7/28/2019 L3 Output Wiyono

1/46

Lampiran 3RELIABILITY/VARIABLES=X1.1 X1.2 X1.3 X1.4 X1.5 X1.6 X1.7 X1.8 X1.9 X1.10/SCALE('Tingkat pendidikan (X1)') ALL/MODEL=ALPHA/STATISTICS=DESCRIPTIVE CORR

/SUMMARY=TOTAL CORR.

Reliability

Notes

Output Created 28-MAY-2013 15:24:17

Comments

Input

Active Dataset DataSet0Filter Weight Split File N of Rows in WorkingData File

24

Matrix Input

Missing Value Handling

Definition of MissingUser-defined missingvalues are treated asmissing.

Cases UsedStatistics are based on allcases with valid data for allvariables in the procedure.

Syntax

RELIABILITY/VARIABLES=X1.1 X1.2X1.3 X1.4 X1.5 X1.6 X1.7X1.8 X1.9 X1.10/SCALE('Tingkat

pendidikan (X1)') ALL/MODEL=ALPHA

/STATISTICS=DESCRIPTIVE CORR/SUMMARY=TOTAL

CORR.

ResourcesProcessor Time 00:00:00,03

Elapsed Time 00:00:00,04

[DataSet0]

Warnings

The determinant of the covariance matrix is zero orapproximately zero. Statistics based on its inverse matrix cannotbe computed and they are displayed as system missing values.

Scale: Tingkat pendidikan (X1)

7/28/2019 L3 Output Wiyono

2/46

Case Processing Summary

N %

Cases

Valid 24 100,0

Excludeda 0 ,0

Total 24 100,0

a. Listwise deletion based on all variables inthe procedure.

Reliability Statistics

Cronbach'sAlpha

Cronbach'sAlpha Based

onStandardized

Items

N ofItems

,960 ,973 10

Item Statistics

Mean Std.Deviation

N

X1.1 4,7083 ,46431 24X1.2 4,8750 ,33783 24X1.3 4,8750 ,33783 24X1.4 4,8750 ,33783 24X1.5 4,7500 ,67566 24X1.6 4,8750 ,33783 24

X1.7 4,7083 ,46431 24X1.8 4,8750 ,33783 24X1.9 4,8750 ,33783 24X1.10

4,7917 ,41485 24

Inter-Item Correlation Matrix

X1.1 X1.2 X1.3 X1.4 X1.5 X1.6 X1.7 X1.8

X1.1 1,000 ,589 ,589 ,589 ,589 ,589 ,193 ,589X1.2 ,589 1,000 1,000 1,000 1,000 1,000 ,589 1,000X1.3 ,589 1,000 1,000 1,000 1,000 1,000 ,589 1,000X1.4 ,589 1,000 1,000 1,000 1,000 1,000 ,589 1,000

X1.5 ,589 1,000 1,000 1,000 1,000 1,000 ,589 1,000X1.6 ,589 1,000 1,000 1,000 1,000 1,000 ,589 1,000X1.7 ,193 ,589 ,589 ,589 ,589 ,589 1,000 ,589X1.8 ,589 1,000 1,000 1,000 1,000 1,000 ,589 1,000X1.9 ,589 1,000 1,000 1,000 1,000 1,000 ,589 1,000X1.10

,348 ,737 ,737 ,737 ,737 ,737 ,348 ,737

Inter-Item Correlation Matrix

X1.9 X1.10

X1.1 ,589 ,348X1.2 1,000 ,737X1.3 1,000 ,737X1.4 1,000 ,737X1.5 1,000 ,737X1.6 1,000 ,737

7/28/2019 L3 Output Wiyono

3/46

X1.7 ,589 ,348X1.8 1,000 ,737X1.9 1,000 ,737X1.10 ,737 1,000

Summary Item StatisticsMean Minimu

mMaximu

mRange Maximum /

MinimumVarianc

e

Inter-Item Correlations ,784 ,193 1,000 ,807 5,174 ,051

Summary Item Statistics

N of Items

Inter-Item Correlations 10

Item-Total Statistics

Scale Mean if

Item Deleted

Scale

Variance ifItem Deleted

Corrected

Item-TotalCorrelation

Squared

MultipleCorrelation

Cronbach's

Alpha if ItemDeleted

X1.1 43,5000 10,957 ,552 . ,968X1.2 43,3333 10,580 ,989 . ,952X1.3 43,3333 10,580 ,989 . ,952X1.4 43,3333 10,580 ,989 . ,952X1.5 43,4583 8,520 ,987 . ,955X1.6 43,3333 10,580 ,989 . ,952X1.7 43,5000 10,957 ,552 . ,968X1.8 43,3333 10,580 ,989 . ,952X1.9 43,3333 10,580 ,989 . ,952X1.10

43,4167 10,775 ,705 . ,961

NEW FILE.DATASET NAME DataSet1 WINDOW=FRONT.RELIABILITY/VARIABLES=X2.1 X2.2 X2.3 X2.4 X2.5 X2.6 X2.7 X2.8 X2.9 X2.10/SCALE('Pengalaman mengajar (X2)') ALL/MODEL=ALPHA/STATISTICS=DESCRIPTIVE CORR/SUMMARY=TOTAL CORR.

Reliability

Notes

Output Created 28-MAY-2013 15:25:36

Comments

Input

Active Dataset DataSet1Filter Weight Split File N of Rows in Working

Data File

24

Matrix Input

7/28/2019 L3 Output Wiyono

4/46

Missing Value Handling

Definition of MissingUser-defined missingvalues are treated asmissing.

Cases UsedStatistics are based on allcases with valid data for allvariables in the procedure.

Syntax

RELIABILITY

/VARIABLES=X2.1 X2.2X2.3 X2.4 X2.5 X2.6 X2.7X2.8 X2.9 X2.10/SCALE('Pengalaman

mengajar (X2)') ALL/MODEL=ALPHA

/STATISTICS=DESCRIPTIVE CORR/SUMMARY=TOTAL

CORR.

ResourcesProcessor Time 00:00:00,00

Elapsed Time 00:00:00,01

[DataSet1]

Warnings

The determinant of the covariance matrix is zero orapproximately zero. Statistics based on its inverse matrix cannotbe computed and they are displayed as system missing values.

Scale: pengalaman mengajar (X2)

Case Processing Summary

N %

Cases

Valid 24 100,0

Excludeda 0 ,0

Total 24 100,0

a. Listwise deletion based on all variables inthe procedure.

Reliability Statistics

Cronbach'sAlpha

Cronbach'sAlpha Based

onStandardized

Items

N ofItems

,938 ,951 10

Item Statistics

7/28/2019 L3 Output Wiyono

5/46

Mean Std.Deviation

N

X2.1 4,3750 ,87539 24X2.2 4,3750 1,05552 24X2.3 4,9167 ,28233 24X2.4 4,7917 ,41485 24X2.5 4,6667 ,76139 24X2.6 4,8750 ,33783 24X2.7 4,3750 1,05552 24X2.8 4,6667 ,76139 24X2.9 4,3750 1,05552 24X2.10

4,7917 ,41485 24

Inter-Item Correlation Matrix

X2.1 X2.2 X2.3 X2.4 X2.5 X2.6 X2.7 X2.8

X2.1 1,000 ,641 ,132 ,224 ,718 ,165 ,641 ,718X2.2 ,641 1,000 ,693 ,782 ,812 ,625 1,000 ,812X2.3 ,132 ,693 1,000 ,588 ,674 ,798 ,693 ,674X2.4 ,224 ,782 ,588 1,000 ,321 ,737 ,782 ,321X2.5 ,718 ,812 ,674 ,321 1,000 ,507 ,812 1,000X2.6 ,165 ,625 ,798 ,737 ,507 1,000 ,625 ,507X2.7 ,641 1,000 ,693 ,782 ,812 ,625 1,000 ,812X2.8 ,718 ,812 ,674 ,321 1,000 ,507 ,812 1,000X2.9 ,641 1,000 ,693 ,782 ,812 ,625 1,000 ,812X2.10

