DAT 565 Wk 5 - Apply: Regression Modeling | eBooks | Education

DAT 565 Wk 5 - Apply: Regression Modeling

DAT 565 Wk 5 - Apply: Regression Modeling PLDZ-13316
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DAT 565 Wk 5 – Apply: Regression Modeling

 

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 FloorAreaOfficesEntrances, 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?

 

 

 

 

Submit your assignment.

 

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