bab 11, 12 & 15
DESCRIPTION
fadaadTRANSCRIPT
GET TRANSLATE FILE='C:\Users\nn\Documents\New folder\Data multivariate\Data multivariate\diskriminan.xls' /TYPE=XLS /MAP /FIELDNAMES .
Data written to the working file.4 variables and 24 cases written.Variable: Persh Type: Number Format: F11.2Variable: Firm Type: Number Format: F11.2Variable: EBITASS Type: Number Format: F11.2Variable: ROTC Type: Number Format: F11.2DATASET NAME DataSet1 WINDOW=FRONT.DISCRIMINANT /GROUPS=Firm(1 2) /VARIABLES=EBITASS ROTC /ANALYSIS ALL /PRIORS EQUAL /STATISTICS=MEAN STDDEV UNIVF BOXM COEFF RAW CORR COV GCOV TCOV /PLOT=COMBINED SEPARATE MAP /PLOT=CASES
/CLASSIFY=NONMISSING POOLED.
Discriminant
Notes
Output Created 07-Dec-2015 22:36:48
Comments
Input Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data File 24
Missing Value Handling Definition of Missing User-defined missing values are treated as
missing in the analysis phase.
Cases Used In the analysis phase, cases with no user-
or system-missing values for any predictor
variable are used. Cases with user-,
system-missing, or out-of-range values for
the grouping variable are always excluded.
Syntax DISCRIMINANT
/GROUPS=Firm(1 2)
/VARIABLES=EBITASS ROTC
/ANALYSIS ALL
/PRIORS EQUAL
/STATISTICS=MEAN STDDEV UNIVF
BOXM COEFF RAW CORR COV GCOV
TCOV
/PLOT=COMBINED SEPARATE MAP
/PLOT=CASES
/CLASSIFY=NONMISSING POOLED.
Resources Processor Time 00:00:00.889
Elapsed Time 00:00:01.277
[DataSet1]
Warnings
All-Groups Stacked Histogram is no longer displayed.
Analysis Case Processing Summary
Unweighted Cases N Percent
Valid 24 100.0
Excluded Missing or out-of-range group
codes0 .0
At least one missing
discriminating variable0 .0
Both missing or out-of-range
group codes and at least one
missing discriminating variable
0 .0
Total 0 .0
Total 24 100.0
Group Statistics
Firm Mean Std. Deviation
Valid N (listwise)
Unweighted Weighted
1 EBITASS .1913 .05324 12 12.000
ROTC .1835 .03022 12 12.000
2 EBITASS .0033 .04492 12 12.000
ROTC .0012 .06852 12 12.000
Total EBITASS .0973 .10743 24 24.000
ROTC .0924 .10652 24 24.000
Tests of Equality of Group Means
Wilks' Lambda F df1 df2 Sig.
EBITASS .201 87.408 1 22 .000
ROTC .236 71.070 1 22 .000
Pooled Within-Groups Matricesa
EBITASS ROTC
Covariance EBITASS .002 .002
ROTC .002 .003
Correlation EBITASS 1.000 .780
ROTC .780 1.000
a. The covariance matrix has 22 degrees of freedom.
Covariance Matricesa
Firm EBITASS ROTC
1 EBITASS .003 .001
ROTC .001 .001
2 EBITASS .002 .003
ROTC .003 .005
Total EBITASS .012 .011
ROTC .011 .011
a. The total covariance matrix has 23 degrees of
freedom.
Analysis 1
Box's Test of Equality of Covariance Matrices
Log Determinants
Firm Rank Log Determinant
1 2 -13.516
2 2 -14.108
Pooled within-groups 2 -12.834
The ranks and natural logarithms of determinants printed are
those of the group covariance matrices.
Test Results
Box's M 21.504
F Approx. 6.464
df1 3
df2 8.712E4
Sig. .000
Tests null hypothesis of equal
population covariance matrices.
Summary of Canonical Discriminant Functions
Eigenvalues
Function Eigenvalue % of Variance Cumulative %
Canonical
Correlation
1 4.124a 100.0 100.0 .897
a. First 1 canonical discriminant functions were used in the analysis.
Wilks' Lambda
Test of
Function
(s) Wilks' Lambda Chi-square df Sig.
1 .195 34.312 2 .000
Standardized Canonical
Discriminant Function
Coefficients
Function
1
EBITASS .743
ROTC .305
Structure Matrix
Function
1
EBITASS .982
ROTC .885
Pooled within-groups
correlations between
discriminating variables and
standardized canonical
discriminant functions
Variables ordered by absolute
size of correlation within
function.
Canonical Discriminant
Function Coefficients
Function
1
EBITASS 15.092
ROTC 5.769
(Constant) -2.002
Unstandardized coefficients
Functions at Group
Centroids
Firm
Function
1
1 1.944
2 -1.944
Unstandardized canonical
discriminant functions
evaluated at group means
Classification Statistics
Classification Processing Summary
Processed 24
Excluded Missing or out-of-range group
codes0
At least one missing
discriminating variable0
Used in Output 24
Prior Probabilities for Groups
Firm Prior
Cases Used in Analysis
Unweighted Weighted
1 .500 12 12.000
2 .500 12 12.000
Total 1.000 24 24.000
Classification Function Coefficients
Firm
1 2
EBITASS 61.237 2.551
ROTC 21.027 -1.404
(Constant) -8.481 -.697
Fisher's linear discriminant functions
Casewise Statistics
Case
Number Actual Group
Highest Group Second Highest Group
Discriminant
Scores
Predicted Group
P(D>d | G=g)
P(G=g | D=d)
Squared
Mahalanobis
Distance to
Centroid Group P(G=g | D=d)
Squared
Mahalanobis
Distance to
Centroid Function 1p df
Original 1 1 1 .609 1 .996 .262 2 .004 11.403 1.433
2 1 1 .681 1 1.000 .169 2 .000 18.491 2.356
3 1 1 .793 1 1.000 .069 2 .000 17.231 2.207
4 1 1 .101 1 1.000 2.693 2 .000 30.576 3.585
5 1 1 .888 1 1.000 .020 2 .000 16.232 2.085
6 1 1 .633 1 1.000 .228 2 .000 19.065 2.422
7 1 1 .561 1 .995 .339 2 .005 10.934 1.362
8 1 1 .267 1 1.000 1.232 2 .000 24.988 3.054
9 1 1 .057 1 .537 3.632 2 .463 3.931 .038
10 1 1 .338 1 .979 .920 2 .021 8.582 .985
11 1 1 .950 1 .999 .004 2 .001 14.639 1.882
12 1 1 .982 1 .999 .000 2 .001 14.949 1.922
13 2 2 .676 1 1.000 .174 1 .000 18.542 -2.362
14 2 2 .429 1 .989 .627 1 .011 9.592 -1.153
15 2 2 .469 1 .991 .524 1 .009 10.017 -1.221
16 2 2 .151 1 1.000 2.060 1 .000 28.342 -3.379
17 2 2 .119 1 1.000 2.430 1 .000 29.675 -3.503
18 2 2 .931 1 1.000 .007 1 .000 15.800 -2.031
19 2 2 .808 1 .999 .059 1 .001 13.291 -1.701
20 2 1** .062 1 .573 3.493 2 .427 4.079 .075
21 2 2 .310 1 1.000 1.033 1 .000 24.057 -2.960
22 2 2 .322 1 .976 .982 1 .024 8.397 -.953
23 2 2 .556 1 1.000 .346 1 .000 20.043 -2.533
24 2 2 .738 1 .998 .112 1 .002 12.636 -1.610
**. Misclassified case
Separate-Groups Graphs
GET FILE='C:\Users\nn\Documents\New folder\Data multivariate\Data multivariate\hatco.sav'.DATASET NAME DataSet2 WINDOW=FRONT.DISCRIMINANT /GROUPS=x11(0 1) /VARIABLES=x1 x2 x3 x4 x5 x6 x7 /ANALYSIS ALL /METHOD=MAHAL /PIN=.05 /POUT=.10 /PRIORS EQUAL /HISTORY /STATISTICS=MEAN STDDEV UNIVF BOXM COEFF RAW CORR COV GCOV TCOV /PLOT=COMBINED SEPARATE MAP /PLOT=CASES
/CLASSIFY=NONMISSING POOLED.
Discriminant
Notes
Output Created 07-Dec-2015 22:43:25
Comments
Input Data C:\Users
n\Documents\New folder\Data
multivariate\Data multivariate\hatco.sav
Active Dataset DataSet2
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data File 100
Missing Value Handling Definition of Missing User-defined missing values are treated as
missing in the analysis phase.
