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

by | Jun 6, 2022 | Data Analytics | 0 comments

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Assignment Content
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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