Apply: Regression Modeling This assignment provides an opportunity to develop, evaluate, and apply bivariate and multivariate linear regression model

Apply: Regression Modeling
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)

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Apply: Regression Modeling This assignment provides an opportunity to develop, evaluate, and apply bivariate and multivariate linear regression model
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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 Excels 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 Excels 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 Excels 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 =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?

Regression Modeling Data

FloorArea (Sq.Ft.) Offices Entrances Age AssessedValue ($’000)

4790 4 2 8 1796

4720 3 2 12 1544

5940 4 2 2 2094

5720 4 2 34 1968

3660 3 2 38 1567

5000 4 2 31 1878

2990 2 1 19 949

2610 2 1 48 910

5650 4 2 42 1774

3570 2 1 4 1187

2930 3 2 15 1113

1280 2 1 31 671

4880 3 2 42 1678

1620 1 2 35 710

1820 2 1 17 678

4530 2 2 5 1585

2570 2 1 13 842

4690 2 2 45 1539

1280 1 1 45 433

4100 3 1 27 1268

3530 2 2 41 1251

3660 2 2 33 1094

1110 1 2 50 638

2670 2 2 39 999

1100 1 1 20 653

5810 4 3 17 1914

2560 2 2 24 772

2340 3 1 5 890

3690 2 2 15 1282

3580 3 2 27 1264

3610 2 1 8 1162

3960 3 2 17 1447

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