Exercise 2 – Cluster and Multiple Regression
1.
Run a 2 Group cluster analysis using X22 – Purchase Level.
We identify a single solution with two new cluster using X22- Purchase Level using Hierarchical Cluster analysis in SPSS. Word’s method was selected in this case because of its tendency to generate clusters that are homogeneous and relatively equal in size (Hair, Black, Babin, and Anderson (2018). Then we run Compare Means to determine the level of purchase and recoded and labeled the new group to (low purchase- high purchase) in SPSS.
Case processing Summary table describes the sample number (N=100) and cases missing (0) in this model. The agglomeration schedule below is a numerical summary of the cluster solution and shows each cluster combined in a specific step. It helps to identify at what point two clusters being combined are considered too different to form a homogeneous group. In this case, it suspected the first two pairs to get joined in the cluster analysis procedure are 37 and 100. The Coefficients value represent the distance (or similarity) statistic used to form the cluster.
Agglomeration Schedule
|
Stage
|
Cluster Combined
|
Coefficients
|
Stage Cluster First Appears
|
Next Stage
|
Cluster 1
|
Cluster 2
|
Cluster 1
|
Cluster 2
|
1
|
37
|
100
|
.000
|
0
|
0
|
54
|
2
|
78
|
99
|
.000
|
0
|
0
|
22
|
3
|
92
|
98
|
.000
|
0
|
0
|
9
|
4
|
87
|
97
|
.000
|
0
|
0
|
14
|
5
|
67
|
96
|
.000
|
0
|
0
|
31
|
6
|
90
|
95
|
.000
|
0
|
0
|
11
|
7
|
93
|
94
|
.000
|
0
|
0
|
8
|
8
|
3
|
93
|
.000
|
0
|
7
|
50
|
9
|
32
|
92
|
.000
|
0
|
3
|
29
|
10
|
70
|
91
|
.000
|
0
|
0
|
28
|
11
|
10
|
90
|
.000
|
0
|
6
|
39
|
12
|
56
|
89
|
.000
|
0
|
0
|
39
|
13
|
85
|
88
|
.000
|
0
|
0
|
16
|
14
|
51
|
87
|
.000
|
0
|
4
|
33
|
15
|
69
|
86
|
.000
|
0
|
0
|
29
|
16
|
30
|
85
|
.000
|
0
|
13
|
81
|
17
|
40
|
83
|
.000
|
0
|
0
|
52
|
18
|
11
|
82
|
.000
|
0
|
0
|
78
|
19
|
76
|
81
|
.000
|
0
|
0
|
23
|
20
|
63
|
80
|
.000
|
0
|
0
|
34
|
21
|
43
|
79
|
.000
|
0
|
0
|
50
|
22
|
41
|
78
|
.000
|
0
|
2
|
43
|
23
|
62
|
76
|
.000
|
0
|
19
|
77
|
24
|
45
|
75
|
.000
|
0
|
0
|
48
|
25
|
73
|
74
|
.000
|
0
|
0
|
26
|
26
|
53
|
73
|
.000
|
0
|
25
|
79
|
27
|
59
|
71
|
.000
|
0
|
0
|
37
|
28
|
13
|
70
|
.000
|
0
|
10
|
47
|
29
|
32
|
69
|
.000
|
9
|
15
|
55
|
30
|
65
|
68
|
.000
|
0
|
0
|
33
|
31
|
6
|
67
|
.000
|
0
|
5
|
67
|
32
|
48
|
66
|
.000
|
0
|
0
|
45
|
33
|
51
|
65
|
.000
|
14
|
30
|
74
|
34
|
31
|
63
|
.000
|
0
|
20
|
57
|
35
|
16
|
61
|
.000
|
0
|
0
|
75
|
36
|
50
|
60
|
.000
|
0
|
0
|
43
|
37
|
2
|
59
|
.000
|
0
|
27
|
44
|
38
|
46
|
58
|
.000
|
0
|
0
