bab 11, 12 & 15

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


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


evaluated at group means

Classification Statistics

Classification Processing Summary

Processed 24


codes0


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


Discriminant

Notes


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>









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



TCOV


/PLOT=CASES




[DataSet2] C:\Users\nn\Documents\New folder\Data multivariate\Data multivariate\hatco.sav

Warnings

All-Groups Stacked Histogram is no longer displayed.



Valid 100 100.0


codes0 .0






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


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



Service 2.916 .7513 100 100.000


Product Quality 6.971 1.5852 100 100.000



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


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



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



Specification Buying Delivery Speed Price Level Price Flexibility

Manufacturer


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


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


Service .607 .461 .069 .254 .564 .139 -.066


Product Quality -1.010 .890 -.985 .359 -.066 .217 2.513

a. The total covariance matrix has 99 degrees of freedom.

Analysis 1


Log Determinants

Specification Buying Rank Log Determinant

Specification Buying 3 -.383

Total Value Analysis 3 .744

Pooled within-groups 3 .445



Test Results

Box's M 14.651

F Approx. 2.356

df1 6

df2 4.704E4

Sig. .028



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


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


Eigenvalues


Canonical

Correlation

1 1.934a 100.0 100.0 .812


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



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



(Constant) -1.624


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



Processed 100


codes0



Used in Output 100


Specification Buying Prior


Unweighted Weighted

Specification Buying .500 40 40.000

Total Value Analysis .500 60 60.000

Total 1.000 100 100.000


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


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


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



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


Discriminant

Notes


Comments

Input Data C:\Users




Filter <none>

Weight <none>

Split File <none>









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



TCOV


/PLOT=CASES




[DataSet3]



Valid 100 100.0


codes0 .0






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



Service 2.229 .6250 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



Service 3.284 .6541 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


Service 3.256 .4054 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



Service 2.916 .7513 100 100.000


Product Quality 6.971 1.5852 100 100.000



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



Manufacturer


Covariance Delivery Speed .964 -.439 .140 -.014 .249 .061 -.408

Price Level -.439 1.126 -.504 .261 .366 .159 .707



Service .249 .366 -.197 .152 .326 .124 .161


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



Service .445 .604 -.339 .239 1.000 .279 .197


Product Quality -.290 .465 -.219 .245 .197 .206 1.000



Type of Buying Situation Delivery Speed Price Level Price Flexibility

Manufacturer


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


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


Service .215 .645 -.521 .223 .428 .201 .416


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



Service .607 .461 .069 .254 .564 .139 -.066


Product Quality -1.010 .890 -.985 .359 -.066 .217 2.513

a. The total covariance matrix has 99 degrees of freedom.

Analysis 1


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



Test Results

Box's M 39.316

F Approx. 3.129

df1 12

df2 4.525E4

Sig. .000



Stepwise Statistics

Variables Entered/Removeda,b,c,d

Step Entered

Min. D Squared

Statistic Between Groups

Exact F


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


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


Eigenvalues


Canonical

Correlation

1 1.937a 84.9 84.9 .812

2 .346a 15.1 100.0 .507


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


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


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



Processed 100


codes0



Used in Output 100


Type of Buying

Situation Prior


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


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


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


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



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


Comments

Input Active Dataset DataSet4

Filter <none>

Weight <none>

Split File <none>



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).



[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


Comments

Input Data C:\Users




Filter <none>

Weight <none>

Split File <none>


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.



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


Manufacturer


Correlation Delivery Speed 1.000 -.349 .509 .050 .612 .077 -.483

Price Level -.349 1.000 -.487 .272 .513 .186 .470



Service .612 .513 .067 .299 1.000 .241 -.055


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


Manufacturer


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


Manufacturer


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


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


a. 3 components extracted.

Reproduced Correlations


Manufacturer


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


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


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


Rotation Method: Varimax with Kaiser

Normalization.