,224 ,782 ,588 1,000 ,321 ,737 ,782 ,321

Inter-Item Correlation Matrix

X2.9 X2.10

X2.1 ,641 ,224X2.2 1,000 ,782

X2.3 ,693 ,588X2.4 ,782 1,000X2.5 ,812 ,321X2.6 ,625 ,737X2.7 1,000 ,782X2.8 ,812 ,321X2.9 1,000 ,782X2.10 ,782 1,000

Summary Item Statistics

Mean Minimum

Maximum

Range Maximum /Minimum

Variance

Inter-Item Correlations ,660 ,132 1,000 ,868 7,579 ,051

Summary Item Statistics

N of Items

Inter-Item Correlations 10

Item-Total Statistics

Scale Mean ifItem Deleted

ScaleVariance if

Item Deleted

CorrectedItem-TotalCorrelation

SquaredMultiple

Correlation

Cronbach'sAlpha if Item

Deleted

X2.1 41,8333 30,493 ,616 . ,939

X2.2 41,8333 25,623 ,981 . ,919X2.3 41,2917 34,824 ,694 . ,940X2.4 41,4167 33,732 ,687 . ,937

7/28/2019 L3 Output Wiyono

6/46

X2.5 41,5417 29,650 ,842 . ,927X2.6 41,3333 34,580 ,635 . ,940X2.7 41,8333 25,623 ,981 . ,919X2.8 41,5417 29,650 ,842 . ,927X2.9 41,8333 25,623 ,981 . ,919X2.10

41,4167 33,732 ,687 . ,937

NEW FILE.DATASET NAME DataSet2 WINDOW=FRONT.RELIABILITY/VARIABLES=Y1.1 Y1.2 Y1.3 Y1.4 Y1.5 Y1.6 Y1.7 Y1.8 Y1.9 Y1.10/SCALE('Variabel Y1 (Iklim kerja)') ALL/MODEL=ALPHA/STATISTICS=DESCRIPTIVE CORR/SUMMARY=TOTAL CORR.

Reliability

Notes

Output Created 28-MAY-2013 15:28:39

Comments

Input

Active Dataset DataSet2Filter Weight Split File

N of Rows in WorkingData File

24

Matrix Input

Missing Value Handling

Definition of MissingUser-defined missingvalues are treated asmissing.

Cases UsedStatistics are based on allcases with valid data for allvariables in the procedure.

Syntax

RELIABILITY/VARIABLES=Y1.1 Y1.2

Y1.3 Y1.4 Y1.5 Y1.6 Y1.7Y1.8 Y1.9 Y1.10/SCALE('Variabel Y1

(Iklim kerja)') ALL/MODEL=ALPHA

/STATISTICS=DESCRIPTIVE CORR/SUMMARY=TOTAL

CORR.

ResourcesProcessor Time 00:00:00,00

Elapsed Time 00:00:00,01

[DataSet2]

7/28/2019 L3 Output Wiyono

7/46

Warnings

The determinant of the covariance matrix is zero orapproximately zero. Statistics based on its inverse matrix cannotbe computed and they are displayed as system missing values.

Scale: Variabel Y1 (iklim kerja)

Case Processing Summary

N %

Cases

Valid 24 100,0

Excludeda 0 ,0

Total 24 100,0

a. Listwise deletion based on all variables inthe procedure.

Reliability Statistics

Cronbach'sAlpha

Cronbach'sAlpha Based

onStandardized

Items

N ofItems

,943 ,956 10

Item Statistics

Mean Std.Deviation

N

Y1.1 4,5417 ,72106 24Y1.2 4,6250 ,82423 24Y1.3 4,8750 ,33783 24Y1.4 4,8333 ,38069 24Y1.5 4,6667 ,76139 24Y1.6 4,8750 ,33783 24Y1.7 4,5000 ,83406 24Y1.8 4,7500 ,60792 24Y1.9 4,6250 ,82423 24Y1.10

4,7917 ,41485 24

Inter-Item Correlation Matrix

Y1.1 Y1.2 Y1.3 Y1.4 Y1.5 Y1.6 Y1.7 Y1.8

Y1.1 1,000 ,649 ,290 ,343 ,660 ,290 ,542 ,719Y1.2 ,649 1,000 ,605 ,762 ,762 ,605 ,917 ,846Y1.3 ,290 ,605 1,000 ,845 ,845 1,000 ,540 ,688Y1.4 ,343 ,762 ,845 1,000 ,700 ,845 ,685 ,564Y1.5 ,660 ,762 ,845 ,700 1,000 ,845 ,685 ,939Y1.6 ,290 ,605 1,000 ,845 ,845 1,000 ,540 ,688Y1.7 ,542 ,917 ,540 ,685 ,685 ,540 1,000 ,772Y1.8 ,719 ,846 ,688 ,564 ,939 ,688 ,772 1,000Y1.9 ,649 1,000 ,605 ,762 ,762 ,605 ,917 ,846Y1.10

,248 ,652 ,737 ,872 ,596 ,737 ,565 ,474

7/28/2019 L3 Output Wiyono

8/46

Inter-Item Correlation Matrix

Y1.9 Y1.10

Y1.1 ,649 ,248Y1.2 1,000 ,652Y1.3 ,605 ,737

Y1.4 ,762 ,872Y1.5 ,762 ,596Y1.6 ,605 ,737Y1.7 ,917 ,565Y1.8 ,846 ,474Y1.9 1,000 ,652Y1.10 ,652 1,000

Summary Item Statistics

Mean Minimum

Maximum

Range Maximum /Minimum

Variance

Inter-Item Correlations ,686 ,248 1,000 ,752 4,027 ,031

Summary Item Statistics

N of Items

Inter-Item Correlations 10

Item-Total Statistics

Scale Mean ifItem Deleted

ScaleVariance if

Item Deleted

CorrectedItem-TotalCorrelation

SquaredMultiple

Correlation

Cronbach'sAlpha if Item

Deleted

Y1.1 42,5417 22,259 ,613 . ,946Y1.2 42,4583 19,476 ,934 . ,929

Y1.3 42,2083 24,346 ,747 . ,942Y1.4 42,2500 23,848 ,795 . ,940Y1.5 42,4167 20,341 ,878 . ,932Y1.6 42,2083 24,346 ,747 . ,942Y1.7 42,5833 19,993 ,839 . ,935Y1.8 42,3333 21,536 ,894 . ,932Y1.9 42,4583 19,476 ,934 . ,929Y1.10

42,2917 24,042 ,672 . ,943

NEW FILE.DATASET NAME DataSet3 WINDOW=FRONT.RELIABILITY/VARIABLES=Y2.1 Y2.2 Y2.3 Y2.4 Y2.5 Y2.6 Y2.7 Y2.8 Y2.9 Y2.10

/SCALE('Variabel Y2. (Profesionalisme guru)') ALL/MODEL=ALPHA/STATISTICS=DESCRIPTIVE CORR/SUMMARY=TOTAL CORR.

Reliability

Notes

Output Created 28-MAY-2013 15:30:22

7/28/2019 L3 Output Wiyono

9/46

Comments

Input

Active Dataset DataSet3Filter Weight Split File N of Rows in WorkingData File

24

Matrix Input

Missing Value Handling

Definition of MissingUser-defined missingvalues are treated asmissing.

Cases UsedStatistics are based on allcases with valid data for allvariables in the procedure.

Syntax

RELIABILITY/VARIABLES=Y2.1 Y2.2

Y2.3 Y2.4 Y2.5 Y2.6 Y2.7Y2.8 Y2.9 Y2.10/SCALE('Variabel Y2.

(Profesionalisme guru)')ALL

/MODEL=ALPHA/STATISTICS=DESCRIPTIVE CORR/SUMMARY=TOTAL

CORR.