|
47
|
39
|
10
|
56
|
.000
|
11
|
12
|
80
|
40
|
34
|
55
|
.000
|
0
|
0
|
56
|
41
|
33
|
54
|
.000
|
0
|
0
|
57
|
42
|
49
|
52
|
.000
|
0
|
0
|
44
|
43
|
41
|
50
|
.000
|
22
|
36
|
51
|
44
|
2
|
49
|
.000
|
37
|
42
|
65
|
45
|
23
|
48
|
.000
|
0
|
32
|
80
|
46
|
20
|
47
|
.000
|
0
|
0
|
65
|
47
|
13
|
46
|
.000
|
28
|
38
|
66
|
48
|
1
|
45
|
.000
|
0
|
24
|
63
|
49
|
28
|
44
|
.000
|
0
|
0
|
59
|
50
|
3
|
43
|
.000
|
8
|
21
|
68
|
51
|
41
|
42
|
.000
|
43
|
0
|
84
|
52
|
12
|
40
|
.000
|
0
|
17
|
71
|
53
|
24
|
39
|
.000
|
0
|
0
|
63
|
54
|
5
|
37
|
.000
|
0
|
1
|
76
|
55
|
32
|
36
|
.000
|
29
|
0
|
81
|
56
|
15
|
34
|
.000
|
0
|
40
|
77
|
57
|
31
|
33
|
.000
|
34
|
41
|
73
|
58
|
27
|
29
|
.000
|
0
|
0
|
60
|
59
|
19
|
28
|
.000
|
0
|
49
|
62
|
60
|
22
|
27
|
.000
|
0
|
58
|
75
|
61
|
18
|
26
|
.000
|
0
|
0
|
66
|
62
|
19
|
25
|
.000
|
59
|
0
|
83
|
63
|
1
|
24
|
.000
|
48
|
53
|
79
|
64
|
17
|
21
|
.000
|
0
|
0
|
67
|
65
|
2
|
20
|
.000
|
44
|
46
|
78
|
66
|
13
|
18
|
.000
|
47
|
61
|
85
|
67
|
6
|
17
|
.000
|
31
|
64
|
72
|
68
|
3
|
14
|
.000
|
50
|
0
|
82
|
69
|
8
|
9
|
.000
|
0
|
0
|
76
|
70
|
4
|
7
|
.500
|
0
|
0
|
87
|
71
|
12
|
35
|
1.250
|
52
|
0
|
90
|
72
|
6
|
77
|
2.083
|
67
|
0
|
86
|
73
|
31
|
72
|
2.917
|
57
|
0
|
93
|
74
|
51
|
64
|
3.750
|
33
|
0
|
92
|
75
|
16
|
22
|
4.950
|
35
|
60
|
88
|
76
|
5
|
8
|
6.150
|
54
|
69
|
83
|
77
|
15
|
62
|
7.650
|
56
|
23
|
84
|
78
|
2
|
11
|
9.206
|
65
|
18
|
89
|
79
|
1
|
53
|
11.081
|
63
|
26
|
89
|
80
|
10
|
23
|
12.956
|
39
|
45
|
85
|
81
|
30
|
32
|
14.956
|
16
|
55
|
86
|
82
|
3
|
57
|
18.384
|
68
|
0
|
88
|
83
|
5
|
19
|
24.073
|
76
|
62
|
90
|
84
|
15
|
41
|
30.823
|
77
|
51
|
94
|
85
|
10
|
13
|
37.881
|
80
|
66
|
94
|
86
|
6
|
30
|
45.981
|
72
|
81
|
92
|
87
|
4
|
84
|
54.148
|
70
|
0
|
93
|
88
|
3
|
16
|
64.519
|
82
|
75
|
91
|
89
|
1
|
2
|
78.971
|
79
|
78
|
96
|
90
|
5
|
12
|
98.255
|
83
|
71
|
95
|
91
|
3
|
38
|
126.179
|
88
|
0
|
96
|
92
|
6
|
51
|
162.221
|
86
|
74
|
95
|
93
|
4
|
31
|
202.721
|
87
|
73
|
97
|
94
|
10
|
15
|
263.390
|
85
|
84
|
98
|
95
|
5
|
6
|
478.187
|
90
|
92
|
97
|
96
|
1
|
3
|
706.748
|
89
|
91
|
98
|
|
4
|
5
|
1355.637
|
93
|
95
|
99
|
98
|
1
|
10
|
2429.401
|
96
|
94
|
99
|
99
|
1
|
4
|
7773.000
|
98
|
97
|
0
|
From the descriptive table below, we compare the number of cluster 1 and cluster 2. It tells us that the sample had more cluster 1 (n = 57) than cluster 2 (n = 43). We compare the mean of cluster 1 and cluster 2, too. The mean of cluster 1 was (64.749) and the mean of cluster 2 was (49.984), which was about14.765 cluster 1 higher than cluster 2.
