**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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