ResourcesProcessor Time 00:00:00,00

Elapsed Time 00:00:00,01

[DataSet3]

Warnings

The determinant of the covariance matrix is zero orapproximately zero. Statistics based on its inverse matrix cannotbe computed and they are displayed as system missing values.

Scale: Variabel Y2. (profesionalisme guru)

Case Processing Summary

N %

Cases

Valid 24 100,0

Excludeda 0 ,0

Total 24 100,0

a. Listwise deletion based on all variables inthe procedure.

Reliability Statistics

7/28/2019 L3 Output Wiyono

10/46

Cronbach'sAlpha

Cronbach'sAlpha Based

onStandardized

Items

N ofItems

,981 ,990 10

Item Statistics

Mean Std.Deviation

N

Y2.1 4,8750 ,33783 24Y2.2 4,7917 ,41485 24Y2.3 4,8750 ,33783 24Y2.4 4,8750 ,33783 24Y2.5 4,7500 ,67566 24Y2.6 4,8750 ,33783 24Y2.7 4,8750 ,33783 24Y2.8 4,8750 ,33783 24Y2.9 4,8750 ,33783 24Y2.10

4,7917 ,41485 24

Inter-Item Correlation Matrix

Y2.1 Y2.2 Y2.3 Y2.4 Y2.5 Y2.6 Y2.7 Y2.8

Y2.1 1,000 ,737 1,000 1,000 1,000 1,000 1,000 1,000Y2.2 ,737 1,000 ,737 ,737 ,737 ,737 ,737 ,737Y2.3 1,000 ,737 1,000 1,000 1,000 1,000 1,000 1,000Y2.4 1,000 ,737 1,000 1,000 1,000 1,000 1,000 1,000Y2.5 1,000 ,737 1,000 1,000 1,000 1,000 1,000 1,000Y2.6 1,000 ,737 1,000 1,000 1,000 1,000 1,000 1,000Y2.7 1,000 ,737 1,000 1,000 1,000 1,000 1,000 1,000

Y2.8 1,000 ,737 1,000 1,000 1,000 1,000 1,000 1,000Y2.9 1,000 ,737 1,000 1,000 1,000 1,000 1,000 1,000Y2.10

,737 1,000 ,737 ,737 ,737 ,737 ,737 ,737

Inter-Item Correlation Matrix

Y2.9 Y2.10

Y2.1 1,000 ,737Y2.2 ,737 1,000Y2.3 1,000 ,737Y2.4 1,000 ,737Y2.5 1,000 ,737Y2.6 1,000 ,737

Y2.7 1,000 ,737Y2.8 1,000 ,737Y2.9 1,000 ,737Y2.10 ,737 1,000

Summary Item Statistics

Mean Minimum

Maximum

Range Maximum /Minimum

Variance

Inter-Item Correlations ,906 ,737 1,000 ,263 1,357 ,016

Summary Item Statistics

N of Items

Inter-Item Correlations 10

7/28/2019 L3 Output Wiyono

11/46

Item-Total Statistics

Scale Mean ifItem Deleted

ScaleVariance if

Item Deleted

CorrectedItem-TotalCorrelation

SquaredMultiple

Correlation

Cronbach'sAlpha if Item

Deleted

Y2.1 43,5833 11,297 ,986 . ,977Y2.2 43,6667 11,275 ,791 . ,983Y2.3 43,5833 11,297 ,986 . ,977Y2.4 43,5833 11,297 ,986 . ,977Y2.5 43,7083 9,172 ,983 . ,985Y2.6 43,5833 11,297 ,986 . ,977Y2.7 43,5833 11,297 ,986 . ,977Y2.8 43,5833 11,297 ,986 . ,977Y2.9 43,5833 11,297 ,986 . ,977Y2.10

43,6667 11,275 ,791 . ,983

DATASET ACTIVATE DataSet0.

DATASET CLOSE DataSet3.DATASET ACTIVATE DataSet0.DATASET CLOSE DataSet2.DATASET ACTIVATE DataSet0.DATASET CLOSE DataSet1.NEW FILE.DATASET NAME DataSet4 WINDOW=FRONT.DATASET ACTIVATE DataSet4.DATASET CLOSE DataSet0.NPAR TESTS/K-S(NORMAL)=Variabel_X1 Variabel_Y1/MISSING ANALYSIS.

NPar Tests

Notes

Output Created 28-MAY-2013 15:36:10

Comments

Input

Active Dataset DataSet4Filter

Weight Split File N of Rows in WorkingData File

24

Missing Value Handling

Definition of MissingUser-defined missingvalues are treated asmissing.

Cases Used

Statistics for each test arebased on all cases withvalid data for thevariable(s) used in thattest.

Syntax

NPAR TESTS/K-

S(NORMAL)=Variabel_X1Variabel_Y1/MISSING ANALYSIS.

7/28/2019 L3 Output Wiyono

12/46

Resources

Processor Time 00:00:00,00

Elapsed Time 00:00:00,03

Number of CasesAlloweda

157286

a. Based on availability of workspace memory.

[DataSet4]

One-Sample Kolmogorov-Smirnov Test

Variabel_X1

Variabel_Y1

N 24 24

Normal Parametersa,bMean 48,2083 47,0833Std.

Deviation3,58717 5,19127

Most Extreme DifferencesAbsolute ,462 ,436Positive ,309 ,287Negative -,462 -,436

Kolmogorov-Smirnov Z 2,265 2,134Asymp. Sig. (2-tailed) ,000 ,000

a. Test distribution is Normal.b. Calculated from data.

REGRESSION/MISSING LISTWISE/STATISTICS COEFF OUTS R ANOVA

/CRITERIA=PIN(.05) POUT(.10)/NOORIGIN/DEPENDENT Variabel_Y1/METHOD=ENTER Variabel_X1/SAVE RESID.

Regression

Notes

Output Created 28-MAY-2013 15:36:48

Comments

Input

Active Dataset DataSet4Filter Weight Split File N of Rows in WorkingData File

24

Missing Value Handling Definition of Missing User-defined missingvalues are treated asmissing.

7/28/2019 L3 Output Wiyono

13/46

Cases Used

Statistics are based oncases with no missingvalues for any variableused.

Syntax

REGRESSION/MISSING LISTWISE/STATISTICS COEFF

OUTS R ANOVA/CRITERIA=PIN(.05)

POUT(.10)/NOORIGIN/DEPENDENT

Variabel_Y1/METHOD=ENTER

Variabel_X1/SAVE RESID.

Resources

Processor Time 00:00:00,05Elapsed Time 00:00:00,06Memory Required 1356 bytesAdditional MemoryRequired for Residual

Plots

0 bytes

Variables Created orModified

RES_1 Unstandardized Residual

[DataSet4]

Variables Entered/Removeda

Model

VariablesEntered

VariablesRemoved

Method

1 Variabel_X1b

. Enter

a. Dependent Variable: Variabel_Y1b. All requested variables entered.

Model Summaryb

Model

R RSquare

Adjusted RSquare

Std. Error ofthe Estimate

1 ,564a ,318 ,287 4,38300

a. Predictors: (Constant), Variabel_X1b. Dependent Variable: Variabel_Y1

ANOVAa

Model Sum of Squares

df MeanSquare

F Sig.

1

Regression 197,198 1 197,198 10,265 ,004b

Residual 422,635 22 19,211

Total 619,833 23

a. Dependent Variable: Variabel_Y1b. Predictors: (Constant), Variabel_X1

7/28/2019 L3 Output Wiyono

14/46

Coefficientsa

Model Unstandardized Coefficients StandardizedCoefficients

t Sig.

B Std. Error Beta

1

(Constant) 7,732 12,315 ,628 ,537

Variabel_X

1 ,816 ,255 ,564 3,204 ,004

a. Dependent Variable: Variabel_Y1

Residuals Statisticsa

Minimum Maximum

Mean Std.Deviation

N

Predicted Value 39,5668 48,5458 47,0833 2,92811 24

Residual-

15,729552,27045 ,00000 4,28666 24

Std. PredictedValue

-2,567 ,499 ,000 1,000 24

Std. Residual -3,589 ,518 ,000 ,978 24

a. Dependent Variable: Variabel_Y1

DESCRIPTIVES VARIABLES=Variabel_X1 Variabel_Y1 RES_1/STATISTICS=KURTOSIS SKEWNESS.