Descriptives
|
X22 - Purchase Level
|
|
N
|
Mean
|
Std. Deviation
|
Std. Error
|
95% Confidence Interval for Mean
|
Minimum
|
Maximum
|
Lower Bound
|
Upper Bound
|
1
|
57
|
64.749
|
5.0727
|
.6719
|
63.403
|
66.095
|
58.1
|
77.1
|
2
|
43
|
49.984
|
4.8512
|
.7398
|
48.491
|
51.477
|
37.1
|
57.1
|
Total
|
100
|
58.400
|
8.8609
|
.8861
|
56.642
|
60.158
|
37.1
|
77.1
|
The ANOVA table below indicates which variables contribute the most to the cluster solution. In this case where one case solution with two clusters, we found F values provide the greatest separation between the two clusters (215.556) and statistically significant at (.000).
ANOVA
|
X22 - Purchase Level
|
|
Sum of Squares
|
df
|
Mean Square
|
F
|
Sig.
|
Between Groups
|
5343.599
|
1
|
5343.599
|
215.556
|
.000
|
Within Groups
|
2429.401
|
98
|
24.790
|
|
|
Total
|
7773.000
|
99
|
|
|
|
# Post hoc tests are not performed for X22 - Purchase Level because there are fewer than three groups.
Then, we recode 2 groups based on mean of each. The cluster with higher mean = High Purchase, we recode it 1. In this case cluster group 1 is higher (mean=64.749). recode it 1=1. The second cluster group (mean=49.984), recode it 2=0 (Lower Purchase).
- Using Recode option to recode the groups:
click Transform -à Record into different variables--à move Clu2-1 from left to right and name (Purchase 1) label (Purchase 1) ---à add ---à OK.
After recoding the two groups, we go to the Variable View in the SPSS file and look for the Values column. Using the Values column, click on None beside the newly created (recoded) variable and add labels so we identify the Higher Purchase and Lower Purchase groups.
Then we run multiple regression models using the outputs from the first section.
2.
Use the newly created two group purchase level variable (after recoding) to Select Cases so you can run two separate multiple regression models. Remember, the Select Cases option is under the Data Tab.
We run two separate multiple regression models.
Case 1 (Higher Purchase):
To select cases: click Data-à Select Cases -à If-à move Purchase 1 to left and add =0.
Then, we run regression Dependent variable (Loyalty) = sum of X19, X20, X21, and IVs are the Four Factors.
Variables Entered/Removeda
|
Model
|
Variables Entered
|
Variables Removed
|
Method
|
1
|
Product_Value, Marketing, Technical_Support, Customer_Servicesb
|
.
|
Enter
|
a. Dependent Variable: Loyalty
|
b. All requested variables entered.
|
|
Model
|
R
|
R Square
|
Adjusted R Square
|
Std. Error of the Estimate
|
1
|
.576a
|
.332
|
.280
|
.63596
|
a. Predictors: (Constant), Product_Value, Marketing, Technical_Support, Customer_Services
|
ANOVAa
|
Model
|
Sum of Squares
|
df
|
Mean Square
|
F
|
Sig.
|
1
|
Regression
|
10.432
|
4
|
2.608
|
6.449
|
.000b
|
Residual
|
21.031
|
52
|
.404
|
|
|
Total
|
31.463
|
56
|
|
|
|
a. Dependent Variable: Loyalty
|
b. Predictors: (Constant), Product_Value, Marketing, Technical_Support, Customer_Services
|
Coefficientsa
|
Model
|
Unstandardized Coefficients
|
Standardized Coefficients
|
t
|
Sig.
|
B
|
Std. Error
|
Beta
|
1
|
(Constant)
|
7.395
|
.130
|
|
56.815
|
.000
|
Customer_Services
|
.289
|
.134
|
.294
|
2.153
|
.036
|
Marketing
|
.376
|
.082
|
.555
|
4.564
|
.000
|
Technical_Support
|
.011
|
.095
|
.013
|
.114
|
.910
|
Product_Value
|
.322
|
.105
|
.412
|
3.075
|
.003
|
a. Dependent Variable: Loyalty
|
Case 2 (Lower Purchase):
Variables Entered/Removeda
|
Model
|
Variables Entered
|
Variables Removed
|
Method
|
1
|
Product_Value, Technical_Support, Customer_Services, Marketingb
|
.
