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Using Regression To Analyze Business Term Paper

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

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

9.664

.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.05). The coefficients for the independent variables are as follows: Age, ? = .318; Days per month, ? =.320; Cups of Coffee per day, ? = -.530; Income ($1,000) ? = .223; Gender, ? = -.110. Again, in this model, the number of cups of coffee per day is significantly predicted the amount of money on the prepaid Starbucks cards purchased by the customers (? = -.530, p

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