A home appraisal company would like to develop a regression model that predicts

# A home appraisal company would like to develop a

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Use the following information to answer the question(s) below. A home appraisal company would like to develop a regression model that predicts the selling price of a house based on the age of the house in years (age), the living area of the house in square feet (living area), and the number of bedrooms (bedrooms). The following Excel output shows the partially completed regression output from a random sample of homes that have recently sold. Identify the dependent and independent variables. Identify the significant independent variables. Explain the next steps you would take with this model. As we can see, the degree of freedom is 14, and here are 3 coeficients are estimated which are Age, Living area, and Bed Room. Now we can know that: N = 17 What is more, we can get the equation that is Y = 108597 – 581X1 + 87X2 + 31262X3. Additionally, the age given value is 10, the living area given value is 2400, and the bedrooms given value is 4. Eventually, the predicted Y is 436226. Because the confidence internal is not included zero, the evidence can be provided that there is a relationship between number of bedrooms in house and the selling price of the house. #### You've reached the end of your free preview.

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• Fall '12
• Donnelly
• • • 