|
Enter
|
a. Dependent Variable: Loyalty
|
b. All requested variables entered.
|
Model Summary
|
Model
|
R
|
R Square
|
Adjusted R Square
|
Std. Error of the Estimate
|
1
|
.612a
|
.374
|
.308
|
.62452
|
a. Predictors: (Constant), Product_Value, Technical_Support, Customer_Services, Marketing
|
ANOVAa
|
Model
|
Sum of Squares
|
df
|
Mean Square
|
F
|
Sig.
|
1
|
Regression
|
8.860
|
4
|
2.215
|
5.679
|
.001b
|
Residual
|
14.821
|
38
|
.390
|
|
|
Total
|
23.681
|
42
|
|
|
|
a. Dependent Variable: Loyalty
|
b. Predictors: (Constant), Product_Value, Technical_Support, Customer_Services, Marketing
|
Coefficientsa
|
Model
|
Unstandardized Coefficients
|
Standardized Coefficients
|
t
|
Sig.
|
B
|
Std. Error
|
Beta
|
1
|
(Constant)
|
6.985
|
.149
|
|
46.850
|
.000
|
Customer_Services
|
.395
|
.106
|
.493
|
3.719
|
.001
|
Marketing
|
.126
|
.133
|
.128
|
.951
|
.347
|
Technical_Support
|
.148
|
.090
|
.218
|
1.634
|
.111
|
Product_Value
|
.362
|
.128
|
.389
|
2.832
|
.007
|
a. Dependent Variable: Loyalty
|
3.
Run the separate regression models again. Keep the Four Factors as IVs but this time add variables X1 to X5, and X23 to the regression models as IVs.
Case 1 (Higher Purchase):
First, we run regression model after selecting case 1 (High Purchase) following the same steps as Q2. We add the same DV (Loyalty= Sum score) as in second exercise, but we add more IV. Then we run regression.
First time we run regression using method: Enter.
Variables Entered/Removeda
|
Model
|
Variables Entered
|
Variables Removed
|
Method
|
1
|
X23 - Consider Strategic Alliance, Product_Value, X2 - Industry Type, Marketing, Technical_Support, X5 - Distribution System, X3 - Firm Size, Customer_Services, X1 - Customer Type, X4 - Regionb
|
.
|
Enter
|
a. Dependent Variable: Loyalty
|
b. All requested variables entered.
|
Model Summary
|
Model
|
R
|
R Square
|
Adjusted R Square
|
Std. Error of the Estimate
|
1
|
.896a
|
.803
|
.760
|
.36749
|
a. Predictors: (Constant), X23 - Consider Strategic Alliance, Product_Value, X2 - Industry Type, Marketing, Technical_Support, X5 - Distribution System, X3 - Firm Size, Customer_Services, X1 - Customer Type, X4 - Region
|
ANOVAa
|
Model
|
Sum of Squares
|
df
|
Mean Square
|
F
|
Sig.
|
1
|
Regression
|
25.251
|
10
|
2.525
|
18.698
|
.000b
|
Residual
|
6.212
|
46
|
.135
|
|
|
Total
|
31.463
|
56
|
|
|
|
a. Dependent Variable: Loyalty
|
b. Predictors: (Constant), X23 - Consider Strategic Alliance, Product_Value, X2 - Industry Type, Marketing, Technical_Support, X5 - Distribution System, X3 - Firm Size, Customer_Services, X1 - Customer Type, X4 - Region
|
Coefficientsa
|
Model
|
Unstandardized Coefficients
|
Standardized Coefficients
|
t
|
Sig.
|
B
|
Std. Error
|
Beta
|
1
|
(Constant)
|
5.646
|
.341
|
|
16.548
|
.000
|
Customer_Services
|
.089
|
.086
|
.091
|
1.044
|
.302
|
Marketing
|
.181
|
.057
|
.267
|
3.147
|
.003
|
Technical_Support
|
.146
|
.062
|
.178
|
2.371
|
.022
|
Product_Value
|
.177
|
.090
|
.226
|
1.972
|
.055
|
X1 - Customer Type
|
.328
|
.131
|
.249
|
2.512
|
.016
|
X2 - Industry Type
|
.122
|
.107
|
.081
|
1.133
|
.263
|
X3 - Firm Size
|
.501
|
.144
|
.334
|
3.469
|
.001
|
X4 - Region
|
.287
|
.157
|
.193
|
1.824
|
.075
|
X5 - Distribution System
|
.421
|
.117
|
.283
|
3.609
|
.001
|
X23 - Consider Strategic Alliance
|
.611
|
.115
|
.376
|
5.295
|
.000
|
a. Dependent Variable: Loyalty
|
Then we run it again using method: Backward and look for the Excluded Variables table.
