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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)

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Purpose? This assignment provides an opportunity to develop, evaluate, and apply bivariate and multivariate linear regression models. Resources:?Micr
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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 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 a=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?

DAT565_v3_Wk5_Data_File.xlsx

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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