Description
The attached Excel file 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)
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 two variables?
Use Excels Analysis ToolPak to conduct a regression analysis of FloorArea and AssessmentValue. Is FloorArea a significant predictor of AssessmentValue?
Construct a scatter plot in Excel 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 Excels Analysis ToolPak to conduct a regression analysis of Age and AssessmentValue. Is Age a significant predictor of AssessmentValue?
Construct a multiple regression model.
- Use Excels 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 ?=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 * FloorArea + 78.34 * 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?
