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NPAR TESTS

/K-S(NORMAL)=RES_1

/STATISTICS DESCRIPTIVES

/MISSING ANALYSIS.

NPar Tests

UJI NORMALITAS

[DataSet1] D:\Semester 6\MENTKUAN\metkuan data tugas.sav

Descriptive Statistics

N

Mean

Std. Deviation

Minimum

Maximum

Unstandardized Residual

38

0E-7

354,99441656

-976,05009

1307,41245

One-Sample Kolmogorov-Smirnov Test

Unstandardized Residual

N

38

Normal Parametersa,b

Mean

0E-7

Std. Deviation

354,99441656

Most Extreme Differences

Absolute

,171

Positive

,171

Negative

-,116

Kolmogorov-Smirnov Z

1,055

Asymp. Sig. (2-tailed)

,215

a. Test distribution is Normal.

b. Calculated from data.

REGRESSION

/MISSING LISTWISE

/CRITERIA=PIN(.05) POUT(.10)

/NOORIGIN

/DEPENDENT TP

/METHOD=ENTER LL BNH PPK TK

/RESIDUALS DURBIN.

Regression

UJI AUTKORELASI


Variables Entered/Removeda

Model

Variables Entered

Variables Removed

Method

1

TK, BNH, PPK, LLb

.

Enter

a. Dependent Variable: TP

b. All requested variables entered.

Model Summaryb

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

Durbin-Watson

1

,933a

,871

,855

375,894

2,416

a. Predictors: (Constant), TK, BNH, PPK, LL

b. Dependent Variable: TP

ANOVAa

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

31439706,229

4

7859926,557

55,627

,000b

Residual

4662778,324

33

141296,313

Total

36102484,553

37


b. Predictors: (Constant), TK, BNH, PPK, LL

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

B

Std. Error

Beta

1

(Constant)

-208,129

154,488

-1,347

,187

LL

,291

,134

,535

2,174

,037

BNH

119,130

58,395

,499

2,040

,049

PPK

-2,412

1,176

-,216

-2,052

,048

TK

2,783

1,195

,161

2,328

,026


Residuals Statisticsa

Minimum

Maximum

Mean

Std. Deviation

N

Predicted Value

105,74

3495,78

1137,66

921,804

38

Residual

-976,050

1307,412

,000

354,994

38

Std. Predicted Value

-1,119

2,558

,000

1,000

38

Std. Residual

-2,597

3,478

,000

,944

38


REGRESSION

/MISSING LISTWISE

/STATISTICS BCOV COLLIN TOL


/NOORIGIN

/DEPENDENT TP

/METHOD=ENTER LL BNH PPK TK.

Regression

UJI MULTIKOLINEARITAS



Model

Variables Entered

Variables Removed

Method

1

TK, BNH, PPK, LLb

.

Enter



Coefficientsa

Model

Collinearity Statistics

Tolerance

VIF

1

LL

,065

15,468

BNH

,066

15,260

PPK

,353

2,833

TK

,814

1,228


Coefficient Correlationsa

Model

TK

BNH

PPK

LL

1

Correlations

TK

1,000

,023

-,188

-,070

BNH

,023

1,000

-,158

-,908

PPK

-,188

-,158

1,000

-,166

LL

-,070

-,908

-,166

1,000

Covariances

TK

1,429

1,624

-,264

-,011

BNH

1,624

3409,944

-10,849

-7,089

PPK

-,264

-10,849

1,382

-,026

LL

-,011

-7,089

-,026

,018


Collinearity Diagnosticsa

Model

Dimension

Eigenvalue

Condition Index

Variance Proportions

(Constant)

LL

BNH

PPK

1

1

4,164

1,000

,01

,00

,00

,01

2

,431

3,108

,02

,00

,00

,01

3

,291

3,785

,38

,01

,00

,10

4

,104

6,321

,04

,06

,02

,89

5

,010

20,018

,56

,92

,98

,00

Collinearity Diagnosticsa

Model

Dimension

Variance Proportions

TK

1

1

,02

2

,97

3

,00

4

,01

5

,00


COMPUTE ABS_RES=ABS(RES_1).

EXECUTE.

REGRESSION

/MISSING LISTWISE

/STATISTICS COEFF OUTS R ANOVA


/NOORIGIN

/DEPENDENT ABS_RES

/METHOD=ENTER LL BNH PPK TK.

Regression

UJI HETEROSKODATISITAS



Model

Variables Entered

Variables Removed

Method

1

TK, BNH, PPK, LLb

.

Enter

a. Dependent Variable: ABS_RES


Model Summary

Model

R

R Square

Adjusted R Square


1

,531a

,282

,195

230,89901


ANOVAa

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

689667,009

4

172416,752

3,234

,024b

Residual

1759373,593

33

53314,351

Total

2449040,602

37



Coefficientsa

Model



t

Sig.

B

Std. Error

Beta

1

(Constant)

-42,517

94,897

-,448

,657

LL

-,091

,082

-,641

-1,105

,277

BNH

71,831

35,870

1,154

2,003

,053

PPK

-,146

,722

-,050

-,202

,841

TK

,075

,734

,017

,103

,919


REGRESSION

/MISSING LISTWISE

/STATISTICS COEFF OUTS R ANOVA


/NOORIGIN

/DEPENDENT TP

/METHOD=ENTER LL BNH PPK TK

/SCATTERPLOT=(*SRESID ,*ZPRED)

/RESIDUALS HISTOGRAM(ZRESID) NORMPROB(ZRESID).

Regression

HETEROSKODATISITAS TABEL



Model

Variables Entered

Variables Removed

Method

1

TK, BNH, PPK, LLb

.

Enter



Model Summaryb

Model

R

R Square

Adjusted R Square


1

,933a

,871

,855

375,894


b. Dependent Variable: TP

ANOVAa

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

31439706,229

4

7859926,557

55,627

,000b

Residual

4662778,324

33

141296,313

Total

36102484,553

37



Coefficientsa

Model



t

Sig.

B

Std. Error

Beta

1

(Constant)

-208,129

154,488

-1,347

,187

LL

,291

,134

,535

2,174

,037

BNH

119,130

58,395

,499

2,040

,049

PPK

-2,412

1,176

-,216

-2,052

,048

TK

2,783

1,195

,161

2,328

,026


Residuals Statisticsa

Minimum

Maximum

Mean

Std. Deviation

N

Predicted Value

105,74

3495,78

1137,66

921,804

38

Std. Predicted Value

-1,119

2,558

,000

1,000

38

Standard Error of Predicted Value

81,882

322,903

127,510

48,948

38

Adjusted Predicted Value

104,33

3857,04

1156,79

991,748

38

Residual

-976,050

1307,412

,000

354,994

38

Std. Residual

-2,597

3,478

,000

,944

38

Stud. Residual

-2,935

3,852

-,015

1,072

38

Deleted Residual

-1357,044

1603,459

-19,136

482,042

38

Stud. Deleted Residual

-3,363

5,113

,007

1,242

38

Mahal. Distance

,782

26,330

3,895

4,818

38

Cook's Distance

,000

1,924

,097

,333

38

Centered Leverage Value

,021

,712

,105

,130

38


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