MBA 6300 Case Study No. 2 There are numerous variables that are believed to be predictors of housing prices, including living area (square feet), number of bedrooms, and number of bathrooms. The da

MBA 6300 Case Study No. 2

There are numerous variables that are believed to be predictors of housing prices, including living area (square feet), number of bedrooms, and number of bathrooms.  The data in the Case Study No. 2.xlsx file pertains to a random sample of houses located in a particular geographic area.

1.    Develop the following simple linear regression models to predict the sale price of a house based upon a 90% level of confidence.  Write the regression equation for each model.

a.    Sale price based upon square feet of living area.

b.    Sale price based upon number of bedrooms.

c.    Sale price based upon number of bathrooms.

2.    Develop the following multiple linear regression models to predict the sale price of a house based upon a 90% level of confidence.  Write the regression equation for each model.

a.    Sale price based upon square feet of living area and number of bedrooms.

b.    Sale price based upon square feet of living area and number of bathrooms.

c.    Sale price based upon number of bedrooms and number of bathrooms.

d.    Sale price based upon square feet of living area, number of bedrooms, and number of bathrooms.

3.    Discuss the joint statistical significance of each of the preceding simple and multiple linear regression models at a 90% level of confidence and 95% level of confidence.

4.    Discuss the individual statistical significance of the coefficient for each independent variable for each of the preceding simple and multiple linear regression models at a 90% level of confidence and 95% level of confidence.

5.    Compare any of the preceding simple and multiple linear regression models that were found to be jointly and individually statistically significant at a 90% level of confidence and select the preferred regression model. Explain your selection using the appropriate regression statistics.

6.    Interpret the coefficient for each independent variable (or variables) associated with your selected preferred regression model.

7.    Using the preferred regression model, predict the sale price of a house with the following values for the independent variables: 3,000 square feet of living area, 3 bedrooms, and 2.5 bathrooms.  (Hint: You should only use the values for those independent variables that are specifically associated with your selected preferred regression model.)

Prepare a single Microsoft Excel file using a separate worksheet for each question.