Excluded Variablesa
|
Model
|
Beta In
|
t
|
Sig.
|
Partial Correlation
|
Collinearity Statistics
|
Tolerance
|
2
|
Customer_Services
|
.091b
|
1.044
|
.302
|
.152
|
.566
|
3
|
Customer_Services
|
.085c
|
.973
|
.336
|
.140
|
.568
|
X2 - Industry Type
|
.077c
|
1.070
|
.290
|
.154
|
.833
|
4
|
Customer_Services
|
.007d
|
.091
|
.928
|
.013
|
.729
|
X2 - Industry Type
|
.071d
|
.973
|
.335
|
.139
|
.835
|
Product_Value
|
.164d
|
1.624
|
.111
|
.228
|
.422
|
5
|
Customer_Services
|
.050e
|
.675
|
.503
|
.096
|
.849
|
X2 - Industry Type
|
.043e
|
.583
|
.562
|
.083
|
.877
|
Product_Value
|
.112e
|
1.112
|
.272
|
.157
|
.453
|
X4 - Region
|
.157e
|
1.608
|
.114
|
.224
|
.469
|
a. Dependent Variable: Loyalty
|
b. Predictors in the Model: (Constant), X23 - Consider Strategic Alliance, Product_Value, X2 - Industry Type, Marketing, Technical_Support, X5 - Distribution System, X3 - Firm Size, X1 - Customer Type, X4 - Region
|
c. Predictors in the Model: (Constant), X23 - Consider Strategic Alliance, Product_Value, Marketing, Technical_Support, X5 - Distribution System, X3 - Firm Size, X1 - Customer Type, X4 - Region
|
d. Predictors in the Model: (Constant), X23 - Consider Strategic Alliance, Marketing, Technical_Support, X5 - Distribution System, X3 - Firm Size, X1 - Customer Type, X4 - Region
|
e. Predictors in the Model: (Constant), X23 - Consider Strategic Alliance, Marketing, Technical_Support, X5 - Distribution System, X3 - Firm Size, X1 - Customer Type
|
Then we to run regression again and use method: Enter again, but before that we remove the Excluded Variables from the IVs block.
Variables Entered/Removeda
|
Model
|
Variables Entered
|
Variables Removed
|
Method
|
1
|
X23 - Consider Strategic Alliance, X1 - Customer Type, Technical_Support, X5 - Distribution System, X3 - Firm Size, Marketingb
|
.
|
Enter
|
a. Dependent Variable: Loyalty
|
b. All requested variables entered.
|
Model Summary
|
Model
|
R
|
R Square
|
Adjusted R Square
|
Std. Error of the Estimate
|
1
|
.878a
|
.770
|
.742
|
.38039
|
a. Predictors: (Constant), X23 - Consider Strategic Alliance, X1 - Customer Type, Technical_Support, X5 - Distribution System, X3 - Firm Size, Marketing
|
ANOVAa
|
Model
|
Sum of Squares
|
df
|
Mean Square
|
F
|
Sig.
|
1
|
Regression
|
24.229
|
6
|
4.038
|
27.908
|
.000b
|
Residual
|
7.235
|
50
|
.145
|
|
|
Total
|
31.463
|
56
|
|
|
|
a. Dependent Variable: Loyalty
|
b. Predictors: (Constant), X23 - Consider Strategic Alliance, X1 - Customer Type, Technical_Support, X5 - Distribution System, X3 - Firm Size, Marketing
|
Coefficientsa
|
Model
|
Unstandardized Coefficients
|
Standardized Coefficients
|
t
|
Sig.