Cases Used In the analysis phase, cases with no user-
or system-missing values for any predictor
variable are used. Cases with user-,
system-missing, or out-of-range values for
the grouping variable are always excluded.
Syntax DISCRIMINANT
/GROUPS=x11(0 1)
/VARIABLES=x1 x2 x3 x4 x5 x6 x7
/ANALYSIS ALL
/METHOD=MAHAL
/PIN=.05
/POUT=.10
/PRIORS EQUAL
/HISTORY
/STATISTICS=MEAN STDDEV UNIVF
BOXM COEFF RAW CORR COV GCOV
TCOV
/PLOT=COMBINED SEPARATE MAP
/PLOT=CASES
/CLASSIFY=NONMISSING POOLED.
Resources Processor Time 00:00:00.374
Elapsed Time 00:00:00.364
[DataSet2] C:\Users\nn\Documents\New folder\Data multivariate\Data multivariate\hatco.sav
Warnings
All-Groups Stacked Histogram is no longer displayed.
Analysis Case Processing Summary
Unweighted Cases N Percent
Valid 100 100.0
Excluded Missing or out-of-range group
codes0 .0
At least one missing
discriminating variable0 .0
Both missing or out-of-range
group codes and at least one
missing discriminating variable
0 .0
Total 0 .0
Total 100 100.0
Group Statistics
Specification Buying Mean Std. Deviation
Valid N (listwise)
Unweighted Weighted
Specification Buying Delivery Speed 2.500 1.0190 40 40.000
Price Level 2.988 1.1711 40 40.000
Price Flexibility 6.802 .8905 40 40.000
Manufacturer Image 5.300 .8488 40 40.000
Service 2.715 .9161 40 40.000
Salesforce Image 2.625 .6084 40 40.000
Product Quality 8.292 .9297 40 40.000
Total Value Analysis Delivery Speed 4.192 1.0375 60 60.000
Price Level 1.948 1.0262 60 60.000
Price Flexibility 8.622 1.1642 60 60.000
Manufacturer Image 5.213 1.2918 60 60.000
Service 3.050 .5887 60 60.000
Salesforce Image 2.692 .8664 60 60.000
Product Quality 6.090 1.2931 60 60.000
Total Delivery Speed 3.515 1.3207 100 100.000
Price Level 2.364 1.1957 100 100.000
Price Flexibility 7.894 1.3865 100 100.000
Manufacturer Image 5.248 1.1314 100 100.000
Service 2.916 .7513 100 100.000
Salesforce Image 2.665 .7709 100 100.000
Product Quality 6.971 1.5852 100 100.000
Tests of Equality of Group Means
Wilks' Lambda F df1 df2 Sig.
Delivery Speed .602 64.716 1 98 .000
Price Level .817 21.968 1 98 .000
Price Flexibility .583 70.191 1 98 .000
Manufacturer Image .999 .140 1 98 .709
Service .952 4.963 1 98 .028
Salesforce Image .998 .178 1 98 .674
Product Quality .532 86.200 1 98 .000
Pooled Within-Groups Matricesa
Delivery Speed Price Level Price Flexibility
Manufacturer
Image Service Salesforce Image Product Quality
Covariance Delivery Speed 1.061 -.127 .188 .112 .475 .052 -.108
Price Level -.127 1.180 -.353 .350 .551 .190 .339
Price Flexibility .188 -.353 1.132 -.145 -.079 -.067 -.014
Manufacturer Image .112 .350 -.145 1.291 .264 .696 .316
Service .475 .551 -.079 .264 .543 .135 .114
Salesforce Image .052 .190 -.067 .696 .135 .599 .255
Product Quality -.108 .339 -.014 .316 .114 .255 1.351
Correlation Delivery Speed 1.000 -.113 .172 .096 .625 .065 -.090
Price Level -.113 1.000 -.306 .283 .688 .226 .269
Price Flexibility .172 -.306 1.000 -.120 -.101 -.081 -.011
Manufacturer Image .096 .283 -.120 1.000 .315 .791 .239
Service .625 .688 -.101 .315 1.000 .237 .134
Salesforce Image .065 .226 -.081 .791 .237 1.000 .283
Product Quality -.090 .269 -.011 .239 .134 .283 1.000
a. The covariance matrix has 98 degrees of freedom.
Covariance Matricesa
Specification Buying Delivery Speed Price Level Price Flexibility
Manufacturer
Image Service Salesforce Image Product Quality
Specification Buying Delivery Speed 1.038 .448 -.112 .366 .740 .167 .068
Price Level .448 1.371 -.158 .296 .919 .215 .258
Price Flexibility -.112 -.158 .793 -.162 -.130 -.143 -.129
Manufacturer Image .366 .296 -.162 .721 .333 .377 .116
Service .740 .919 -.130 .333 .839 .203 .159
Salesforce Image .167 .215 -.143 .377 .203 .370 .202
Product Quality .068 .258 -.129 .116 .159 .202 .864
Total Value Analysis Delivery Speed 1.076 -.507 .387 -.056 .299 -.025 -.225
Price Level -.507 1.053 -.482 .385 .308 .174 .392
Price Flexibility .387 -.482 1.355 -.135 -.046 -.016 .062
Manufacturer Image -.056 .385 -.135 1.669 .218 .907 .447
Service .299 .308 -.046 .218 .347 .091 .085
Salesforce Image -.025 .174 -.016 .907 .091 .751 .290
Product Quality -.225 .392 .062 .447 .085 .290 1.672
Total Delivery Speed 1.744 -.551 .933 .075 .607 .079 -1.010
Price Level -.551 1.430 -.808 .368 .461 .172 .890
Price Flexibility .933 -.808 1.922 -.182 .069 -.037 -.985
Manufacturer Image .075 .368 -.182 1.280 .254 .687 .359
Service .607 .461 .069 .254 .564 .139 -.066
Salesforce Image .079 .172 -.037 .687 .139 .594 .217
Product Quality -1.010 .890 -.985 .359 -.066 .217 2.513
a. The total covariance matrix has 99 degrees of freedom.
Analysis 1
Box's Test of Equality of Covariance Matrices
Log Determinants
Specification Buying Rank Log Determinant
Specification Buying 3 -.383
Total Value Analysis 3 .744
Pooled within-groups 3 .445
The ranks and natural logarithms of determinants printed are
those of the group covariance matrices.
Test Results
Box's M 14.651
F Approx. 2.356
df1 6
df2 4.704E4
Sig. .028
Tests null hypothesis of equal
population covariance matrices.
Stepwise Statistics
Variables Entered/Removeda,b,c,d
Step Entered
Min. D Squared
Statistic Between Groups
Exact F
Statistic df1 df2 Sig.
1Product
Quality3.592
Specification
Buying and Total
Value Analysis
86.200 1 98.000 4.327E-15
2Price
Flexibility6.445
Specification
Buying and Total
Value Analysis
76.552 2 97.000 1.121E-20
3Delivery
Speed7.896
Specification
Buying and Total
Value Analysis
61.879 3 96.000 2.356E-22
At each step, the variable that maximizes the Mahalanobis distance between the two closest groups is entered.
a. Maximum number of steps is 14.
b. Maximum significance of F to enter is .05.
c. Minimum significance of F to remove is .10.
d. F level, tolerance, or VIN insufficient for further computation.
