Business Statistics
Regression Dependent Variable: Amount of Prepaid Card $
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
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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