Descriptives

Notes

Output Created 28-MAY-2013 15:37:22

Comments

Input

Active Dataset DataSet4Filter Weight Split File N of Rows in WorkingData File

24

Missing Value HandlingDefinition of Missing

User defined missing

values are treated asmissing.

Cases UsedAll non-missing data areused.

Syntax

DESCRIPTIVESVARIABLES=Variabel_X1Variabel_Y1 RES_1/STATISTICS=KURTOSISSKEWNESS.

ResourcesProcessor Time 00:00:00,02

Elapsed Time 00:00:00,01

[DataSet4]

7/28/2019 L3 Output Wiyono

15/46

Descriptive Statistics

N Skewness Kurtosis

Statistic Statistic Std.Error

Statistic Std.Error

Variabel_X1 24 -2,337 ,472 3,961 ,918Variabel_Y1 24 -1,834 ,472 2,233 ,918Unstandardized Residual 24 -3,233 ,472 9,790 ,918

Valid N (listwise) 24

COMPUTE Abresid=ABS(RES_1).EXECUTE.REGRESSION/MISSING LISTWISE/STATISTICS COEFF OUTS R ANOVA COLLIN TOL ZPP/CRITERIA=PIN(.05) POUT(.10)

/NOORIGIN/DEPENDENT Abresid/METHOD=ENTER Variabel_X1/RESIDUALS DURBIN HISTOGRAM(ZRESID)/SAVE RESID.

Regression

Notes

Output Created 28-MAY-2013 15:38:26

Comments

Input

Active Dataset DataSet4Filter Weight Split File N of Rows in WorkingData File

24

Missing Value Handling

Definition of MissingUser-defined missingvalues are treated asmissing.

Cases UsedStatistics are based oncases with no missingvalues for any variableused.

7/28/2019 L3 Output Wiyono

16/46

Syntax

REGRESSION/MISSING LISTWISE/STATISTICS COEFF

OUTS R ANOVA COLLINTOL ZPP/CRITERIA=PIN(.05)

POUT(.10)

/NOORIGIN/DEPENDENT Abresid/METHOD=ENTER

Variabel_X1/RESIDUALS DURBIN

HISTOGRAM(ZRESID)/SAVE RESID.

Resources

Processor Time 00:00:02,57Elapsed Time 00:00:02,36Memory Required 1396 bytesAdditional MemoryRequired for ResidualPlots

328 bytes

Variables Created or

Modified

RES_2 Unstandardized Residual

[DataSet4]

Variables Entered/Removeda

Model

VariablesEntered

VariablesRemoved

Method

1 Variabel_X1b . Enter

a. Dependent Variable: Abresidb. All requested variables entered.

Model Summaryb

Model

R RSquare

Adjusted RSquare

Std. Error ofthe Estimate

Durbin-Watson

1 ,155a ,024 -,020 3,57490 2,233

a. Predictors: (Constant), Variabel_X1b. Dependent Variable: Abresid

ANOVAa

Model Sum of Squares

df MeanSquare

F Sig.

1

Regression 6,964 1 6,964 ,545 ,468b

Residual 281,159 22 12,780

Total 288,123 23

a. Dependent Variable: Abresidb. Predictors: (Constant), Variabel_X1

Coefficientsa

7/28/2019 L3 Output Wiyono

17/46

Model Unstandardized Coefficients StandardizedCoefficients

t Sig.

B Std. Error Beta

1

(Constant) -5,028 10,044 -,501 ,622

Variabel_X1

,153 ,208 ,155 ,738 ,468

Coefficientsa

Model Correlations Collinearity Statistics

Zero-order Partial Part Tolerance VIF

1(Constant)

Variabel_X1 ,155 ,155 ,155 1,000 1,000

a. Dependent Variable: Abresid

Collinearity Diagnosticsa

Model

Dimension Eigenvalue

ConditionIndex

Variance Proportions(Constant

)Variabel_X

1

11 1,997 1,000 ,00 ,00

2 ,003 27,493 1,00 1,00

a. Dependent Variable: Abresid

Residuals Statisticsa

Minimum

Maximum

Mean Std.Deviation

N

Predicted Value ,9548 2,6423 2,3674 ,55028 24Residual -1,21841 13,24069 ,00000 3,49633 24Std. PredictedValue

-2,567 ,499 ,000 1,000 24

Std. Residual -,341 3,704 ,000 ,978 24

a. Dependent Variable: Abresid

Charts

7/28/2019 L3 Output Wiyono

18/46

NEW FILE.DATASET NAME DataSet5 WINDOW=FRONT.NPAR TESTS/K-S(NORMAL)=Variabel_X1 Variabel_Y2/MISSING ANALYSIS.

NPar Tests

Notes

Output Created 28-MAY-2013 15:40:36

Comments

Input

Active Dataset DataSet5Filter Weight Split File N of Rows in WorkingData File

24

Missing Value Handling Definition of Missing User-defined missing

values are treated asmissing.

7/28/2019 L3 Output Wiyono

19/46

Cases Used

Statistics for each test arebased on all cases withvalid data for thevariable(s) used in thattest.

Syntax

NPAR TESTS/K-

S(NORMAL)=Variabel_X1Variabel_Y2/MISSING ANALYSIS.

Resources

Processor Time 00:00:00,00

Elapsed Time 00:00:00,01

Number of CasesAlloweda

157286

a. Based on availability of workspace memory.

[DataSet5]

One-Sample Kolmogorov-Smirnov Test

Variabel_X1

Variabel_Y2

N 24 24

Normal Parametersa,bMean 48,2083 48,4583Std.Deviation

3,58717 3,69464

Most Extreme DifferencesAbsolute ,462 ,453Positive ,309 ,338Negative -,462 -,453

Kolmogorov-Smirnov Z 2,265 2,221Asymp. Sig. (2-tailed) ,000 ,000

a. Test distribution is Normal.b. Calculated from data.

REGRESSION/MISSING LISTWISE/STATISTICS COEFF OUTS R ANOVA/CRITERIA=PIN(.05) POUT(.10)/NOORIGIN/DEPENDENT Variabel_Y2/METHOD=ENTER Variabel_X1

/SAVE RESID.

Regression

Notes

Output Created 28-MAY-2013 15:41:11

CommentsInput Active Dataset DataSet5Filter

7/28/2019 L3 Output Wiyono

20/46

Weight Split File N of Rows in WorkingData File

24

Missing Value Handling

Definition of MissingUser-defined missingvalues are treated asmissing.

Cases Used

Statistics are based oncases with no missingvalues for any variableused.

Syntax

REGRESSION/MISSING LISTWISE/STATISTICS COEFF

OUTS R ANOVA/CRITERIA=PIN(.05)

POUT(.10)/NOORIGIN/DEPENDENT

Variabel_Y2/METHOD=ENTER

Variabel_X1/SAVE RESID.

Resources

Processor Time 00:00:00,06Elapsed Time 00:00:00,07Memory Required 1356 bytesAdditional MemoryRequired for ResidualPlots

0 bytes

Variables Created orModified

RES_1 Unstandardized Residual

[DataSet5]

Variables Entered/Removeda

Model

VariablesEntered

VariablesRemoved

Method

1 Variabel_X1b . Enter

a. Dependent Variable: Variabel_Y2b. All requested variables entered.

Model Summary

b

Model

R RSquare

Adjusted RSquare

Std. Error ofthe Estimate

1 ,986a ,973 ,972 ,61879

a. Predictors: (Constant), Variabel_X1b. Dependent Variable: Variabel_Y2

ANOVAa

Model Sum of Squares

df MeanSquare

F Sig.