Variables in the Analysis
Step Tolerance
Sig. of F to
Remove Min. D Squared Between Groups
1 Product Quality 1.000 .000
2 Product Quality
1.000 .000 2.925
Specification
Buying and Total
Value Analysis
Price Flexibility
1.000 .000 3.592
Specification
Buying and Total
Value Analysis
3 Product Quality
.992 .000 4.797
Specification
Buying and Total
Value Analysis
Price Flexibility
.970 .000 5.772
Specification
Buying and Total
Value Analysis
Delivery Speed
.963 .000 6.445
Specification
Buying and Total
Value Analysis
Variables Not in the Analysis
Step Tolerance Min. Tolerance Sig. of F to Enter Min. D Squared Between Groups
0 Delivery Speed
1.000 1.000 .000 2.696
Specification
Buying and Total
Value Analysis
Price Level
1.000 1.000 .000 .915
Specification
Buying and Total
Value Analysis
Price Flexibility
1.000 1.000 .000 2.925
Specification
Buying and Total
Value Analysis
Manufacturer Image
1.000 1.000 .709 .006
Specification
Buying and Total
Value Analysis
Service 1.000 1.000 .028 .207 Specification
Buying and Total
Value Analysis
Salesforce Image
1.000 1.000 .674 .007
Specification
Buying and Total
Value Analysis
Product Quality
1.000 1.000 .000 3.592
Specification
Buying and Total
Value Analysis
1 Delivery Speed
.992 .992 .000 5.772
Specification
Buying and Total
Value Analysis
Price Level
.928 .928 .102 3.808
Specification
Buying and Total
Value Analysis
Price Flexibility
1.000 1.000 .000 6.445
Specification
Buying and Total
Value Analysis
Manufacturer Image
.943 .943 .171 3.742
Specification
Buying and Total
Value Analysis
Service
.982 .982 .013 4.102
Specification
Buying and Total
Value Analysis
Salesforce Image
.920 .920 .023 4.014
Specification
Buying and Total
Value Analysis
2 Delivery Speed
.963 .963 .000 7.896
Specification
Buying and Total
Value Analysis
Price Level
.836 .836 .835 6.450
Specification
Buying and Total
Value Analysis
Manufacturer Image
.929 .929 .075 6.801
Specification
Buying and Total
Value Analysis
Service
.972 .972 .009 7.234
Specification
Buying and Total
Value Analysis
Salesforce Image
.914 .914 .019 7.068
Specification
Buying and Total
Value Analysis
3 Price Level
.835 .835 .738 7.910
Specification
Buying and Total
Value Analysis
Manufacturer Image
.909 .909 .233 8.078
Specification
Buying and Total
Value Analysis
Service
.527 .522 .779 7.906
Specification
Buying and Total
Value Analysis
Salesforce Image
.903 .903 .066 8.332
Specification
Buying and Total
Value Analysis
Wilks' Lambda
Step
Number of
Variables Lambda df1 df2 df3
Exact F
Statistic df1 df2 Sig.
1 1 .532 1 1 98 86.200 1 98.000 .000
2 2 .388 2 1 98 76.552 2 97.000 .000
3 3 .341 3 1 98 61.879 3 96.000 .000
Summary of Canonical Discriminant Functions
Eigenvalues
Function Eigenvalue % of Variance Cumulative %
Canonical
Correlation
1 1.934a 100.0 100.0 .812
a. First 1 canonical discriminant functions were used in the analysis.
Wilks' Lambda
Test of
Function
(s) Wilks' Lambda Chi-square df Sig.
1 .341 103.860 3 .000
Standardized Canonical
Discriminant Function Coefficients
Function
1
Delivery Speed .437
Price Flexibility .526
Product Quality -.629
Structure Matrix
Function
1
Product Quality -.674
Price Flexibility .609
Delivery Speed .584
Price Levela -.379
Salesforce Imagea -.193
Manufacturer Imagea -.172
Servicea .136
Pooled within-groups correlations between
discriminating variables and standardized
canonical discriminant functions
Variables ordered by absolute size of
correlation within function.
a. This variable not used in the analysis.
Canonical Discriminant Function
Coefficients
Function
1
Delivery Speed .424
Price Flexibility .495
Product Quality -.541
(Constant) -1.624
Unstandardized coefficients
Functions at Group Centroids
Specification Buying
Function
1
Specification Buying -1.686
Total Value Analysis 1.124
Unstandardized canonical discriminant
functions evaluated at group means
Classification Statistics
Classification Processing Summary
Processed 100
Excluded Missing or out-of-range group
codes0
At least one missing
discriminating variable0
Used in Output 100
Prior Probabilities for Groups
Specification Buying Prior
Cases Used in Analysis
Unweighted Weighted
Specification Buying .500 40 40.000
Total Value Analysis .500 60 60.000
Total 1.000 100 100.000
Classification Function Coefficients
Specification Buying
Specification
Buying
Total Value
Analysis
Delivery Speed 1.982 3.174
Price Flexibility 5.759 7.149
Product Quality 6.357 4.836
(Constant) -49.116 -52.891
Fisher's linear discriminant functions
Casewise Statistics
Case
Number Actual Group
Highest Group Second Highest Group
Discriminant
Scores
Predicted Group
P(D>d | G=g)
P(G=g | D=d)
Squared
Mahalanobis
Distance to
Centroid Group P(G=g | D=d)
Squared
Mahalanobis
Distance to
Centroid Function 1p df
Original 1 1 1 .682 1 .943 .167 0 .057 5.764 .715
2 0 0 .546 1 .996 .365 1 .004 11.654 -2.290
3 0 0 .909 1 .986 .013 1 .014 8.549 -1.800
4 0 0 .618 1 .927 .249 1 .073 5.342 -1.187
5 1 1 .035 1 1.000 4.460 0 .000 24.224 3.236
6 0 0 .636 1 .995 .224 1 .005 10.779 -2.159
7 1 1 .834 1 .966 .044 0 .034 6.763 .915
8 0 0 .957 1 .984 .003 1 .016 8.199 -1.739
9 1 1 .902 1 .987 .015 0 .013 8.601 1.247
10 0 0 .790 1 .961 .071 1 .039 6.472 -1.420
11 1 1 .607 1 .924 .265 0 .076 5.269 .609
12 1 1 .279 1 .712 1.173 0 .288 2.982 .041
13 0 1** .261 1 .687 1.264 0 .313 2.841 .000
14 1 1 .583 1 .917 .302 0 .083 5.110 .575
15 1 1 .643 1 .995 .215 0 .005 10.719 1.588
16 1 1 .370 1 .998 .803 0 .002 13.736 2.020
17 1 0** .377 1 .812 .781 1 .188 3.710 -.802
18 1 1 .958 1 .978 .003 0 .022 7.602 1.071
19 1 1 .536 1 .997 .384 0 .003 11.761 1.743
20 1 1 .643 1 .995 .215 0 .005 10.719 1.588
21 1 1 .615 1 .927 .253 0 .073 5.324 .621
22 1 1 .989 1 .980 .000 0 .020 7.822 1.111
23 1 0** .197 1 .579 1.667 1 .421 2.307 -.395
24 0 0 .618 1 .927 .248 1 .073 5.344 -1.188
25 1 1 .096 1 1.000 2.767 0 .000 20.012 2.788
26 1 1 .570 1 .996 .323 0 .004 11.413 1.692
27 0 0 .654 1 .936 .201 1 .064 5.576 -1.237
28 1 1 .528 1 .997 .398 0 .003 11.841 1.755
29 1 1 .476 1 .997 .508 0 .003 12.410 1.837
30 0 0 .862 1 .970 .030 1 .030 6.950 -1.512
31 0 0 .979 1 .982 .001 1 .018 8.047 -1.713
32 1 0** .224 1 .630 1.477 1 .370 2.543 -.471
33 1 1 .116 1 1.000 2.465 0 .000 19.183 2.694
34 0 0 .901 1 .973 .015 1 .027 7.213 -1.562
35 0 1** .202 1 .590 1.627 0 .410 2.354 -.152
36 0 0 .672 1 .940 .179 1 .060 5.697 -1.263
37 0 0 .565 1 .996 .331 1 .004 11.458 -2.261
38 1 1 .617 1 .927 .250 0 .073 5.338 .624
39 0 0 .180 1 1.000 1.798 1 .000 17.230 -3.027
40 0 0 .606 1 .995 .267 1 .005 11.064 -2.202
41 0 0 .645 1 .934 .212 1 .066 5.518 -1.225
42 1 1 .039 1 1.000 4.282 0 .000 23.808 3.193
43 1 1 .564 1 .996 .332 0 .004 11.469 1.701
