Apply: Regression, Use the data to construct a model, Construct a multiple regression model.

5. Apply: Regression 

Assignment Content

Purpose 

This assignment provides an opportunity to develop, evaluate, and apply bivariate and multivariate linear regression models.

Resources: Microsoft Excel®, DAT565_v3_Wk5_Data_File (Attached)

Instructions: 

The Excel file for this assignment contains a database with information about the tax assessment value assigned to medical office buildings in a city. The following is a list of the variables in the database:

• FloorArea:       square feet of floor space


• Offices:       number of offices in the building


• Entrances:       number of customer entrances


• Age:       age of the building (years)


• AssessedValue:       tax assessment value (thousands of dollars)

Use the data to construct a model that predicts the tax assessment value assigned to medical office buildings with specific characteristics.

• Construct a scatter plot in Excel with FloorArea as the independent variable and AssessmentValue as the dependent variable. Insert the bivariate linear regression equation and r^2 in your graph. Do you observe a linear relationship between the 2 variables?


• Use Excel’s Analysis ToolPak to conduct a regression analysis of FloorArea and AssessmentValue. Is FloorArea a significant predictor of AssessmentValue?


• Construct a scatter plot in Excel with Age as the independent variable and AssessmentValue as the dependent variable. Insert the bivariate linear regression equation and r^2 in your graph. Do you observe a linear relationship between the 2 variables?


• Use Excel’s Analysis ToolPak to conduct a regression analysis of Age and Assessment Value. Is Age a significant predictor of AssessmentValue?

Construct a multiple regression model.

• Use Excel’s Analysis ToolPak to conduct a regression analysis with AssessmentValue as the dependent variable and FloorArea, Offices, Entrances, and Age as independent variables. What is the overall fit r^2? What is the adjusted r^2?


• Which predictors are considered significant if we work with α=0.05? Which predictors can be eliminated?


• What is the final model if we only use FloorArea and Offices as predictors?


• Suppose our final model is:


• AssessedValue = 115.9 + 0.26 x FloorArea + 78.34 x Offices

What would be the assessed value of a medical office building with a floor area of 3500 sq. ft., 2 offices, that was built 15 years ago? Is this assessed value consistent with what appears in the database?