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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 FloorAreaOfficesEntrances, 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?
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 FloorAreaOfficesEntrances, 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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