44 1 1 .104 1 1.000 2.643 0 .000 19.676 2.750
45 0 0 .635 1 .995 .225 1 .005 10.785 -2.160
46 1 1 .539 1 .997 .377 0 .003 11.726 1.738
47 1 1 .145 1 1.000 2.128 0 .000 18.222 2.583
48 0 0 .515 1 .997 .423 1 .003 11.975 -2.337
49 1 1 .660 1 .994 .194 0 .006 10.564 1.564
50 1 1 .492 1 .997 .471 0 .003 12.226 1.811
51 1 1 .949 1 .984 .004 0 .016 8.263 1.189
52 0 0 .726 1 .951 .123 1 .049 6.046 -1.335
53 0 0 .745 1 .954 .106 1 .046 6.176 -1.361
54 0 0 .678 1 .994 .172 1 .006 10.399 -2.101
55 1 1 .890 1 .987 .019 0 .013 8.690 1.262
56 1 0** .350 1 .790 .873 1 .210 3.517 -.751
57 0 0 .545 1 .904 .367 1 .096 4.858 -1.080
58 1 1 .900 1 .987 .016 0 .013 8.615 1.249
59 1 1 .649 1 .995 .207 0 .005 10.661 1.579
60 0 0 .774 1 .959 .083 1 .041 6.364 -1.399
61 1 1 .159 1 1.000 1.986 0 .000 17.802 2.533
62 1 1 .125 1 1.000 2.347 0 .000 18.854 2.656
63 1 1 .680 1 .942 .170 0 .058 5.749 .712
64 1 0** .419 1 .842 .653 1 .158 4.007 -.878
65 0 0 .186 1 1.000 1.746 1 .000 17.067 -3.007
66 1 1 .685 1 .943 .165 0 .057 5.781 .718
67 1 1 .715 1 .949 .133 0 .051 5.977 .759
68 0 0 .949 1 .977 .004 1 .023 7.537 -1.621
69 1 1 .268 1 .999 1.228 0 .001 15.351 2.232
70 0 0 .826 1 .966 .048 1 .034 6.712 -1.467
71 0 0 .969 1 .983 .001 1 .017 8.113 -1.724
72 1 1 .414 1 .998 .666 0 .002 13.149 1.940
73 1 1 .305 1 .999 1.052 0 .001 14.714 2.150
74 1 1 .992 1 .982 .000 0 .018 7.949 1.133
75 0 0 .871 1 .970 .026 1 .030 7.012 -1.524
76 1 1 .360 1 .798 .839 0 .202 3.588 .208
77 1 1 .919 1 .986 .010 0 .014 8.481 1.226
78 1 1 .524 1 .997 .406 0 .003 11.883 1.761
79 0 0 .174 1 1.000 1.847 1 .000 17.381 -3.045
80 1 1 .830 1 .966 .046 0 .034 6.735 .909
81 1 1 .216 1 .999 1.532 0 .001 16.384 2.362
82 1 0** .415 1 .840 .665 1 .160 3.978 -.870
83 0 0 .497 1 .997 .461 1 .003 12.175 -2.365
84 1 0** .428 1 .848 .629 1 .152 4.068 -.893
85 0 1** .263 1 .690 1.253 0 .310 2.857 .004
86 0 0 .775 1 .991 .081 1 .009 9.580 -1.971
87 0 1** .230 1 .640 1.439 0 .360 2.593 -.076
88 1 0** .473 1 .873 .515 1 .127 4.379 -.969
89 0 0 .566 1 .912 .330 1 .088 4.999 -1.112
90 1 1 .743 1 .954 .107 0 .046 6.163 .797
91 1 0** .359 1 .797 .843 1 .203 3.579 -.768
92 1 1 .928 1 .976 .008 0 .024 7.395 1.033
93 1 0** .480 1 .877 .500 1 .123 4.423 -.979
94 0 0 .273 1 .999 1.202 1 .001 15.261 -2.782
95 1 1 .658 1 .937 .196 0 .063 5.606 .682
96 0 0 .288 1 .999 1.130 1 .001 15.002 -2.749
97 1 1 .583 1 .996 .301 0 .004 11.282 1.673
98 0 0 .288 1 .999 1.131 1 .001 15.003 -2.749
99 0 0 .884 1 .972 .021 1 .028 7.099 -1.540
100 1 1 .630 1 .931 .232 0 .069 5.422 .642
**. Misclassified case
Separate-Groups Graphs
GET FILE='C:\Users\nn\Documents\New folder\Data multivariate\Data multivariate\hatco.sav'.DATASET NAME DataSet3 WINDOW=FRONT.DISCRIMINANT /GROUPS=x14(1 3) /VARIABLES=x1 x2 x3 x4 x5 x6 x7 /ANALYSIS ALL /METHOD=MAHAL /PIN=.05 /POUT=.10 /PRIORS EQUAL /HISTORY /STATISTICS=MEAN STDDEV UNIVF BOXM COEFF RAW CORR COV GCOV TCOV /PLOT=COMBINED SEPARATE MAP /PLOT=CASES
/CLASSIFY=NONMISSING POOLED.
Discriminant
Notes
Output Created 07-Dec-2015 22:46:49
Comments
Input Data C:\Users
n\Documents\New folder\Data
multivariate\Data multivariate\hatco.sav
Active Dataset DataSet3
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data File 100
Missing Value Handling Definition of Missing User-defined missing values are treated as
missing in the analysis phase.
Cases Used In the analysis phase, cases with no user-
or system-missing values for any predictor
variable are used. Cases with user-,
system-missing, or out-of-range values for
the grouping variable are always excluded.
Syntax DISCRIMINANT
/GROUPS=x14(1 3)
/VARIABLES=x1 x2 x3 x4 x5 x6 x7
/ANALYSIS ALL
/METHOD=MAHAL
/PIN=.05
/POUT=.10
/PRIORS EQUAL
/HISTORY
/STATISTICS=MEAN STDDEV UNIVF
BOXM COEFF RAW CORR COV GCOV
TCOV
/PLOT=COMBINED SEPARATE MAP
/PLOT=CASES
/CLASSIFY=NONMISSING POOLED.
Resources Processor Time 00:00:00.889
Elapsed Time 00:00:01.052
[DataSet3]
Analysis Case Processing Summary
Unweighted Cases N Percent
Valid 100 100.0
Excluded Missing or out-of-range group
codes0 .0
At least one missing
discriminating variable0 .0
Both missing or out-of-range
group codes and at least one
missing discriminating variable
0 .0
Total 0 .0
Total 100 100.0
Group Statistics
Type of Buying Situation Mean Std. Deviation
Valid N (listwise)
Unweighted Weighted
New Task Delivery Speed 2.482 .9928 34 34.000
Price Level 2.094 .9512 34 34.000
Price Flexibility 7.135 1.0233 34 34.000
Manufacturer Image 4.959 1.0237 34 34.000
Service 2.229 .6250 34 34.000
Salesforce Image 2.615 .7110 34 34.000
Product Quality 7.615 1.3087 34 34.000
Modified Rebuy Delivery Speed 3.422 .9931 32 32.000
Price Level 3.181 1.3651 32 32.000
Price Flexibility 7.297 1.3054 32 32.000
Manufacturer Image 5.566 1.0216 32 32.000
Service 3.284 .6541 32 32.000
Salesforce Image 2.712 .6964 32 32.000
Product Quality 7.316 1.6316 32 32.000
Straight Rebuy Delivery Speed 4.635 .9594 34 34.000
Price Level 1.865 .8086 34 34.000
Price Flexibility 9.215 .6190 34 34.000
Manufacturer Image 5.238 1.2759 34 34.000
Service 3.256 .4054 34 34.000
Salesforce Image 2.671 .9037 34 34.000
Product Quality 6.003 1.3483 34 34.000
Total Delivery Speed 3.515 1.3207 100 100.000
Price Level 2.364 1.1957 100 100.000
Price Flexibility 7.894 1.3865 100 100.000
Manufacturer Image 5.248 1.1314 100 100.000
Service 2.916 .7513 100 100.000
Salesforce Image 2.665 .7709 100 100.000
Product Quality 6.971 1.5852 100 100.000
Tests of Equality of Group Means
Wilks' Lambda F df1 df2 Sig.
Delivery Speed .541 41.093 2 97 .000
Price Level .772 14.356 2 97 .000
Price Flexibility .526 43.776 2 97 .000
Manufacturer Image .952 2.442 2 97 .092
Service .565 37.320 2 97 .000
Salesforce Image .997 .132 2 97 .877
Product Quality .800 12.123 2 97 .000
Pooled Within-Groups Matricesa
Delivery Speed Price Level Price Flexibility
Manufacturer
Image Service Salesforce Image Product Quality
Covariance Delivery Speed .964 -.439 .140 -.014 .249 .061 -.408
Price Level -.439 1.126 -.504 .261 .366 .159 .707
Price Flexibility .140 -.504 1.031 -.196 -.197 -.044 -.318
Manufacturer Image -.014 .261 -.196 1.244 .152 .692 .392
Service .249 .366 -.197 .152 .326 .124 .161
Salesforce Image .061 .159 -.044 .692 .124 .605 .229
Product Quality -.408 .707 -.318 .392 .161 .229 2.052
Correlation Delivery Speed 1.000 -.422 .141 -.013 .445 .080 -.290
Price Level -.422 1.000 -.468 .221 .604 .192 .465
Price Flexibility .141 -.468 1.000 -.173 -.339 -.056 -.219
Manufacturer Image -.013 .221 -.173 1.000 .239 .797 .245
Service .445 .604 -.339 .239 1.000 .279 .197
Salesforce Image .080 .192 -.056 .797 .279 1.000 .206
Product Quality -.290 .465 -.219 .245 .197 .206 1.000
a. The covariance matrix has 97 degrees of freedom.