1 Regression 305,535 1 305,535 797,952 ,000b

Residual 8,424 22 ,383

7/28/2019 L3 Output Wiyono

21/46

Total 313,958 23

a. Dependent Variable: Variabel_Y2b. Predictors: (Constant), Variabel_X1

Coefficientsa

Model Unstandardized Coefficients StandardizedCoefficients

t Sig.

B Std. Error Beta

1

(Constant) -,524 1,739 -,301 ,766

Variabel_X1

1,016 ,036 ,986 28,248 ,000

a. Dependent Variable: Variabel_Y2

Residuals Statisticsa

Minimum

Maximum

Mean Std.Deviation

N

Predicted Value 39,1022 50,2788 48,4583 3,64474 24Residual -1,26271 ,73729 ,00000 ,60519 24Std. PredictedValue

-2,567 ,499 ,000 1,000 24

Std. Residual -2,041 1,192 ,000 ,978 24

a. Dependent Variable: Variabel_Y2

DESCRIPTIVES VARIABLES=Variabel_X1 Variabel_Y2 RES_1/STATISTICS=KURTOSIS SKEWNESS.

Descriptives

Notes

Output Created 28-MAY-2013 15:41:33

Comments

Input

Active Dataset DataSet5Filter Weight Split File N of Rows in WorkingData File

24

Missing Value HandlingDefinition of Missing

User defined missingvalues are treated asmissing.

Cases UsedAll non-missing data areused.

Syntax

DESCRIPTIVESVARIABLES=Variabel_X1Variabel_Y2 RES_1/STATISTICS=KURTOSISSKEWNESS.

7/28/2019 L3 Output Wiyono

22/46

ResourcesProcessor Time 00:00:00,00

Elapsed Time 00:00:00,01

[DataSet5]

Descriptive Statistics

N Skewness Kurtosis

Statistic Statistic Std.Error

Statistic Std.Error

Variabel_X1 24 -2,337 ,472 3,961 ,918Variabel_Y2 24 -2,322 ,472 3,873 ,918Unstandardized Residual 24 -,227 ,472 -,272 ,918

Valid N (listwise) 24

COMPUTE Abresid=ABS(RES_1).EXECUTE.REGRESSION/MISSING LISTWISE/STATISTICS COEFF OUTS R ANOVA COLLIN TOL ZPP/CRITERIA=PIN(.05) POUT(.10)/NOORIGIN/DEPENDENT Abresid/METHOD=ENTER Variabel_X1/RESIDUALS DURBIN HISTOGRAM(ZRESID)/SAVE RESID.

Regression

Notes

Output Created 28-MAY-2013 15:42:28

Comments

Input

Active Dataset DataSet5Filter Weight

Split File N of Rows in WorkingData File

24

Missing Value Handling

Definition of MissingUser-defined missingvalues are treated asmissing.

Cases Used

Statistics are based oncases with no missingvalues for any variableused.

7/28/2019 L3 Output Wiyono

23/46

Syntax

REGRESSION/MISSING LISTWISE/STATISTICS COEFF

OUTS R ANOVA COLLINTOL ZPP/CRITERIA=PIN(.05)

POUT(.10)

/NOORIGIN/DEPENDENT Abresid/METHOD=ENTER

Variabel_X1/RESIDUALS DURBIN

HISTOGRAM(ZRESID)/SAVE RESID.

Resources

Processor Time 00:00:00,42Elapsed Time 00:00:00,39Memory Required 1396 bytesAdditional MemoryRequired for ResidualPlots

328 bytes

Variables Created or

Modified

RES_2 Unstandardized Residual

[DataSet5]

Variables Entered/Removeda

Model

VariablesEntered

VariablesRemoved

Method

1 Variabel_X1b . Enter

a. Dependent Variable: Abresidb. All requested variables entered.

Model Summaryb

Model

R RSquare

Adjusted RSquare

Std. Error ofthe Estimate

Durbin-Watson

1 ,335a ,112 ,072 ,32548 1,806

a. Predictors: (Constant), Variabel_X1b. Dependent Variable: Abresid

ANOVAa

Model Sum of Squares

df MeanSquare

F Sig.

1

Regression ,295 1 ,295 2,782 ,110b

Residual 2,331 22 ,106

Total 2,625 23

a. Dependent Variable: Abresidb. Predictors: (Constant), Variabel_X1

Coefficientsa

7/28/2019 L3 Output Wiyono

24/46

Model Unstandardized Coefficients StandardizedCoefficients

t Sig.

B Std. Error Beta

1

(Constant) -1,030 ,914 -1,126 ,272

Variabel_X1

,032 ,019 ,335 1,668 ,110

Coefficientsa

Model Correlations Collinearity Statistics

Zero-order Partial Part Tolerance VIF

1(Constant)

Variabel_X1 ,335 ,335 ,335 1,000 1,000

a. Dependent Variable: Abresid

Collinearity Diagnosticsa

Model

Dimension Eigenvalue

ConditionIndex

Variance Proportions(Constant

)Variabel_X

1

11 1,997 1,000 ,00 ,00

2 ,003 27,493 1,00 1,00

a. Dependent Variable: Abresid

Residuals Statisticsa

Minimum

Maximum

Mean Std.Deviation

N

Predicted Value ,2010 ,5481 ,4915 ,11319 24Residual -,26931 ,74620 ,00000 ,31833 24Std. PredictedValue

-2,567 ,499 ,000 1,000 24

Std. Residual -,827 2,293 ,000 ,978 24

a. Dependent Variable: Abresid

Charts

7/28/2019 L3 Output Wiyono

25/46

DATASET ACTIVATE DataSet5.DATASET CLOSE DataSet4.NEW FILE.DATASET NAME DataSet6 WINDOW=FRONT.DATASET ACTIVATE DataSet6.DATASET CLOSE DataSet5.NPAR TESTS/K-S(NORMAL)=Variabel_X2 Variabel_Y1/MISSING ANALYSIS.

NPar Tests

Notes

Output Created 28-MAY-2013 15:44:24

Comments

Input Active Dataset DataSet6Filter Weight Split File

7/28/2019 L3 Output Wiyono

26/46

N of Rows in WorkingData File

24

Missing Value Handling

Definition of MissingUser-defined missingvalues are treated asmissing.

Cases Used

Statistics for each test arebased on all cases with

valid data for thevariable(s) used in thattest.

Syntax

NPAR TESTS/K-

S(NORMAL)=Variabel_X2Variabel_Y1/MISSING ANALYSIS.

Resources

Processor Time 00:00:00,02

Elapsed Time 00:00:00,01

Number of CasesAlloweda

157286

a. Based on availability of workspace memory.

[DataSet6]

One-Sample Kolmogorov-Smirnov Test

Variabel_X2

Variabel_Y1

N 24 24

Normal Parametersa,bMean 46,2083 47,0833Std.Deviation 6,10046 5,19127

Most Extreme DifferencesAbsolute ,385 ,436Positive ,267 ,287Negative -,385 -,436

Kolmogorov-Smirnov Z 1,885 2,134Asymp. Sig. (2-tailed) ,002 ,000

a. Test distribution is Normal.b. Calculated from data.

REGRESSION/MISSING LISTWISE/STATISTICS COEFF OUTS R ANOVA

/CRITERIA=PIN(.05) POUT(.10)/NOORIGIN/DEPENDENT Variabel_Y1/METHOD=ENTER Variabel_X2/SAVE RESID.

Regression

7/28/2019 L3 Output Wiyono

27/46

Notes

Output Created 28-MAY-2013 15:44:44

Comments

Input

Active Dataset DataSet6Filter Weight

Split File N of Rows in WorkingData File

24

Missing Value Handling

Definition of MissingUser-defined missingvalues are treated asmissing.

Cases Used

Statistics are based oncases with no missingvalues for any variableused.

Syntax

REGRESSION/MISSING LISTWISE/STATISTICS COEFF

OUTS R ANOVA/CRITERIA=PIN(.05)

POUT(.10)/NOORIGIN/DEPENDENT

Variabel_Y1/METHOD=ENTER

Variabel_X2/SAVE RESID.

