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4-16 Steve Caples, a real estate appraiser in Lake Charles, Louisiana, has developed a regression model to help appraise residential housing in the Lake Charles area. The model was developed using recently sold homes in a particular neighborhood. The price (Y) of the house is based on the square footage (X) of the house. The model is Ŷ= 33,478 +62.4X The coefficient of correlation for the model is 0.63. (a) Use the model to predict the selling price of a house that is 1,860 square feet. (b) A house with 1,860 square feet recently sold for $165,000. Explain why this is not what the model predicted. (c) If you were going to use multiple regression to develop an appraisal model, what other quantita- tive variables might be included in the model? (d) What is the coefficient of determination for this model?

4-16 Steve Caples, a real estate appraiser in Lake Charles, Louisiana, has developed a regression model to help appraise residential housing in the Lake Charles area. The model was developed using recently sold homes in a particular neighborhood. The price (Y) of the house is based on the square footage (X) of the house. The model is Ŷ= 33,478 +62.4X The coefficient of correlation for the model is 0.63. (a) Use the model to predict the selling price of a house that is 1,860 square feet. (b) A house with 1,860 square feet recently sold for $165,000. Explain why this is not what the model predicted. (c) If you were going to use multiple regression to develop an appraisal model, what other quantita- tive variables might be included in the model? (d) What is the coefficient of determination for this model?

MATLAB: An Introduction with Applications
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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4-16 Steve Caples, a real estate appraiser in Lake Charles, Louisiana, has developed a regression model to help appraise residential housing in the Lake Charles area. The model was developed using recently sold homes in a particular neighborhood. The price (Y) of the house is based on the square footage (X) of the house. The model is Ŷ = 33,478 + 62.4X The coefficient of correlation for the model is 0.63. (a) Use the model to predict the selling price of a house that is 1,860 square feet. (b) A house with 1,860 square feet recently sold for $165,000. Explain why this is not what the model predicted. (c) If you were going to use multiple regression to develop an appraisal model, what other quantita- tive variables might be included in the model? (d) What is the coefficient of determination for this model?
Transcribed Image Text:4-16 Steve Caples, a real estate appraiser in Lake Charles, Louisiana, has developed a regression model to help appraise residential housing in the Lake Charles area. The model was developed using recently sold homes in a particular neighborhood. The price (Y) of the house is based on the square footage (X) of the house. The model is Ŷ = 33,478 + 62.4X The coefficient of correlation for the model is 0.63. (a) Use the model to predict the selling price of a house that is 1,860 square feet. (b) A house with 1,860 square feet recently sold for $165,000. Explain why this is not what the model predicted. (c) If you were going to use multiple regression to develop an appraisal model, what other quantita- tive variables might be included in the model? (d) What is the coefficient of determination for this model?
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