Covariance Matricesa
Type of Buying Situation Delivery Speed Price Level Price Flexibility
Manufacturer
Image Service Salesforce Image Product Quality
New Task Delivery Speed .986 -.299 .378 .009 .307 .049 -.756
Price Level -.299 .905 -.299 .314 .373 .253 .687
Price Flexibility .378 -.299 1.047 -.224 .004 .036 -.666
Manufacturer Image .009 .314 -.224 1.048 .256 .536 .514
Service .307 .373 .004 .256 .391 .199 .047
Salesforce Image .049 .253 .036 .536 .199 .506 .300
Product Quality -.756 .687 -.666 .514 .047 .300 1.713
Modified Rebuy Delivery Speed .986 -.554 .187 -.080 .215 .041 -.457
Price Level -.554 1.864 -1.232 .526 .645 .362 1.324
Price Flexibility .187 -1.232 1.704 -.590 -.521 -.328 -.346
Manufacturer Image -.080 .526 -.590 1.044 .223 .633 .192
Service .215 .645 -.521 .223 .428 .201 .416
Salesforce Image .041 .362 -.328 .633 .201 .485 .139
Product Quality -.457 1.324 -.346 .192 .416 .139 2.662
Straight Rebuy Delivery Speed .921 -.472 -.142 .025 .223 .092 -.012
Price Level -.472 .654 -.026 -.041 .096 -.126 .148
Price Flexibility -.142 -.026 .383 .203 -.092 .143 .056
Manufacturer Image .025 -.041 .203 1.628 -.019 .901 .457
Service .223 .096 -.092 -.019 .164 -.024 .036
Salesforce Image .092 -.126 .143 .901 -.024 .817 .243
Product Quality -.012 .148 .056 .457 .036 .243 1.818
Total Delivery Speed 1.744 -.551 .933 .075 .607 .079 -1.010
Price Level -.551 1.430 -.808 .368 .461 .172 .890
Price Flexibility .933 -.808 1.922 -.182 .069 -.037 -.985
Manufacturer Image .075 .368 -.182 1.280 .254 .687 .359
Service .607 .461 .069 .254 .564 .139 -.066
Salesforce Image .079 .172 -.037 .687 .139 .594 .217
Product Quality -1.010 .890 -.985 .359 -.066 .217 2.513
a. The total covariance matrix has 99 degrees of freedom.
Analysis 1
Box's Test of Equality of Covariance Matrices
Log Determinants
Type of Buying Situation Rank Log Determinant
New Task 3 -.370
Modified Rebuy 3 .265
Straight Rebuy 3 -2.056
Pooled within-groups 3 -.335
The ranks and natural logarithms of determinants printed are
those of the group covariance matrices.
Test Results
Box's M 39.316
F Approx. 3.129
df1 12
df2 4.525E4
Sig. .000
Tests null hypothesis of equal
population covariance matrices.
Stepwise Statistics
Variables Entered/Removeda,b,c,d
Step Entered
Min. D Squared
Statistic Between Groups
Exact F
Statistic df1 df2 Sig.
1 Delivery
Speed.916
New Task and
Modified Rebuy15.099 1 97.000 .000
2
Price Level 2.157
Modified Rebuy
and Straight
Rebuy
17.599 2 96.000 3.080E-7
3 Price
Flexibility4.090
New Task and
Modified Rebuy22.013 3 95.000 6.599E-11
At each step, the variable that maximizes the Mahalanobis distance between the two closest groups is entered.
a. Maximum number of steps is 14.
b. Maximum significance of F to enter is .05.
c. Minimum significance of F to remove is .10.
d. F level, tolerance, or VIN insufficient for further computation.
Variables in the Analysis
Step Tolerance
Sig. of F to
Remove Min. D Squared Between Groups
1 Delivery Speed 1.000 .000
2 Delivery Speed.822 .000 .047
New Task and
Straight Rebuy
Price Level.822 .000 .916
New Task and
Modified Rebuy
3 Delivery Speed.818 .000 1.571
New Task and
Modified Rebuy
Price Level.652 .000 .917
New Task and
Modified Rebuy
Price Flexibility
.777 .000 2.157
Modified Rebuy
and Straight
Rebuy
Variables Not in the Analysis
Step Tolerance Min. Tolerance Sig. of F to Enter Min. D Squared Between Groups
0 Delivery Speed1.000 1.000 .000 .916
New Task and
Modified Rebuy
Price Level1.000 1.000 .000 .047
New Task and
Straight Rebuy
Price Flexibility 1.000 1.000 .000 .025 New Task and
Modified Rebuy
Manufacturer Image1.000 1.000 .092 .063
New Task and
Straight Rebuy
Service1.000 1.000 .000 .002
Modified Rebuy
and Straight Rebuy
Salesforce Image1.000 1.000 .877 .003
Modified Rebuy
and Straight Rebuy
Product Quality1.000 1.000 .000 .044
New Task and
Modified Rebuy
1 Price Level.822 .822 .000 2.157
Modified Rebuy
and Straight Rebuy
Price Flexibility.980 .980 .000 .917
New Task and
Modified Rebuy
Manufacturer Image1.000 1.000 .106 1.226
New Task and
Modified Rebuy
Service.802 .802 .000 1.976
Modified Rebuy
and Straight Rebuy
Salesforce Image.994 .994 .858 .918
New Task and
Modified Rebuy
Product Quality.916 .916 .112 .921
New Task and
Modified Rebuy
2 Price Flexibility.777 .652 .000 4.090
New Task and
Modified Rebuy
Manufacturer Image.944 .776 .830 2.166
Modified Rebuy
and Straight Rebuy
Service.041 .041 .107 2.370
Modified Rebuy
and Straight Rebuy
Salesforce Image.931 .771 .507 2.160
Modified Rebuy
and Straight Rebuy
Product Quality.773 .694 .034 2.243
Modified Rebuy
and Straight Rebuy
3 Manufacturer Image.938 .631 .728 4.153
New Task and
Modified Rebuy
Service.041 .041 .143 4.170
New Task and
Modified Rebuy
Salesforce Image.929 .612 .447 4.251
New Task and
Modified Rebuy
Product Quality.773 .570 .101 4.441
New Task and
Modified Rebuy
Wilks' Lambda
Step
Number of
Variables Lambda df1 df2 df3
Exact F
Statistic df1 df2 Sig.
1 1 .541 1 2 97 41.093 2 97.000 .000
2 2 .391 2 2 97 28.756 4 192.000 .000
3 3 .253 3 2 97 31.295 6 190.000 .000
Summary of Canonical Discriminant Functions
Eigenvalues
Function Eigenvalue % of Variance Cumulative %
Canonical
Correlation
1 1.937a 84.9 84.9 .812
2 .346a 15.1 100.0 .507
a. First 2 canonical discriminant functions were used in the analysis.
Wilks' Lambda
Test of
Function(s) Wilks' Lambda Chi-square df Sig.
1 through 2 .253 131.954 6 .000
2 .743 28.517 2 .000
Standardized Canonical Discriminant
Function Coefficients
Function
1 2
Delivery Speed .821 .362
Price Level .651 .934
Price Flexibility .813 -.271
Structure Matrix
Function
1 2
Delivery Speed .661* -.070
Price Level -.076 .908*
Service .482 .817*
Price Flexibility .624 -.656*
Product Qualitya -.113 .389*
Manufacturer Imagea -.007 .248*
Salesforce Imagea .146 .224*
Pooled within-groups correlations between
discriminating variables and standardized canonical
discriminant functions
Variables ordered by absolute size of correlation
within function.
*. Largest absolute correlation between each variable
and any discriminant function
a. This variable not used in the analysis.
