Use the data to construct a model that predicts the tax assessment | dat 565 | Apollo Education Group, Inc.

  

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?

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