# There are numerous variables that are believed to be predictors of housing sale prices, including age, living area (square feet), number of bedrooms,…

There are numerous variables that are believed to be predictors of housing sale prices, including age, living area (square feet), number of bedrooms, and number of bathrooms. The data in the Case Study No. 1 Data.xlsx file pertains to a random sample of houses located in a particular geographic area.

1. Develop a simple linear regression model to predict the price of a house based upon its age using a 90% level of confidence.

a. Write the reqression equation.

b. Discuss the joint and individual statistical significance of the model using the appropriate regression statistic at a 90% and 95% level of confidence.

c. Interpret the coefficient for the independent variable.

d. What percentage of the observed variation in housing sale prices is explained by the model?

e. Predict the value of a house that is 35 years old.

2. Develop a simple linear regression model to predict the price of a house based upon the living area (square feet) using a 90% level of confidence.

a. Write the reqression equation.

b. Discuss the joint and individual statistical significance of the model using the appropriate regression statistic at a 90% and 95% level of confidence.

c. Interpret the coefficient for the independent variable.

d. What percentage of the observed variation in housing sale prices is explained by the model?

e. Predict the value of a house with 2,500 square feet of living area.

3. Develop a simple linear regression model to predict the price of a house based upon the number of bedrooms using a 90% level of confidence.

a. Write the reqression equation.

b. Discuss the joint and individual statistical significance of the model using the appropriate regression statistic at a 90% and 95% level of confidence.

c. Interpret the coefficient for the independent variable.

d. What percentage of the observed variation in housing sale prices is explained by the model?

e. Predict the value of a house with 3 bedrooms.

4. Develop a simple linear regression model to predict the price of a house based upon the number of bathrooms using a 90% level of confidence.

a. Write the reqression equation.

b. Discuss the joint and individual statistical significance of the model using the appropriate regression statistic at a 90% and 95% level of confidence.

c. Interpret the coefficient for the independent variable.

d. What percentage of the observed variation in housing sale prices is explained by the model?

e. Predict the value of a house with 1.5 bathrooms.

5. Compare the four simple linear regression models and select the preferred model. Explain your selection using the appropriate regression statistics.

Prepare a single Microsoft Excel file, using a separate worksheet for each regression model, to document your regression analyses. Prepare a single Microsoft Word document that outlines your responses for each of the preceding portions of the case study. Upload your Excel and Word files for grading