There are numerous variables that are believed to be predictors of housing prices,
including living area (square feet), number of bedrooms, number of bathrooms, and
age. The information in the MBA 6300 Case Study.xlsx file pertains to a random
sample of houses located in the greater Wilmington, Delaware area.
1. Develop a simple linear regression model to predict the price of a house based upon
the living area (square feet) using a 95% level of confidence.
a. Write the reqression equation
b. Discuss the statistical significance of the model as a whole using the
appropriate regression statistic at a 95% level of confidence.
c. Discuss the statistical significance of the coefficient for the independent
variable using the appropriate regression statistic at a 95% level of
confidence.
d. Interpret the coefficient for the independent variable.
e. What percentage of the observed variation in housing prices is explained by
the model?
f. Predict the value of a house with 3,000 square feet of living area.
2. Develop a simple linear regression model to predict the price of a house based upon
the number of bedrooms using a 95% level of confidence.
a. Write the reqression equation
b. Discuss the statistical significance of the model as a whole using the
appropriate regression statistic at a 95% level of confidence.
c. Discuss the statistical significance of the coefficient for the independent
variable using the appropriate regression statistic at a 95% level of
confidence.
d. Interpret the coefficient for the independent variable.
e. What percentage of the observed variation in housing prices is explained by
the model?
f. Predict the value of a house with 3 bedrooms.
3. Develop a simple linear regression model to predict the price of a house based upon
the number of bathrooms using a 95% level of confidence.
a. Write the reqression equation
b. Discuss the statistical significance of the model as a whole using the
appropriate regression statistic at a 95% level of confidence.
c. Discuss the statistical significance of the coefficient for the independent
variable using the appropriate regression statistic at a 95% level of
confidence.
d. Interpret the coefficient for the independent variable.
e. What percentage of the observed variation in housing prices is explained by
the model?
f. Predict the value of a house with 2.5 bathrooms.
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4. Develop a simple linear regression model to predict the price of a house based upon
its age using a 95% level of confidence.
a. Write the reqression equation
b. Discuss the statistical significance of the model as a whole using the
appropriate regression statistic at a 95% level of confidence.
c. Discuss the statistical significance of the coefficient for the independent
variable using the appropriate regression statistic at a 95% level of
confidence.
d. Interpret the coefficient for the independent variable.
e. What percentage of the observed variation in housing prices is explained by
the model?
f. Predict the value of a house that is 50 years old.
5. Compare the preceding four simple linear regression models to determine which
model is the preferred model. Use the Significance F values, p-values for
independent variable coefficients, R-squared or Adjusted R-squared values (as
appropriate), and standard errors to explain your selection.
6. Calculate a 95% prediction interval estimate for the price of a 50 year old house with
3,000 square feet of living area, 3 bedrooms, and 2.5 bathrooms using your
preferred regression model from part 5.
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 portions of the case study. Upload your
Excel and Word files for grading via the Blackboard submission link.
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