Canonical Discriminant Function
Coefficients
Function
1 2
Delivery Speed .836 .369
Price Level .613 .880
Price Flexibility .800 -.266
(Constant) -10.706 -1.274
Unstandardized coefficients
Functions at Group Centroids
Type of Buying
Situation
Function
1 2
New Task -1.636 -.416
Modified Rebuy -.054 .844
Straight Rebuy 1.687 -.378
Unstandardized canonical discriminant functions
evaluated at group means
Classification Statistics
Classification Processing Summary
Processed 100
Excluded Missing or out-of-range group
codes0
At least one missing
discriminating variable0
Used in Output 100
Prior Probabilities for Groups
Type of Buying
Situation Prior
Cases Used in Analysis
Unweighted Weighted
New Task .333 34 34.000
Modified Rebuy .333 32 32.000
Straight Rebuy .333 34 34.000
Total 1.000 100 100.000
Classification Function Coefficients
Type of Buying Situation
New Task Modified Rebuy Straight Rebuy
Delivery Speed 4.903 6.690 7.696
Price Level 8.413 10.492 10.485
Price Flexibility 10.364 11.294 13.013
(Constant) -52.968 -70.440 -88.667
Fisher's linear discriminant functions
Territorial MapCanonical DiscriminantFunction 2
-8,0 -6,0 -4,0 -2,0 ,0 2,0 4,0 6,0 8,0 ┼─────────┼─────────┼─────────┼─────────┼─────────┼─────────┼─────────┼─────────┼ 8,0 ┼ 12 23 ┼ │ 122 23 │ │ 112 223 │ │ 12 233 │ │ 122 23 │ │ 112 23 │ 6,0 ┼ ┼ 12 ┼ ┼ ┼ ┼ ┼ 23 ┼ ┼ │ 122 23 │ │ 112 223 │ │ 12 233 │ │ 122 23 │ │ 112 23 │ 4,0 ┼ ┼ 12 ┼ ┼ ┼ 23┼ ┼ ┼ │ 122 23 │ │ 112 223 │ │ 12 233 │ │ 122 23 │ │ 112 23 │ 2,0 ┼ ┼ ┼ 12┼ ┼ ┼23 ┼ ┼ ┼ │ 122 23 │ │ 112 223 │ │ 12 * 233 │ │ 122 23 │ │ 112 23 │ ,0 ┼ ┼ ┼ ┼ 12 ┼ 23 ┼ ┼ ┼ ┼ │ * 122 23 * │ │ 112223 │ │ 133 │ │ 13 │ │ 13 │ -2,0 ┼ ┼ ┼ ┼ 13 ┼ ┼ ┼ ┼ │ 13 │ │ 13 │ │ 13 │ │ 13 │ │ 13 │ -4,0 ┼ ┼ ┼ ┼ 13 ┼ ┼ ┼ ┼ │ 13 │ │ 13 │ │ 13 │ │ 13 │ │ 13 │ -6,0 ┼ ┼ ┼ ┼ 13 ┼ ┼ ┼ ┼ │ 13 │ │ 13 │ │ 13 │ │ 13 │ │ 13 │ -8,0 ┼ 13 ┼ ┼─────────┼─────────┼─────────┼─────────┼─────────┼─────────┼─────────┼─────────┼ -8,0 -6,0 -4,0 -2,0 ,0 2,0 4,0 6,0 8,0 Canonical Discriminant Function 1
Symbols used in territorial map
Symbol Group Label------ ----- --------------------
1 1 New Task 2 2 Modified Rebuy 3 3 Straight Rebuy
* Indicates a group centroid
Casewise Statistics
Case
Number Actual Group
Highest Group Second Highest Group Discriminant Scores
Predicted Group
P(D>d | G=g)
P(G=g | D=d)
Squared
Mahalanobis
Distance to
Centroid Group P(G=g | D=d)
Squared
Mahalanobis
Distance to
Centroid Function 1 Function 2p df
Original 1 1 1 .782 2 .915 .491 2 .077 5.450 -1.388 -1.072
2 1 1 .589 2 .896 1.057 2 .104 5.373 -2.319 .352
3 2 2 .090 2 .985 4.818 1 .009 14.256 -.112 3.038
4 1 1 .597 2 .981 1.030 2 .019 8.959 -2.154 -1.290
5 3 3 .626 2 .986 .937 2 .013 9.547 2.545 -.826
6 2 2 .639 2 .516 .896 1 .452 1.161 -.771 .226
7 1 3** .809 2 .937 .424 2 .062 5.853 2.214 .004
8 2 1** .226 2 .656 2.976 2 .343 4.274 -2.081 1.250
9 3 3 .777 2 .969 .504 2 .031 7.411 2.396 -.341
10 2 2 .779 2 .912 .500 1 .045 6.501 -.013 1.550
11 1 1 .417 2 .780 1.748 2 .147 5.092 -.676 -1.325
12 2 3** .881 2 .777 .254 2 .207 2.903 1.186 -.324
13 1 1 .626 2 .861 .938 2 .114 4.986 -1.024 -1.167
14 1 3** .287 2 .425 2.495 2 .325 3.033 .190 -.880
15 3 3 .781 2 .969 .496 2 .029 7.518 1.943 -1.034
16 3 3 .766 2 .845 .533 2 .132 4.242 1.126 -.844
17 2 2 .343 2 .884 2.137 1 .111 6.286 -.954 1.996
18 2 2 .574 2 .490 1.111 3 .455 1.259 .657 .066
19 3 3 .771 2 .977 .520 2 .023 8.060 2.346 -.671
20 3 3 .781 2 .969 .496 2 .029 7.518 1.943 -1.034
21 2 1** .278 2 .745 2.561 2 .136 5.970 -.513 -1.556
22 1 1 .276 2 .916 2.576 2 .053 8.265 -.976 -1.879
23 3 3 .421 2 .598 1.730 2 .398 2.543 1.538 .929
24 1 1 .670 2 .979 .802 2 .021 8.467 -2.418 -.854
25 2 3** .949 2 .856 .105 2 .135 3.804 1.379 -.478
26 3 2** .575 2 .487 1.107 3 .471 1.175 .750 .166
27 1 1 .634 2 .980 .911 2 .019 8.762 -2.498 -.827
28 3 3 .803 2 .974 .439 2 .025 7.779 2.201 -.796
29 3 3 .961 2 .907 .079 2 .091 4.666 1.860 -.156
30 2 2 .544 2 .938 1.218 1 .045 7.293 -.287 1.923
31 1 2** .378 2 .529 1.944 1 .467 2.193 -1.433 1.051
32 3 2** .517 2 .527 1.318 3 .460 1.592 1.088 .732
33 3 3 .786 2 .951 .481 2 .048 6.436 2.311 -.073
34 1 2** .724 2 .887 .647 1 .093 5.149 -.472 1.532
35 1 1 .499 2 .980 1.391 2 .019 9.276 -1.924 -1.560
36 1 1 .698 2 .596 .720 2 .392 1.556 -1.175 .296
37 2 2 .427 2 .927 1.704 1 .064 7.052 -.601 2.029
38 3 3 .275 2 .679 2.584 2 .162 5.453 .473 -1.431
39 1 1 .054 2 .999 5.828 2 .001 19.207 -3.896 -1.264
40 1 1 .772 2 .955 .518 2 .044 6.662 -2.351 -.334
41 1 2** .605 2 .506 1.005 1 .475 1.128 -.950 .394
42 3 3 .659 2 .985 .835 2 .015 9.244 2.461 -.863
43 3 3 .771 2 .935 .520 2 .065 5.866 2.244 .080
44 2 3** .850 2 .813 .324 2 .168 3.479 1.154 -.577
45 1 1 .759 2 .920 .552 2 .080 5.445 -2.238 .020
46 3 3 .570 2 .950 1.126 2 .050 7.014 2.530 .266
47 3 3 .620 2 .862 .956 2 .109 5.096 1.054 -1.123
48 2 2 .362 2 .868 2.034 1 .127 5.880 -.990 1.921
49 3 3 .607 2 .926 .999 2 .060 6.467 1.308 -1.303
50 3 3 .706 2 .716 .696 2 .279 2.581 1.499 .435
51 2 1** .340 2 .781 2.157 2 .132 5.712 -.621 -1.478
52 2 2 .400 2 .819 1.833 3 .174 4.931 .974 1.725
53 2 2 .271 2 .942 2.612 3 .052 8.413 .613 2.316
54 1 1 .877 2 .773 .263 2 .222 2.756 -1.531 .086
55 1 1 .167 2 .481 3.578 3 .329 4.337 -.076 -1.486
56 2 2 .644 2 .532 .880 1 .403 1.437 -.525 .033
57 2 2 .033 2 .647 6.798 3 .352 8.016 2.012 2.435
58 3 3 .311 2 .976 2.334 2 .024 9.715 3.025 .361
59 3 2** .618 2 .560 .963 3 .239 2.661 .075 -.128
60 2 2 .778 2 .874 .501 3 .098 4.885 .379 1.403
61 3 3 .599 2 .838 1.025 2 .126 4.821 .974 -1.096
62 3 3 .904 2 .936 .201 2 .063 5.612 2.086 -.172
63 3 3 .468 2 .818 1.518 2 .125 5.273 .839 -1.271
64 2 2 .254 2 .711 2.744 1 .287 4.558 -1.465 1.712
65 1 1 .423 2 .990 1.722 2 .009 11.026 -2.798 -1.026
66 2 3** .364 2 .611 2.021 2 .235 3.935 .448 -1.075
67 3 3 .922 2 .855 .162 2 .142 3.754 1.701 .024
68 2 2 .275 2 .666 2.580 1 .332 3.974 -1.487 1.572
69 3 3 .952 2 .842 .098 2 .153 3.507 1.570 -.087
70 2 2 .959 2 .759 .084 3 .143 3.425 .128 .619
