l3 output wiyono
TRANSCRIPT
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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)
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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
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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
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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
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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
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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]
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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
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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
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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
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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
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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.
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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.
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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
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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]
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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.
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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
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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
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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.
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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
-
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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
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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.
-
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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.
-
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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
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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
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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
-
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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
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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
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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.
-
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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.
-
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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
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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
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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
-
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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
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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.
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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.
-
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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.
-
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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
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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
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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.
-
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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
-
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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
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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.
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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.
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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
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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
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