|
B
|
Std. Error
|
Beta
|
1
|
(Constant)
|
5.614
|
.271
|
|
20.688
|
.000
|
Marketing
|
.194
|
.053
|
.287
|
3.690
|
.001
|
Technical_Support
|
.139
|
.063
|
.169
|
2.210
|
.032
|
X1 - Customer Type
|
.424
|
.097
|
.322
|
4.358
|
.000
|
X3 - Firm Size
|
.569
|
.116
|
.380
|
4.898
|
.000
|
X5 - Distribution System
|
.482
|
.109
|
.324
|
4.419
|
.000
|
X23 - Consider Strategic Alliance
|
.661
|
.116
|
.407
|
5.677
|
.000
|
a. Dependent Variable: Loyalty
|
Case 2 (Lower Purchase):
First, we run regression model after selecting case 2 (Lower Purchase) following the same steps as Q2. We add the same DV (Loyalty= Sum score) as in second exercise, but we add more IV. Then we run regression.
First time we run regression using method: Enter.
Variables Entered/Removeda
|
Model
|
Variables Entered
|
Variables Removed
|
Method
|
1
|
X23 - Consider Strategic Alliance, X3 - Firm Size, Technical_Support, X2 - Industry Type, Marketing, X1 - Customer Type, Product_Value, Customer_Services, X5 - Distribution System, X4 - Regionb
|
.
|
Enter
|
a. Dependent Variable: Loyalty
|
b. All requested variables entered.
|
Model Summary
|
Model
|
R
|
R Square
|
Adjusted R Square
|
Std. Error of the Estimate
|
1
|
.912a
|
.832
|
.779
|
.35264
|
a. Predictors: (Constant), X23 - Consider Strategic Alliance, X3 - Firm Size, Technical_Support, X2 - Industry Type, Marketing, X1 - Customer Type, Product_Value, Customer_Services, X5 - Distribution System, X4 - Region
|
ANOVAa
|
Model
|
Sum of Squares
|
df
|
Mean Square
|
F
|
Sig.
|
1
|
Regression
|
19.702
|
10
|
1.970
|
15.844
|
.000b
|
Residual
|
3.979
|
32
|
.124
|
|
|
Total
|
23.681
|
42
|
|
|
|
a. Dependent Variable: Loyalty
|
b. Predictors: (Constant), X23 - Consider Strategic Alliance, X3 - Firm Size, Technical_Support, X2 - Industry Type, Marketing, X1 - Customer Type, Product_Value, Customer_Services, X5 - Distribution System, X4 - Region
|
Coefficientsa
|
Model
|
Unstandardized Coefficients
|
Standardized Coefficients
|
t
|
Sig.
|
B
|
Std. Error
|
Beta
|
1
|
(Constant)
|
5.602
|
.320
|
|
17.506
|
.000
|
Customer_Services
|
.019
|
.083
|
.023
|
.224
|
.824
|
Marketing
|
-.017
|
.082
|
-.018
|
-.213
|
.833
|
Technical_Support
|
.035
|
.054
|
.052
|
.650
|
.520
|
Product_Value
|
-.220
|
.140
|
-.236
|
-1.565
|
.127
|
X1 - Customer Type
|
.420
|
.169
|
.260
|
2.480
|
.019
|
X2 - Industry Type
|
-.159
|
.117
|
-.107
|
-1.355
|
.185
|
X3 - Firm Size
|
.819
|
.149
|
.548
|
5.503
|
.000
|
X4 - Region
|
-.566
|
.239
|
-.342
|
-2.369
|
.024
|
X5 - Distribution System
|
.911
|
.213
|
.593
|
4.281
|
.000
|
X23 - Consider Strategic Alliance
|
.482
|
.212
|
.208
|
2.275
|
.030
|
a. Dependent Variable: Loyalty
|
Then we run it again using method: Backward and look for the Excluded Variables table.
