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

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

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 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?