Using Regression to Analyze Business Term Paper

Open, View, and/or
Download this Document

Total Length: 1732 words ( 6 double-spaced pages)

Total Sources: 0

Page 1 of 6

Business Statistics

Regression

Variables Entered/Removeda

Model

Variables Entered

Variables Removed

Income ($1,000), Cups of Coffee per Day, Age, Days per Month at Starbucksb

Dependent Variable: Amount of Prepaid Card $

All requested variables entered.

Model Summaryb

Model

R

R Square

Error of the Estimate

Durbin-Watson

Predictors: (Constant), Income ($1,000), Cups of Coffee per Day, Age, Days per Month at Starbucks

Dependent Variable: Amount of Prepaid Card $

ANOVAa

Model

Sum of Squares

df

Mean Square

F

Sig.

Regression

Residual

Total

Dependent Variable: Amount of Prepaid Card $

Predictors: (Constant), Income ($1,000), Cups of Coffee per Day, Age, Days per Month at Starbucks

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

Collinearity Statistics

B

Std. Error

Beta

Tolerance

VIF

1

(Constant)

10.949

10.562

1.037

.312

Age

.415

.270

.313

1.535

.140

.873

1.146

Days per Month at Starbucks

1.005

.692

.362

1.452

.162

.584

1.712

Cups of Coffee per Day

-2.590

1.235

-.520

-2.096

.049

.590

1.696

Income ($1,000)

.166

.169

.201

.984

.337

.873

1.146

a. Dependent Variable: Amount of Prepaid Card $

Collinearity Diagnosticsa

Model

Dimension

Eigenvalue

Condition Index

Variance Proportions

(Constant)

Age

Days per Month at Starbucks

Cups of Coffee per Day

Income ($1,000)

1

1

4.683

1.000

.00

.00

.00

.00

.00

2

.152

5.546

.02

.03

.03

.43

.13

3

.084

7.451

.03

.23

.16

.03

.39

4

.056

9.109

.08

.00

.51

.40

.47

5

.024

14.014

.87

.74

.28

.14

.00

a. Dependent Variable: Amount of Prepaid Card $

Residuals Statisticsa

Minimum

Maximum

Mean

Std. Deviation

N

Predicted Value

17.48

42.97

29.96

5.823

25

Residual

-21.080

19.592

.000

9.494

25

Std. Predicted Value

-2.144

2.235

.000

1.000

25

Std. Residual

-2.027

1.884

.000

.913

25

a. Dependent Variable: Amount of Prepaid Card $

Regression

Variables Entered/Removeda

Model

Variables Entered

Variables Removed

Method

1

Gender, Age, Income ($1,000), Cups of Coffee per Day, Days per Month at Starbucksb

Enter

a. Dependent Variable: Amount of Prepaid Card $

b. All requested variables entered.

Model Summaryb

Model

R

R Square

Adjusted R. Square

Std. Error of the Estimate

Durbin-Watson

1

.532a

.283

.095

10.598

1.655

a. Predictors: (Constant), Gender, Age, Income ($1,000), Cups of Coffee per Day, Days per Month at Starbucks

b. Dependent Variable: Amount of Prepaid Card $

ANOVAa

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

5

1.501

.236b

Residual

19

Total

24

a. Dependent Variable: Amount of Prepaid Card $

b. Predictors: (Constant), Gender, Age, Income ($1,000), Cups of Coffee per Day, Days per Month at Starbucks

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

Collinearity Statistics

B

Std. Error

Beta

Tolerance

VIF

1

(Constant)

12.669

11.280

1.123

.275

Age

.421

.276

.318

1.528

.143

.871

1.148

Days per Month at Starbucks

.888

.741

.320

1.198

.246

.529

1.891

Cups of Coffee per Day

-2.636

1.262

-.530

-2.089

.050

.587

1.704

Income ($1,000)

.185

.176

.223

1.050

.307

.836

1.197

Gender

-2.393

4.692

-.110

-.510

.616

.818

1.223

a. Dependent Variable: Amount of Prepaid Card $

Collinearity Diagnosticsa

Model

Dimension

Eigenvalue

Condition Index

Variance Proportions

(Constant)

Age

Days per Month at Starbucks

Cups of Coffee per Day

Income ($1,000)

Gender

1

1

5.158

1.000

.00

.00

.00

.00

.00

.01

2

.566

3.020

.00

.00

.01

.02

.00

.61

3

.118

6.602

.01

.01

.00

.54

.25

.21

4

.080

8.045

.05

.31

.12

.01

.29 .08

5

.055

9.

Stuck Writing Your "Using Regression to Analyze Business" Term Paper?

View All Our Example Using Regression to Analyze Business Term Paper

Have A Custom Example Essay Written

664

.06

.02

.52

.31

.46

.03

6

.023

15.093

.88

.65

.35

.12

.00

.06

a. Dependent Variable: Amount of Prepaid Card $

Residuals Statisticsa

Minimum

Maximum

Mean

Std. Deviation

N

Predicted Value

18.19

44.45

29.96

5.927

25

Residual

-20.423

20.625

.000

9.429

25

Std. Predicted Value

-1.986

2.444

.000

1.000

25

Std. Residual

-1.927

1.946

.000

.890

25

a. Dependent Variable: Amount of Prepaid Card $

1. Starbucks Debit Card

Multiple regression was used to explore how well the amount of the prepaid card can be predicted by other variables, and which variables show the most promise for generating a prediction. The results of the regression indicated that the four predictors explained only .27 of the variance (R2 = .27, F = 1.881, p >.05). The coefficients for the independent variables are as follows: Age, ? = .313; Days per month, ? =.362; Cups of Coffee per day, ? = -.520; Income ($1,000) ? = .201. Of these, the number of cups of coffee per day is significantly predicted the amount of money on the prepaid Starbucks cards purchased by the customers (? = -.520, p

Show More ⇣

     Open the full completed essay and source list


OR

     Order a one-of-a-kind custom essay on this topic


sample essay writing service

Cite This Resource:

Latest APA Format (6th edition)

Copy Reference
"Using Regression To Analyze Business" (2014, December 07) Retrieved June 5, 2026, from
https://www.aceyourpaper.com/essays/using-regression-analyze-business-2154304

Latest MLA Format (8th edition)

Copy Reference
"Using Regression To Analyze Business" 07 December 2014. Web.5 June. 2026. <
https://www.aceyourpaper.com/essays/using-regression-analyze-business-2154304>

Latest Chicago Format (16th edition)

Copy Reference
"Using Regression To Analyze Business", 07 December 2014, Accessed.5 June. 2026,
https://www.aceyourpaper.com/essays/using-regression-analyze-business-2154304
   

Open, View, and/or
Download this Document
order custom essay example