Excluded Variablesa
|
Model
|
Beta In
|
t
|
Sig.
|
Partial Correlation
|
Collinearity Statistics
|
Tolerance
|
2
|
Marketing
|
-.018b
|
-.213
|
.833
|
-.038
|
.766
|
3
|
Marketing
|
-.019c
|
-.228
|
.821
|
-.040
|
.768
|
Customer_Services
|
.024c
|
.240
|
.812
|
.042
|
.493
|
4
|
Marketing
|
-.028d
|
-.346
|
.731
|
-.059
|
.790
|
Customer_Services
|
.027d
|
.270
|
.788
|
.046
|
.494
|
Technical_Support
|
.055d
|
.725
|
.473
|
.123
|
.853
|
5
|
Marketing
|
-.026e
|
-.320
|
.751
|
-.054
|
.790
|
Customer_Services
|
.035e
|
.342
|
.734
|
.058
|
.495
|
Technical_Support
|
.065e
|
.847
|
.403
|
.142
|
.860
|
X2 - Industry Type
|
-.113e
|
-1.485
|
.146
|
-.243
|
.850
|
6
|
Marketing
|
.003f
|
.033
|
.974
|
.006
|
.829
|
Customer_Services
|
.043f
|
.417
|
.679
|
.069
|
.497
|
Technical_Support
|
.094f
|
1.268
|
.213
|
.207
|
.940
|
X2 - Industry Type
|
-.079f
|
-1.033
|
.308
|
-.170
|
.896
|
Product_Value
|
-.215f
|
-1.624
|
.113
|
-.261
|
.287
|
a. Dependent Variable: Loyalty
|
b. Predictors in the Model: (Constant), X23 - Consider Strategic Alliance, X3 - Firm Size, Technical_Support, X2 - Industry Type, X1 - Customer Type, Product_Value, Customer_Services, X5 - Distribution System, X4 - Region
|
c. Predictors in the Model: (Constant), X23 - Consider Strategic Alliance, X3 - Firm Size, Technical_Support, X2 - Industry Type, X1 - Customer Type, Product_Value, X5 - Distribution System, X4 - Region
|
d. Predictors in the Model: (Constant), X23 - Consider Strategic Alliance, X3 - Firm Size, X2 - Industry Type, X1 - Customer Type, Product_Value, X5 - Distribution System, X4 - Region
|
e. Predictors in the Model: (Constant), X23 - Consider Strategic Alliance, X3 - Firm Size, X1 - Customer Type, Product_Value, X5 - Distribution System, X4 - Region
|
f. Predictors in the Model: (Constant), X23 - Consider Strategic Alliance, X3 - Firm Size, X1 - Customer Type, X5 - Distribution System, X4 - Region
|
Then we to run regression again and use method: Enter again, but before that we remove the Excluded Variables from the IVs block.
Variables Entered/Removeda
|
Model
|
Variables Entered
|
Variables Removed
|
Method
|
1
|
X23 - Consider Strategic Alliance, X3 - Firm Size, X1 - Customer Type, X4 - Region, X5 - Distribution Systemb
|
.
|
Enter
|
a. Dependent Variable: Loyalty
|
b. All requested variables entered.
|
Model Summary
|
Model
|
R
|
R Square
|
Adjusted R Square
|
Std. Error of the Estimate
|
1
|
.897a
|
.805
|
.778
|
.35355
|
a. Predictors: (Constant), X23 - Consider Strategic Alliance, X3 - Firm Size, X1 - Customer Type, X4 - Region, X5 - Distribution System
|
ANOVAa
|
Model
|
Sum of Squares
|
df
|
Mean Square
|
F
|
Sig.
|
1
|
Regression
|
19.056
|
5
|
3.811
|
30.491
|
.000b
|
Residual
|
4.625
|
37
|
.125
|
|
|
Total
|
23.681
|
42
|
|
|
|
a. Dependent Variable: Loyalty
|
b. Predictors: (Constant), X23 - Consider Strategic Alliance, X3 - Firm Size, X1 - Customer Type, X4 - Region, X5 - Distribution System
|
Coefficientsa
|
Model
|
Unstandardized Coefficients
|
Standardized Coefficients
|
t
|
Sig.
|
B
|
Std. Error
|
Beta
|
1
|
(Constant)
|
5.399
|
.231
|
|
23.412
|
.000
|
X1 - Customer Type
|
.542
|
.134
|
.335
|
4.043
|
.000
|
X3 - Firm Size
|
.699
|
.130
|
.468
|
5.388
|
.000
|
X4 - Region
|
-.336
|
.174
|
-.203
|
-1.938
|
.060
|
X5 - Distribution System
|
.780
|
.182
|
.508
|
4.298
|
.000
|
X23 - Consider Strategic Alliance
|
.565
|
.190
|
.244
|
2.965
|
.005
|
a. Dependent Variable: Loyalty
|