Resources

Processor Time 00:00:00,03Elapsed Time 00:00:00,04Memory Required 1356 bytesAdditional MemoryRequired for ResidualPlots

0 bytes

Variables Created or

ModifiedRES_1 Unstandardized Residual

[DataSet6]

Variables Entered/Removeda

Model

VariablesEntered

VariablesRemoved

Method

1 Variabel_X2b . Enter

a. Dependent Variable: Variabel_Y1b. All requested variables entered.

Model Summaryb

Model

R RSquare

Adjusted RSquare

Std. Error ofthe Estimate

1 ,695a ,484 ,460 3,81398

a. Predictors: (Constant), Variabel_X2b. Dependent Variable: Variabel_Y1

ANOVAa

7/28/2019 L3 Output Wiyono

28/46

Model Sum of Squares

df MeanSquare

F Sig.

1

Regression 299,812 1 299,812 20,611 ,000b

Residual 320,021 22 14,546

Total 619,833 23

a. Dependent Variable: Variabel_Y1b. Predictors: (Constant), Variabel_X2

Coefficientsa

Model Unstandardized Coefficients StandardizedCoefficients

t Sig.

B Std. Error Beta

1

(Constant) 19,736 6,074 3,249 ,004

Variabel_X2

,592 ,130 ,695 4,540 ,000

a. Dependent Variable: Variabel_Y1

Residuals Statisticsa

Minimum

Maximum

Mean Std.Deviation

N

Predicted Value 38,6744 49,3274 47,0833 3,61044 24Residual -6,67439 10,32561 ,00000 3,73014 24Std. PredictedValue

-2,329 ,622 ,000 1,000 24

Std. Residual -1,750 2,707 ,000 ,978 24

a. Dependent Variable: Variabel_Y1

DESCRIPTIVES VARIABLES=Variabel_X2 Variabel_Y1 RES_1/STATISTICS=KURTOSIS SKEWNESS.

Descriptives

Notes

Output Created 28-MAY-2013 15:45:08

Comments

Input

Active Dataset DataSet6Filter Weight Split File N of Rows in WorkingData File

24

Missing Value HandlingDefinition of Missing

User defined missingvalues are treated asmissing.

Cases UsedAll non-missing data areused.

7/28/2019 L3 Output Wiyono

29/46

Syntax

DESCRIPTIVESVARIABLES=Variabel_X2Variabel_Y1 RES_1/STATISTICS=KURTOSISSKEWNESS.

Resources

Processor Time 00:00:00,02

Elapsed Time 00:00:00,02

[DataSet6]

Descriptive Statistics

N Skewness Kurtosis

Statistic Statistic Std.Error

Statistic Std.Error

Variabel_X2 24 -1,424 ,472 ,658 ,918Variabel_Y1 24 -1,834 ,472 2,233 ,918Unstandardized Residual 24 ,826 ,472 2,656 ,918

Valid N (listwise) 24

COMPUTE Abresid=ABS(RES_1).EXECUTE.REGRESSION/MISSING LISTWISE/STATISTICS COEFF OUTS R ANOVA COLLIN TOL ZPP/CRITERIA=PIN(.05) POUT(.10)/NOORIGIN/DEPENDENT Abresid

/METHOD=ENTER Variabel_X2/RESIDUALS DURBIN HISTOGRAM(ZRESID)/SAVE RESID.

Regression

Notes

Output Created 28-MAY-2013 15:46:09

Comments

Input

Active Dataset DataSet6Filter Weight Split File N of Rows in WorkingData File

24

Missing Value Handling

Definition of MissingUser-defined missingvalues are treated asmissing.

Cases Used

Statistics are based oncases with no missing

values for any variableused.

7/28/2019 L3 Output Wiyono

30/46

Syntax

REGRESSION/MISSING LISTWISE/STATISTICS COEFF

OUTS R ANOVA COLLINTOL ZPP/CRITERIA=PIN(.05)

POUT(.10)

/NOORIGIN/DEPENDENT Abresid/METHOD=ENTER

Variabel_X2/RESIDUALS DURBIN

HISTOGRAM(ZRESID)/SAVE RESID.

Resources

Processor Time 00:00:00,41Elapsed Time 00:00:00,33Memory Required 1396 bytesAdditional MemoryRequired for ResidualPlots

328 bytes

Variables Created or

Modified

RES_2 Unstandardized Residual

[DataSet6]

Variables Entered/Removeda

Model

VariablesEntered

VariablesRemoved

Method

1 Variabel_X2b . Enter

a. Dependent Variable: Abresidb. All requested variables entered.

Model Summaryb

Model

R RSquare

Adjusted RSquare

Std. Error ofthe Estimate

Durbin-Watson

1 ,937a ,877 ,872 1,06511 1,983

a. Predictors: (Constant), Variabel_X2b. Dependent Variable: Abresid

ANOVAa

Model Sum of Squares

df MeanSquare

F Sig.

1

Regression 178,231 1 178,231 157,106 ,000b

Residual 24,958 22 1,134

Total 203,189 23

a. Dependent Variable: Abresidb. Predictors: (Constant), Variabel_X2

Coefficientsa

7/28/2019 L3 Output Wiyono

31/46

Model Unstandardized Coefficients StandardizedCoefficients

t Sig.

B Std. Error Beta

1

(Constant) 23,292 1,696 13,732 ,000

Variabel_X2

-,456 ,036 -,937 -12,534 ,000

Coefficientsa

Model Correlations Collinearity Statistics

Zero-order Partial Part Tolerance VIF

1(Constant)

Variabel_X2 -,937 -,937 -,937 1,000 1,000

a. Dependent Variable: Abresid

Collinearity Diagnosticsa

Model

Dimension Eigenvalue

ConditionIndex

Variance Proportions(Constant

)Variabel_X

2

11 1,992 1,000 ,00 ,00

2 ,008 15,539 1,00 1,00

a. Dependent Variable: Abresid

Residuals Statisticsa

Minimum

Maximum

Mean Std.Deviation

N

Predicted Value ,4762 8,6898 2,2064 2,78373 24Residual -2,01545 2,51417 ,00000 1,04170 24Std. PredictedValue

-,622 2,329 ,000 1,000 24

Std. Residual -1,892 2,360 ,000 ,978 24

a. Dependent Variable: Abresid

Charts

7/28/2019 L3 Output Wiyono

32/46

NEW FILE.DATASET NAME DataSet7 WINDOW=FRONT.DATASET ACTIVATE DataSet7.DATASET CLOSE DataSet6.NPAR TESTS/K-S(NORMAL)=Variabel_X2 Variabel_Y2/MISSING ANALYSIS.

NPar Tests

Notes

Output Created 28-MAY-2013 15:49:16

Comments

Input

Active Dataset DataSet7Filter Weight Split File N of Rows in Working

Data File 24

7/28/2019 L3 Output Wiyono

33/46

Missing Value Handling

Definition of MissingUser-defined missingvalues are treated asmissing.

Cases Used

Statistics for each test arebased on all cases withvalid data for thevariable(s) used in that

test.

Syntax

NPAR TESTS/K-

S(NORMAL)=Variabel_X2Variabel_Y2/MISSING ANALYSIS.

Resources

Processor Time 00:00:00,00

Elapsed Time 00:00:00,01

Number of CasesAlloweda

157286

a. Based on availability of workspace memory.

[DataSet7]

One-Sample Kolmogorov-Smirnov Test

Variabel_X2

Variabel_Y2

N 24 24

Normal Parametersa,bMean 46,2083 48,4583Std.Deviation

6,10046 3,69464

Most Extreme DifferencesAbsolute ,385 ,453Positive ,267 ,338Negative -,385 -,453

Kolmogorov-Smirnov Z 1,885 2,221Asymp. Sig. (2-tailed) ,002 ,000

a. Test distribution is Normal.b. Calculated from data.

