PurposeThis assignment provides an opportunity to develop, evaluate, and apply bivariate and multivariate linear regression models.Resources:Microsoft Excel® DAT5/65 Week 5 Data FileInstructions: The...

Purpose

This assignment provides an opportunity to develop, evaluate, and apply bivariate and multivariate linear regression models.

Resources:
Microsoft Excel® DAT5/65 Week 5 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?