71 2 2 .053 2 .986 5.869 3 .011 14.874 .338 3.235
72 3 3 .641 2 .814 .889 2 .184 3.862 1.888 .543
73 3 3 .958 2 .912 .085 2 .083 4.882 1.576 -.648
74 3 3 .967 2 .930 .068 2 .066 5.344 1.721 -.636
75 1 1 .968 2 .857 .066 2 .140 3.692 -1.689 -.166
76 2 3** .960 2 .893 .081 2 .105 4.362 1.800 -.117
77 2 1** .292 2 .662 2.465 2 .181 5.061 -.396 -1.380
78 3 3 .962 2 .845 .077 2 .147 3.578 1.410 -.353
79 1 1 .297 2 .994 2.425 2 .006 12.688 -3.023 -1.125
80 3 3 .781 2 .753 .494 2 .217 2.983 1.000 -.524
81 3 3 .890 2 .903 .233 2 .095 4.730 1.959 .021
82 2 2 .192 2 .960 3.300 1 .036 9.871 -.640 2.563
83 1 1 .447 2 .958 1.611 2 .042 7.846 -2.769 .155
84 1 2** .931 2 .767 .144 1 .189 2.945 -.432 .806
85 2 2 .726 2 .614 .641 3 .221 2.681 .110 .061
86 1 1 .567 2 .506 1.135 2 .482 1.230 -1.113 .512
87 2 2 .649 2 .566 .864 1 .293 2.183 -.199 -.074
88 1 2** .691 2 .597 .739 1 .384 1.621 -.882 .609
89 1 1 .794 2 .950 .463 2 .047 6.472 -1.704 -1.093
90 3 3 .904 2 .886 .202 2 .112 4.339 1.865 .035
91 2 2 .809 2 .648 .425 1 .247 2.357 -.238 .219
92 2 2 .605 2 .530 1.004 3 .375 1.696 .432 -.032
93 2 2 .148 2 .823 3.825 1 .175 6.916 -1.457 2.207
94 1 1 .128 2 .978 4.114 2 .022 11.738 -3.460 .471
95 1 1 .766 2 .952 .532 2 .045 6.636 -1.693 -1.143
96 1 1 .031 2 .999 6.950 2 .001 21.192 -4.102 -1.350
97 3 3 .751 2 .975 .573 2 .024 8.015 2.063 -1.035
98 1 1 .199 2 .962 3.227 2 .038 9.705 -3.155 .543
99 1 1 .885 2 .750 .245 2 .243 2.497 -1.403 .021
100 1 1 .313 2 .578 2.321 2 .237 4.104 -.310 -1.166
**. Misclassified case
Separate-Groups Graphs
GET TRANSLATE FILE='C:\Users\nn\Documents\New folder\Data multivariate\Data multivariate\logit.xls' /TYPE=XLS /MAP /FIELDNAMES .
Data written to the working file.3 variables and 24 cases written.Variable: perusahaan Type: Number Format: F14.2Variable: SIZE Type: Number Format: F11.2Variable: FP Type: Number Format: F11.2DATASET NAME DataSet4 WINDOW=FRONT.LOGISTIC REGRESSION VARIABLES perusahaan /METHOD=ENTER FP SIZE /CLASSPLOT /PRINT=GOODFIT CORR ITER(1)
/CRITERIA=PIN(0.05) POUT(0.10) ITERATE(20) CUT(0.5).
Logistic Regression
Notes
Output Created 07-Dec-2015 22:50:20
Comments
Input Active Dataset DataSet4
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data File 24
Missing Value Handling Definition of Missing User-defined missing values are treated as
missing
Syntax LOGISTIC REGRESSION VARIABLES
perusahaan
/METHOD=ENTER FP SIZE
/CLASSPLOT
/PRINT=GOODFIT CORR ITER(1)
/CRITERIA=PIN(0.05) POUT(0.10)
ITERATE(20) CUT(0.5).
Resources Processor Time 00:00:00.015
Elapsed Time 00:00:00.015
[DataSet4]
Case Processing Summary
Unweighted Casesa N Percent
Selected Cases Included in Analysis 24 100.0
Missing Cases 0 .0
Total 24 100.0
Unselected Cases 0 .0
Total 24 100.0
a. If weight is in effect, see classification table for the total number of cases.
Dependent Variable
Encoding
Original
Value Internal Value
0 0
1 1
Block 0: Beginning Block
Iteration Historya,b,c
Iteration -2 Log likelihood
Coefficients
Constant
Step 0 1 33.271 .000
a. Constant is included in the model.
b. Initial -2 Log Likelihood: 33,271
c. Estimation terminated at iteration number 1 because
parameter estimates changed by less than ,001.
Classification Tablea,b
Observed
Predicted
perusahaan Percentage
Correct0 1
Step 0 perusahaan 0 0 12 .0
1 0 12 100.0
Overall Percentage 50.0
a. Constant is included in the model.
b. The cut value is ,500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 0 Constant .000 .408 .000 1 1.000 1.000
Variables not in the Equation
Score df Sig.
Step 0 Variables FP 13.830 1 .000
SIZE 13.594 1 .000
Overall Statistics 16.551 2 .000
Block 1: Method = Enter
Iteration Historya,b,c,d
Iteration -2 Log likelihood
Coefficients
Constant FP SIZE
Step 1 1 14.620 -2.303 .885 1.793
2 12.223 -3.510 1.477 2.430
3 11.813 -4.213 1.816 2.883
4 11.789 -4.428 1.917 3.042
5 11.789 -4.445 1.924 3.055
6 11.789 -4.445 1.924 3.055
a. Method: Enter
b. Constant is included in the model.
c. Initial -2 Log Likelihood: 33,271
d. Estimation terminated at iteration number 6 because parameter estimates
changed by less than ,001.
Omnibus Tests of Model Coefficients
Chi-square df Sig.
Step 1 Step 21.482 2 .000
Block 21.482 2 .000
Model 21.482 2 .000
Model Summary
Step -2 Log likelihood
Cox & Snell R
Square
Nagelkerke R
Square
1 11.789a .591 .789
a. Estimation terminated at iteration number 6 because parameter
estimates changed by less than ,001.
Hosmer and Lemeshow Test
Step Chi-square df Sig.
1 10.450 8 .235
Contingency Table for Hosmer and Lemeshow Test
perusahaan = ,00 perusahaan = 1,00 Total
Observed Expected Observed Expected
Step 1 1 2 1.971 0 .029 2
2 2 1.951 0 .049 2
3 2 1.909 0 .091 2
4 2 1.859 0 .141 2
5 2 1.817 0 .183 2
6 1 1.362 1 .638 2
7 0 .698 2 1.302 2
8 0 .243 2 1.757 2
9 1 .104 1 1.896 2
10 0 .086 6 5.914 6
Classification Tablea
Observed
Predicted
perusahaan Percentage
Correct0 1
Step 1 perusahaan 0 11 1 91.7
1 1 11 91.7
Overall Percentage 91.7
a. The cut value is ,500
Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 1a FP 1.924 .912 4.457 1 .035 6.851
SIZE 3.055 1.598 3.655 1 .056 21.226
Constant -4.445 1.843 5.816 1 .016 .012
a. Variable(s) entered on step 1: FP, SIZE.
Correlation Matrix
Constant FP SIZE
Step 1 Constant 1.000 -.852 -.427
FP -.852 1.000 .131
SIZE -.427 .131 1.000
Step number: 1
Observed Groups and Predicted Probabilities
4 ┼ ┼ │ │ │ │F │ │R 3 ┼ 1 ┼E │ 1 │Q │ 1 │U │ 1 │E 2 ┼ 00 0 0 1 11┼N │ 00 0 0 1 11│C │ 00 0 0 1 11│Y │ 00 0 0 1 11│ 1 ┼ 00 00 00 0 1 1 1 1 10 111┼ │ 00 00 00 0 1 1 1 1 10 111│ │ 00 00 00 0 1 1 1 1 10 111│ │ 00 00 00 0 1 1 1 1 10 111│Predicted ─────────┼─────────┼─────────┼─────────┼─────────┼─────────┼─────────┼─────────┼─────────┼────────── Prob: 0 ,1 ,2 ,3 ,4 ,5 ,6 ,7 ,8 ,9 1 Group: 0000000000000000000000000000000000000000000000000011111111111111111111111111111111111111111111111111
Predicted Probability is of Membership for 1,00 The Cut Value is ,50 Symbols: 0 - ,00 1 - 1,00
Each Symbol Represents ,25 Cases.