REGRESSION/MISSING LISTWISE/STATISTICS COEFF OUTS R ANOVA/CRITERIA=PIN(.05) POUT(.10)/NOORIGIN/DEPENDENT Variabel_Y2/METHOD=ENTER Variabel_X2/SAVE RESID.

Regression

Notes

Output Created 28-MAY-2013 15:49:32

7/28/2019 L3 Output Wiyono

34/46

Comments

Input

Active Dataset DataSet7Filter Weight Split File N of Rows in WorkingData File

24

Missing Value Handling

Definition of MissingUser-defined missingvalues are treated asmissing.

Cases Used

Statistics are based oncases with no missingvalues for any variableused.

Syntax

REGRESSION/MISSING LISTWISE/STATISTICS COEFF

OUTS R ANOVA/CRITERIA=PIN(.05)

POUT(.10)/NOORIGIN

/DEPENDENTVariabel_Y2/METHOD=ENTER

Variabel_X2/SAVE RESID.

Resources

Processor Time 00:00:00,05Elapsed Time 00:00:00,06Memory Required 1356 bytesAdditional MemoryRequired for ResidualPlots

0 bytes

Variables Created orModified

RES_1 Unstandardized Residual

[DataSet7]

Variables Entered/Removeda

Model

VariablesEntered

VariablesRemoved

Method

1 Variabel_X2b . Enter

a. Dependent Variable: Variabel_Y2b. All requested variables entered.

Model Summaryb

Model

R RSquare

Adjusted RSquare

Std. Error ofthe Estimate

1 ,399a ,159 ,121 3,46436

a. Predictors: (Constant), Variabel_X2b. Dependent Variable: Variabel_Y2

ANOVAa

Model Sum of Squares

df MeanSquare

F Sig.

7/28/2019 L3 Output Wiyono

35/46

1

Regression 49,919 1 49,919 4,159 ,054b

Residual 264,040 22 12,002

Total 313,958 23

a. Dependent Variable: Variabel_Y2

b. Predictors: (Constant), Variabel_X2

Coefficientsa

Model Unstandardized Coefficients StandardizedCoefficients

t Sig.

B Std. Error Beta

1

(Constant) 37,299 5,517 6,761 ,000

Variabel_X2

,241 ,118 ,399 2,039 ,054

a. Dependent Variable: Variabel_Y2

Residuals Statisticsa

Minimum

Maximum

Mean Std.Deviation

N

Predicted Value 45,0271 49,3740 48,4583 1,47322 24Residual -8,68354 3,76542 ,00000 3,38821 24Std. PredictedValue

-2,329 ,622 ,000 1,000 24

Std. Residual -2,507 1,087 ,000 ,978 24

a. Dependent Variable: Variabel_Y2

DESCRIPTIVES VARIABLES=Variabel_X2 Variabel_Y2 RES_1/STATISTICS=KURTOSIS SKEWNESS.

Descriptives

Notes

Output Created 28-MAY-2013 15:49:58

Comments

Input

Active Dataset DataSet7Filter Weight Split File N of Rows in WorkingData File

24

Missing Value HandlingDefinition of Missing

User defined missingvalues are treated asmissing.

Cases UsedAll non-missing data areused.

7/28/2019 L3 Output Wiyono

36/46

Syntax

DESCRIPTIVESVARIABLES=Variabel_X2Variabel_Y2 RES_1/STATISTICS=KURTOSISSKEWNESS.

Resources

Processor Time 00:00:00,00

Elapsed Time 00:00:00,01

[DataSet7]

Descriptive Statistics

N Skewness Kurtosis

Statistic Statistic Std.Error

Statistic Std.Error

Variabel_X2 24 -1,424 ,472 ,658 ,918Variabel_Y2 24 -2,322 ,472 3,873 ,918Unstandardized Residual 24 -1,942 ,472 3,117 ,918

Valid N (listwise) 24

COMPUTE Abresid=ABS(RES_1).EXECUTE.REGRESSION/MISSING LISTWISE/STATISTICS COEFF OUTS R ANOVA COLLIN TOL ZPP/CRITERIA=PIN(.05) POUT(.10)/NOORIGIN/DEPENDENT Abresid

/METHOD=ENTER Variabel_X2/RESIDUALS DURBIN HISTOGRAM(ZRESID)/SAVE RESID.

Regression

Notes

Output Created 28-MAY-2013 15:50:55

Comments

Input

Active Dataset DataSet7Filter Weight Split File N of Rows in WorkingData File

24

Missing Value Handling

Definition of MissingUser-defined missingvalues are treated asmissing.

Cases Used

Statistics are based oncases with no missing

values for any variableused.

7/28/2019 L3 Output Wiyono

37/46

Syntax

REGRESSION/MISSING LISTWISE/STATISTICS COEFF

OUTS R ANOVA COLLINTOL ZPP/CRITERIA=PIN(.05)

POUT(.10)

/NOORIGIN/DEPENDENT Abresid/METHOD=ENTER

Variabel_X2/RESIDUALS DURBIN

HISTOGRAM(ZRESID)/SAVE RESID.

Resources

Processor Time 00:00:00,41Elapsed Time 00:00:00,37Memory Required 1396 bytesAdditional MemoryRequired for ResidualPlots

328 bytes

Variables Created or

Modified

RES_2 Unstandardized Residual

[DataSet7]

Variables Entered/Removeda

Model

VariablesEntered

VariablesRemoved

Method

1 Variabel_X2b . Enter

a. Dependent Variable: Abresidb. All requested variables entered.

Model Summaryb

Model

R RSquare

Adjusted RSquare

Std. Error ofthe Estimate

Durbin-Watson

1 ,614a ,377 ,348 2,12343 2,100

a. Predictors: (Constant), Variabel_X2b. Dependent Variable: Abresid

ANOVAa

Model Sum of Squares

df MeanSquare

F Sig.

1

Regression 59,969 1 59,969 13,300 ,001b

Residual 99,197 22 4,509

Total 159,166 23

a. Dependent Variable: Abresidb. Predictors: (Constant), Variabel_X2

Coefficientsa

7/28/2019 L3 Output Wiyono

38/46

Model Unstandardized Coefficients StandardizedCoefficients

t Sig.

B Std. Error Beta

1

(Constant) 14,321 3,382 4,235 ,000

Variabel_X2

-,265 ,073 -,614 -3,647 ,001

Coefficientsa

Model Correlations Collinearity Statistics

Zero-order Partial Part Tolerance VIF

1(Constant)

Variabel_X2 -,614 -,614 -,614 1,000 1,000

a. Dependent Variable: Abresid

Collinearity Diagnosticsa

Model

Dimension Eigenvalue

ConditionIndex

Variance Proportions(Constant

)Variabel_X

2

11 1,992 1,000 ,00 ,00

2 ,008 15,539 1,00 1,00

a. Dependent Variable: Abresid

Residuals Statisticsa

Minimum

Maximum

Mean Std.Deviation

N

Predicted Value 1,0868 5,8512 2,0904 1,61473 24Residual -2,87832 5,74394 ,00000 2,07675 24Std. PredictedValue

-,622 2,329 ,000 1,000 24

Std. Residual -1,356 2,705 ,000 ,978 24

a. Dependent Variable: Abresid

Charts

7/28/2019 L3 Output Wiyono

39/46

NEW FILE.DATASET NAME DataSet8 WINDOW=FRONT.NPAR TESTS/K-S(NORMAL)=Variabel_X Variabel_Y/MISSING ANALYSIS.

NPar Tests

Notes

Output Created 28-MAY-2013 15:52:54

Comments

Input

Active Dataset DataSet8Filter Weight Split File N of Rows in WorkingData File

24

Missing Value Handling Definition of Missing User-defined missing

values are treated asmissing.

7/28/2019 L3 Output Wiyono

40/46

Cases Used

Statistics for each test arebased on all cases withvalid data for thevariable(s) used in thattest.

Syntax

NPAR TESTS/K-

S(NORMAL)=Variabel_XVariabel_Y/MISSING ANALYSIS.

