# DAT 565 Week 5 Assignment Apply Regression Modelling

Wk 5 - Apply: Regression Modeling [due Mon]

**Assignment Content**

1.

**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:

o *FloorArea*: square feet of floor space

o *Offices*: number of offices in the building

o *Entrances*: number of customer entrances

o *Age*: age of the building (years)

o *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.

o 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?

o Use Excel’s Analysis ToolPak to conduct a regression analysis of *FloorArea* and *AssessmentValue*. Is* FloorArea* a significant predictor of *AssessmentValue*?

o 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?

o 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.

o 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?

o Which predictors are considered significant if we work with α=0.05? Which predictors can be eliminated?

o What is the final model if we only use *FloorArea* and Offices as predictors?

o Suppose our final model is:

o *AssessedValue* = 115.9 + 0.26 x *FloorArea* + 78.34 x *Offices*

o 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?

**Submit **your assignment.

Purchase the answer to view it