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?

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 wouldbe 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?

2

>Regression Modeling Data

AssessedValue (

000)

790

4 2

796

2

44

4 2 2

4 2

68

3 2

67

00

4 2

2 1 19

2 1

4 2

74

70

2 1 4

3 2 15

2 1 31

3 2 42

1 2 35

2 1 17 678

30

2 2 5

2 1 13

2 2 45

00

3 1

2 2 41

1 2 50

2 2 39

1 1 20

4 3 17

2 2

3 1 5

2 2 15

3 2 27

2 1 8

3 2 17