Resources

Processor Time 00:00:00,00

Elapsed Time 00:00:00,01

Number of CasesAlloweda

157286

a. Based on availability of workspace memory.

[DataSet8]

One-Sample Kolmogorov-Smirnov Test

Variabel_X

Variabel_Y

N 24 24

Normal Parametersa,bMean 94,4167 95,5417Std.Deviation

7,95595 8,01075

Most Extreme DifferencesAbsolute ,382 ,417Positive ,241 ,289Negative -,382 -,417

Kolmogorov-Smirnov Z 1,872 2,043Asymp. Sig. (2-tailed) ,002 ,000

a. Test distribution is Normal.b. Calculated from data.

REGRESSION/MISSING LISTWISE/STATISTICS COEFF OUTS R ANOVA/CRITERIA=PIN(.05) POUT(.10)/NOORIGIN/DEPENDENT Variabel_Y/METHOD=ENTER Variabel_X

/SAVE RESID.

Regression

Notes

Output Created 28-MAY-2013 15:53:14

CommentsInput Active Dataset DataSet8Filter

7/28/2019 L3 Output Wiyono

41/46

Weight Split File N of Rows in WorkingData File

24

Missing Value Handling

Definition of MissingUser-defined missingvalues are treated asmissing.

Cases Used

Statistics are based oncases with no missingvalues for any variableused.

Syntax

REGRESSION/MISSING LISTWISE/STATISTICS COEFF

OUTS R ANOVA/CRITERIA=PIN(.05)

POUT(.10)/NOORIGIN/DEPENDENT

Variabel_Y/METHOD=ENTER

Variabel_X/SAVE RESID.

Resources

Processor Time 00:00:00,11Elapsed Time 00:00:00,12Memory Required 1356 bytesAdditional MemoryRequired for ResidualPlots

0 bytes

Variables Created orModified

RES_1 Unstandardized Residual

[DataSet8]

Variables Entered/Removeda

Model

VariablesEntered

VariablesRemoved

Method

1 Variabel_Xb . Enter

a. Dependent Variable: Variabel_Yb. All requested variables entered.

Model Summary

b

Model

R RSquare

Adjusted RSquare

Std. Error ofthe Estimate

1 ,857a ,734 ,722 4,22697

a. Predictors: (Constant), Variabel_Xb. Dependent Variable: Variabel_Y

ANOVAa

Model Sum of Squares

df MeanSquare

F Sig.

1 Regression 1082,878 1 1082,878 60,607 ,000b

Residual 393,081 22 17,867

7/28/2019 L3 Output Wiyono

42/46

Total 1475,958 23

a. Dependent Variable: Variabel_Yb. Predictors: (Constant), Variabel_X

Coefficientsa

Model Unstandardized Coefficients StandardizedCoefficients

t Sig.

B Std. Error Beta

1

(Constant) 14,112 10,495 1,345 ,192

Variabel_X

,862 ,111 ,857 7,785 ,000

a. Dependent Variable: Variabel_Y

Residuals Statisticsa

Minimum

Maximum

Mean Std.Deviation

N

Predicted Value 83,1080 100,3570 95,5417 6,86161 24Residual -6,83291 13,02954 ,00000 4,13406 24Std. PredictedValue

-1,812 ,702 ,000 1,000 24

Std. Residual -1,617 3,082 ,000 ,978 24

a. Dependent Variable: Variabel_Y

DESCRIPTIVES VARIABLES=Variabel_X Variabel_Y RES_1/STATISTICS=KURTOSIS SKEWNESS.

Descriptives

Notes

Output Created 28-MAY-2013 15:53:35

Comments

Input

Active Dataset DataSet8Filter Weight Split File N of Rows in WorkingData File

24

Missing Value HandlingDefinition of Missing

User defined missingvalues are treated asmissing.

Cases UsedAll non-missing data areused.

Syntax

DESCRIPTIVESVARIABLES=Variabel_XVariabel_Y RES_1/STATISTICS=KURTOSISSKEWNESS.

7/28/2019 L3 Output Wiyono

43/46

ResourcesProcessor Time 00:00:00,00

Elapsed Time 00:00:00,01

[DataSet8]

Descriptive Statistics

N Skewness Kurtosis

Statistic Statistic Std.Error

Statistic Std.Error

Variabel_X 24 -1,075 ,472 -,726 ,918Variabel_Y 24 -1,629 ,472 ,908 ,918Unstandardized Residual 24 1,971 ,472 5,444 ,918

Valid N (listwise) 24

COMPUTE Abresid=ABS(RES_1).EXECUTE.REGRESSION/MISSING LISTWISE/STATISTICS COEFF OUTS R ANOVA COLLIN TOL ZPP/CRITERIA=PIN(.05) POUT(.10)/NOORIGIN/DEPENDENT Abresid/METHOD=ENTER Variabel_X/RESIDUALS DURBIN HISTOGRAM(ZRESID)/SAVE RESID.

Regression

Notes

Output Created 28-MAY-2013 15:54:54

Comments

Input

Active Dataset DataSet8Filter Weight

Split File N of Rows in WorkingData File

24

Missing Value Handling

Definition of MissingUser-defined missingvalues are treated asmissing.

Cases Used

Statistics are based oncases with no missingvalues for any variableused.

7/28/2019 L3 Output Wiyono

44/46

Syntax

REGRESSION/MISSING LISTWISE/STATISTICS COEFF

OUTS R ANOVA COLLINTOL ZPP/CRITERIA=PIN(.05)

POUT(.10)

/NOORIGIN/DEPENDENT Abresid/METHOD=ENTER

Variabel_X/RESIDUALS DURBIN

HISTOGRAM(ZRESID)/SAVE RESID.

Resources

Processor Time 00:00:00,39Elapsed Time 00:00:00,32Memory Required 1396 bytesAdditional MemoryRequired for ResidualPlots

328 bytes

Variables Created or

Modified

RES_2 Unstandardized Residual

[DataSet8]

Variables Entered/Removeda

Model

VariablesEntered

VariablesRemoved

Method

1 Variabel_Xb . Enter

a. Dependent Variable: Abresidb. All requested variables entered.

Model Summaryb

Model

R RSquare

Adjusted RSquare

Std. Error ofthe Estimate

Durbin-Watson

1 ,774a ,599 ,581 2,26629 2,012

a. Predictors: (Constant), Variabel_Xb. Dependent Variable: Abresid

ANOVAa

Model Sum of Squares

df MeanSquare

F Sig.

1

Regression 168,619 1 168,619 32,830 ,000b

Residual 112,994 22 5,136

Total 281,612 23

a. Dependent Variable: Abresidb. Predictors: (Constant), Variabel_X

Coefficientsa

7/28/2019 L3 Output Wiyono

45/46

Model Unstandardized Coefficients StandardizedCoefficients

t Sig.

B Std. Error Beta

1

(Constant) 34,288 5,627 6,093 ,000

Variabel_X

-,340 ,059 -,774 -5,730 ,000

Coefficientsa

Model Correlations Collinearity Statistics

Zero-order Partial Part Tolerance VIF

1(Constant)

Variabel_X -,774 -,774 -,774 1,000 1,000

a. Dependent Variable: Abresid

Collinearity Diagnosticsa

Model

Dimension Eigenvalue

ConditionIndex

Variance Proportions(Constant

)Variabel_

X

11 1,997 1,000 ,00 ,00

2 ,003 24,287 1,00 1,00

a. Dependent Variable: Abresid

Residuals Statisticsa

Minimum

Maximum

Mean Std.Deviation

N

Predicted Value ,2550 7,0615 2,1551 2,70763 24Residual -3,95349 6,30836 ,00000 2,21648 24Std. PredictedValue

-,702 1,812 ,000 1,000 24

Std. Residual -1,744 2,784 ,000 ,978 24

a. Dependent Variable: Abresid

Charts

7/28/2019 L3 Output Wiyono

46/46