MBA 6300Case Study No.1 | Complete Solution

MBA 6300Case Study No.1

There are numerous variables that are believed to be predictors of housing prices,including living area (square feet), number of bedrooms, number of bathrooms, andage.The information in theMBA 6300 Case Study.xlsxfile pertains to a randomsample of houses located in the greater Wilmington, Delaware area.1.Develop a simple linear regression model to predict the price of a house based uponthe living area (square feet)using a 95% level of confidence.a.Write the reqression equationb.Discuss the statistical significance of the model as a whole using theappropriate regression statistic at a 95% level of confidence.c.Discuss the statistical significance of the coefficient for the independentvariable using the appropriate regression statistic at a 95% level ofconfidence.d.Interpret the coefficient for the independent variable.e.What percentage of the observed variation in housing prices is explained bythe 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 uponthe number of bedroomsusing a 95% level of confidence.a.Write the reqression equationb.Discuss the statistical significance of the model as a whole using theappropriate regression statistic at a 95% level of confidence.c.Discuss the statistical significance of the coefficient for the independentvariable using the appropriate regression statistic at a 95% level ofconfidence.d.Interpret the coefficient for the independent variable.e.What percentage of the observed variation in housing prices is explained bythe 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 uponthe number of bathroomsusing a 95% level of confidence.a.Write the reqression equationb.Discuss the statistical significance of the model as a whole using theappropriate regression statistic at a 95% level of confidence.c.Discuss the statistical significance of the coefficient for the independentvariable using the appropriate regression statistic at a 95% level ofconfidence.d.Interpret the coefficient for the independent variable.e.What percentage of the observed variation in housing prices is explained bythe model?f.Predict the value of a house with 2.5 bathrooms.

4.Develop a simple linear regression model to predict the price of a house based uponits ageusing a 95% level of confidence.a.Write the reqression equationb.Discuss the statistical significance of the model as a whole using theappropriate regression statistic at a 95% level of confidence.c.Discuss the statistical significance of the coefficient for the independentvariable using the appropriate regression statistic at a 95% level ofconfidence.d.Interpret the coefficient for the independent variable.e.What percentage of the observed variation in housing prices is explained bythe model?f.Predict the value of a house that is 50 years old.

5.Compare the preceding four simple linear regression models to determine whichmodel is the preferred model.Use the Significance F values, p-values forindependent variable coefficients, R-squared or Adjusted R-squared values (asappropriate), and standard errors to explain your selection.

6.Calculate a 95% prediction intervalestimate for the price of a 50 year old house with3,000 square feet of living area, 3 bedrooms, and 2.5 bathrooms using yourpreferred regression model from part 5.Prepare a single Microsoft Excel file, using a separate worksheet for each regressionmodel, to document your regression analyses.Prepare a single Microsoft Worddocument that outlines your responses for each portions of the case study.Upload yourExcel and Word files for grading via the Blackboard submission link.