Wk 5 – Apply: Regression Modeling.

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

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Assignment Content

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**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.