Wk. 5 – Apply: Regression Modeling.

Wk 5 – Apply: Regression Modeling.

Wk 5 – Apply: Regression Modeling [due Day 7]

Top of Form

Bottom of Form

Assignment Content

Top of Form

Purpose 

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

Resources: 

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:

• Floor Area: square feet of floor space
• Offices: number of offices in the building
• Entrances: number of customer entrances
• Age: age of the building (years)
• Assessed Value: 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 Floor Area as the independent variable and Assessment Value 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 Tool Pak to conduct a regression analysis of Floor Area and Assessment Value. Is Floor Area a significant predictor of Assessment Value?
• Construct a scatter plot in Excel with Age as the independent variable and Assessment Value as the dependent variable. Insert the bivariate linear regression equation and Assessment Value in your graph. Do you observe a linear relationship between the 2 variables?
• Use Excel’s Analysis Tool Pak to conduct a regression analysis of Age and Assessment Value. Is Age a significant predictor of Assessment Value?

Construct a multiple regression model.

• Use Excel’s Analysis Tool Pak to conduct a regression analysis with Assessment Value as the dependent variable and Floor Area, Offices, Entrances, and Age as independent variables. What is the overall fit Assessment Value? What is the adjusted Assessment Value?
• 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 Floor Area and Offices as predictors?
• Suppose our final model is:
• Assessed Value = 115.9 + 0.26 x Floor Area + 78.34 x Offices
• What would be 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?

Submit your assignment.