Regression Modeling
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)
Assessed Value: 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 Floor Area as the independent variable and Assessment Value 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 Floor Area and Assessment Value. Is Floor Area a significant predictor of Assessment Value?
Construct a scatter plot in Excel with Age as the independent variable and Assessment Value 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 Assessment Value?
Construct a multiple regression model.
Use Excel’s Analysis ToolPak to conduct a regression analysis with Assessment Value as the dependent variable and Floor Area, 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 Floor Area and Offices as predictors?
Suppose our final model is:
Assessed Value = 115.9 + 0.26 x Floor Area + 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?
