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**MBA 6300 Case Study No.1 | Complete Solution**

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**MBA 6300 Case 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, 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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**MBA 6300 Case Study No.1 | Complete Solution**

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