245,900 250,000 255,000 258,000 267,000 268,000 275,000 Baths 1.5 1,786 2 1,768 1 Sq Ft 1 1,578 1,219 1.5 1,125 2 1,196 2 2,128 2 3 1,280 2.5 1,596 3.5 2,374 2,439 2 1,470 2 1,688 Beds 3 3 3 2 3 2 3 3 3 4 3 4 4 Selling Price 295,000 325,000 325,000 328,400 331,000 344,500 365,000 385,000 395,000 399,000 430,000 430,000 454,000 Baths 2.5 3.5 3 2,056 1.5 Sq Ft 2 1,408 1,860 2.5 2,776 2.5 1,736 2 2.5 1,990 2 1,972 2.5 3,640 1,928 2,108 2,462 2 2,615 3.5 3,700 Beds 3 4 4 4 3 3 4 4 4 3 4 4 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 footal number of bedrooms, and y denotes the selling price.) 9 = -5761.41 +-1226.47x₁ + 60.16x₂ + 54800.77x3 (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 = , the estimated regression equation ---Select--- ✓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 number of bedrooms, and y denotes the selling price.) y=-6071.34 + 59.68x₂ +54368.03x3 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 p2 for the simpler model is which is Select than the adjusted p2 in part (a). The model from part 2 is preferred
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 160,000 170,000 178,000 182,500 195,100 212,500 245,900 250,000 255,000 258,000 267,000 268,000 275,000 Baths 1.5 1,786 2 1,768 1 Sq Ft 1 1,578 1,219 1.5 1,125 2 1,196 2 2,128 2 3 1,280 2.5 1,596 3.5 2,374 2,439 2 1,470 2 1,688 Beds 3 3 3 2 3 2 3 3 3 4 3 4 4 Selling Price 295,000 325,000 325,000 328,400 331,000 344,500 365,000 385,000 395,000 399,000 430,000 430,000 454,000 Baths 2.5 3.5 3 2,056 1.5 Sq Ft 2 1,408 1,860 2.5 2,776 2.5 1,736 2 2.5 1,990 2 1,972 2.5 3,640 1,928 2,108 2,462 2 2,615 3.5 3,700 Beds 3 4 4 4 3 3 4 4 4 3 4 4 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 footal number of bedrooms, and y denotes the selling price.) 9 = -5761.41 +-1226.47x₁ + 60.16x₂ + 54800.77x3 (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 = , the estimated regression equation ---Select--- ✓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 number of bedrooms, and y denotes the selling price.) y=-6071.34 + 59.68x₂ +54368.03x3 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 p2 for the simpler model is which is Select than the adjusted p2 in part (a). The model from part 2 is preferred
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Transcribed Image Text: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
Selling Price
160,000
170,000
178,000
182,500
195,100
212,500
245,900
250,000
255,000
258,000
267,000
268,000
275,000
1.5 1,786
2 1,768
1 1,219
1 1,578
1.5 1,125
2 1,196
2 2,128
N W
3
2
1,280
1,596
3.5 2,374
2.5 2,439
2 1,470
2 1,688
Beds
3
3
3
2
3
2
3
3
3
4
3
4
4
295,000
325,000
325,000
328,400
331,000
344,500
365,000
385,000
395,000
399,000
430,000
430,000
454,000
Baths Sq Ft
2.5 1,860
3 2,056
3.5 2,776
2
1,408
1.5 1,972
2.5 1,736
2.5 1,990
2.5 3,640
2.5 1,928
2 2,108
2
2,462
2 2,615
3.5 3,700
Beds
3
4
4
4
3
3
4
4
4
3
4
4
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.)
y = -5761.41 +-1226.47x₁ + 60.16x₂ +54800.77x3
(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 =
the estimated regression equation --Select---
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 number of bedrooms, and y denotes the selling price.)
y = -6071.34 + 59.68x₂ +54368.03x3
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 R² for the simpler model is
, which is ---Select--- than the adjusted R² in part (a). The model from part? is preferred.
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