Realtor-regression analysis for house prices

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Reference no: EM1396167

You are a realtor with a small business. You use a simple 2 variable linear regression analysis for quick first glance house price estimates using square footage of the house. You are asked by a customer what the price for his house would be given that his house has 1,500 square feet. The number you are trying to explain is the potential price of the house. You look at your numbers from his neighborhood and run the regression analysis. Your program returns the following data: R square: .5808, Square footage (X) Coefficient: 95.0, Observations: 20, Degrees of Freedom: 19, Intercept t-stat: 1.87, Multiple R: .7723, Intercept Coefficient: 120,200, Square footage (X) Standard Error: .0288. What is the best first glance prediction you can give the customer about the value of his house given your 2 variable linear regression analysis?

a) 0 to 500

b) 501 to 100,000

c) 100,001 to 200,000

d) 200,001 to 400,000

e) 400,001 to 1.5 million

Reference no: EM1396167

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