GET FILE='C:\Users\nn\Documents\New folder\Data multivariate\Data multivariate\hatco.sav'.DATASET NAME DataSet5 WINDOW=FRONT.FACTOR /VARIABLES x1 x2 x3 x4 x5 x6 x7 /MISSING LISTWISE /ANALYSIS x1 x2 x3 x4 x5 x6 x7 /PRINT UNIVARIATE INITIAL CORRELATION SIG DET KMO INV REPR AIC EXTRACTION ROTATION /CRITERIA MINEIGEN(1) ITERATE(25) /EXTRACTION PC /CRITERIA ITERATE(25) /ROTATION VARIMAX
/METHOD=CORRELATION.
Factor Analysis
Notes
Output Created 07-Dec-2015 22:53:22
Comments
Input Data C:\Users
n\Documents\New folder\Data
multivariate\Data multivariate\hatco.sav
Active Dataset DataSet5
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data File 100
Missing Value Handling Definition of Missing MISSING=EXCLUDE: User-defined missing
values are treated as missing.
Cases Used LISTWISE: Statistics are based on cases
with no missing values for any variable
used.
Syntax FACTOR
/VARIABLES x1 x2 x3 x4 x5 x6 x7
/MISSING LISTWISE
/ANALYSIS x1 x2 x3 x4 x5 x6 x7
/PRINT UNIVARIATE INITIAL
CORRELATION SIG DET KMO INV REPR
AIC EXTRACTION ROTATION
/CRITERIA MINEIGEN(1) ITERATE(25)
/EXTRACTION PC
/CRITERIA ITERATE(25)
/ROTATION VARIMAX
/METHOD=CORRELATION.
Resources Processor Time 00:00:00.078
Elapsed Time 00:00:00.088
Maximum Memory Required 7204 (7,035K) bytes
[DataSet5]
Descriptive Statistics
Mean Std. Deviation Analysis N
Delivery Speed 3.515 1.3207 100
Price Level 2.364 1.1957 100
Price Flexibility 7.894 1.3865 100
Manufacturer Image 5.248 1.1314 100
Service 2.916 .7513 100
Salesforce Image 2.665 .7709 100
Product Quality 6.971 1.5852 100
Correlation Matrixa
Delivery Speed Price Level Price Flexibility
Manufacturer
Image Service Salesforce Image Product Quality
Correlation Delivery Speed 1.000 -.349 .509 .050 .612 .077 -.483
Price Level -.349 1.000 -.487 .272 .513 .186 .470
Price Flexibility .509 -.487 1.000 -.116 .067 -.034 -.448
Manufacturer Image .050 .272 -.116 1.000 .299 .788 .200
Service .612 .513 .067 .299 1.000 .241 -.055
Salesforce Image .077 .186 -.034 .788 .241 1.000 .177
Product Quality -.483 .470 -.448 .200 -.055 .177 1.000
Sig. (1-tailed) Delivery Speed .000 .000 .309 .000 .223 .000
Price Level .000 .000 .003 .000 .032 .000
Price Flexibility .000 .000 .125 .255 .367 .000
Manufacturer Image .309 .003 .125 .001 .000 .023
Service .000 .000 .255 .001 .008 .293
Salesforce Image .223 .032 .367 .000 .008 .039
Product Quality .000 .000 .000 .023 .293 .039
a. Determinant = ,003
Inverse of Correlation Matrix
Delivery Speed Price Level Price Flexibility
Manufacturer
Image Service Salesforce Image Product Quality
Delivery Speed 35.747 32.158 .140 1.507 -38.694 -.590 -.122
Price Level 32.158 31.597 1.118 1.277 -36.298 -.413 -1.005
Price Flexibility .140 1.118 1.645 .207 -.775 -.179 .227
Manufacturer Image 1.507 1.277 .207 2.879 -1.942 -2.134 -.084
Service -38.694 -36.298 -.775 -1.942 43.834 .562 .735
Salesforce Image -.590 -.413 -.179 -2.134 .562 2.697 -.191
Product Quality -.122 -1.005 .227 -.084 .735 -.191 1.606
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .446
Bartlett's Test of Sphericity Approx. Chi-Square 567.541
df 21
Sig. .000
Anti-image Matrices
Delivery Speed Price Level Price Flexibility
Manufacturer
Image Service Salesforce Image Product Quality
Anti-image Covariance Delivery Speed .028 .028 .002 .015 -.025 -.006 -.002
Price Level .028 .032 .022 .014 -.026 -.005 -.020
Price Flexibility .002 .022 .608 .044 -.011 -.040 .086
Manufacturer Image .015 .014 .044 .347 -.015 -.275 -.018
Service -.025 -.026 -.011 -.015 .023 .005 .010
Salesforce Image -.006 -.005 -.040 -.275 .005 .371 -.044
Product Quality -.002 -.020 .086 -.018 .010 -.044 .623
Anti-image Correlation Delivery Speed .344a .957 .018 .149 -.978 -.060 -.016
Price Level .957 .330a .155 .134 -.975 -.045 -.141
Price Flexibility .018 .155 .913a .095 -.091 -.085 .140
Manufacturer Image .149 .134 .095 .558a -.173 -.766 -.039
Service -.978 -.975 -.091 -.173 .288a .052 .088
Salesforce Image -.060 -.045 -.085 -.766 .052 .552a -.092
Product Quality -.016 -.141 .140 -.039 .088 -.092 .927a
a. Measures of Sampling Adequacy(MSA)
Communalities
Initial Extraction
Delivery Speed 1.000 .884
Price Level 1.000 .895
Price Flexibility 1.000 .649
Manufacturer Image 1.000 .885
Service 1.000 .995
Salesforce Image 1.000 .901
Product Quality 1.000 .618
Extraction Method: Principal Component Analysis.
Total Variance Explained
Compon
ent
Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings
Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative %
1 2.526 36.082 36.082 2.526 36.082 36.082 2.379 33.984 33.984
2 2.120 30.291 66.374 2.120 30.291 66.374 1.827 26.098 60.082
3 1.181 16.873 83.246 1.181 16.873 83.246 1.622 23.165 83.246
4 .541 7.731 90.977
5 .418 5.972 96.949
6 .204 2.920 99.869
7 .009 .131 100.000
Extraction Method: Principal Component Analysis.
Component Matrixa
Component
1 2 3
Delivery Speed -.528 .752 .202
Price Level .792 .093 .508
Price Flexibility -.692 .374 -.173
Manufacturer Image .564 .602 -.452
Service .186 .779 .595
Salesforce Image .492 .604 -.542
Product Quality .739 -.270 -.005
Extraction Method: Principal Component Analysis.
a. 3 components extracted.
Reproduced Correlations
Delivery Speed Price Level Price Flexibility
Manufacturer
Image Service Salesforce Image Product Quality
Reproduced Correlation Delivery Speed .884a -.246 .612 .063 .608 .084 -.594
Price Level -.246 .895a -.601 .273 .522 .171 .557
Price Flexibility .612 -.601 .649a -.087 .060 -.021 -.611
Manufacturer Image .063 .273 -.087 .885a .305 .886 .257
Service .608 .522 .060 .305 .995a .240 -.076
Salesforce Image .084 .171 -.021 .886 .240 .901a .203
Product Quality -.594 .557 -.611 .257 -.076 .203 .618a
Residualb Delivery Speed -.104 -.103 -.013 .004 -.007 .111
Price Level -.104 .114 .000 -.009 .015 -.088
Price Flexibility -.103 .114 -.029 .006 -.014 .163
Manufacturer Image -.013 .000 -.029 -.006 -.098 -.057
Service .004 -.009 .006 -.006 .001 .021
Salesforce Image -.007 .015 -.014 -.098 .001 -.026
Product Quality .111 -.088 .163 -.057 .021 -.026
Extraction Method: Principal Component Analysis.
a. Reproduced communalities
b. Residuals are computed between observed and reproduced correlations. There are 8 (38,0%) nonredundant residuals with absolute values greater than 0.05.
Rotated Component Matrixa
Component
1 2 3
Delivery Speed -.752 .071 .560
Price Level .754 .108 .561
Price Flexibility -.806 .006 .010
Manufacturer Image .117 .921 .153
Service -.062 .176 .980
Salesforce Image .034 .945 .077
Product Quality .760 .193 -.064
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 5 iterations.
Component Transformation Matrix
Compon
ent 1 2 3
1 .865 .477 .159
2 -.452 .602 .658
3 .218 -.641 .736
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser
Normalization.