Regression Model

 

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:

   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.

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   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?

2

>Regression Modeling Data

AssessedValue (

000)

790

4 2

796

2

44

4 2 2

4 2

68

3 2

67

00

4 2

2 1 19

2 1

4 2

74

70

2 1 4

3 2 15

2 1 31

3 2 42

1 2 35

2 1 17 678

30

2 2 5

2 1 13

2 2 45

1280 1 1 45

00

3 1

2 2 41

3660 2 2 33

1 2 50

2 2 39

1 1 20

4 3 17

2 2

3 1 5

2 2 15

3 2 27

2 1 8

3 2 17

FloorArea (Sq.Ft.)	Offices	Entrances	Age
							4		8																1
47	20									3	12		1		5
5940	2094
5720	34		19
	3660	38		15
	50		31	1878
2990	949
2610	48	910
5650		42				17
	35	1187
2930	11	13
	1280	671
4880	1	678
1620	710
1820
		45	1585
2570	842
4690	15	39
4	33
	41		27	1268
3530	1251
1094
1110	638
2670	999
1100	653
5810	1914
2560	24	772
2340	890
3690	1282
3580	1264
3610	1162
3960	1447