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REGRESSION /DESCRIPTIVES MEAN STDDEV CORR SIG N /MISSING LISTWISE /STATISTICS COEFF OUTS CI(95) R ANOVA CHANGE /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT Y /METHOD=ENTER X /SCATTERPLOT=(*ZPRED ,Y) /RESIDUALS HISTOGRAM(ZRESID) NORMPROB(ZRESID) /CASEWISE PLOT(ZRESID) OUTLIERS(3). Regression Notes Output Created 24-FEB-2015 03:39:18 Comments Input Active Dataset DataSet0 Filter <none> Weight <none> Split File <none> N of Rows in Working Data File 5 Missing Value Handling Definition of Missing User-defined missing values are treated as missing. Cases Used Statistics are based on cases with no missing values for any variable used.

Output SPSS Regresi

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Merupakanjmnjknkdma;ll;kokdj odkaokdamdlamd,amkadjaksdjak''adappadakklamerana ijayajama;kejekeua padadjdak hasi; dari SPSS

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Page 1: Output SPSS Regresi

REGRESSION /DESCRIPTIVES MEAN STDDEV CORR SIG N /MISSING LISTWISE /STATISTICS COEFF OUTS CI(95) R ANOVA CHANGE /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT Y /METHOD=ENTER X /SCATTERPLOT=(*ZPRED ,Y) /RESIDUALS HISTOGRAM(ZRESID) NORMPROB(ZRESID) /CASEWISE PLOT(ZRESID) OUTLIERS(3).

Regression

Notes

Output Created 24-FEB-2015 03:39:18

Comments

Input

Active Dataset DataSet0

Filter <none>

Weight <none>

Split File <none>

N of Rows in Working Data

File5

Missing Value Handling

Definition of MissingUser-defined missing values

are treated as missing.

Cases Used

Statistics are based on cases

with no missing values for

any variable used.

Page 2: Output SPSS Regresi

Syntax

REGRESSION

/DESCRIPTIVES MEAN

STDDEV CORR SIG N

/MISSING LISTWISE

/STATISTICS COEFF OUTS

CI(95) R ANOVA CHANGE

/CRITERIA=PIN(.05)

POUT(.10)

/NOORIGIN

/DEPENDENT Y

/METHOD=ENTER X

/SCATTERPLOT=(*ZPRED ,

Y)

/RESIDUALS

HISTOGRAM(ZRESID)

NORMPROB(ZRESID)

/CASEWISE

PLOT(ZRESID)

OUTLIERS(3).

Resources

Processor Time 00:00:06.28

Elapsed Time 00:00:28.66

Memory Required 1380 bytes

Additional Memory Required

for Residual Plots912 bytes

[DataSet0]

Descriptive Statistics

Mean Std. Deviation N

Dividend Payout .28360 .087697 5

Likuiditas 46792.0000 17661.65253 5

Correlations

Page 3: Output SPSS Regresi

Dividend Payout Likuiditas

Pearson CorrelationDividend Payout 1.000 .882

Likuiditas .882 1.000

Sig. (1-tailed)Dividend Payout . .024

Likuiditas .024 .

NDividend Payout 5 5

Likuiditas 5 5

Variables Entered/Removeda

Model Variables

Entered

Variables

Removed

Method

1 Likuiditasb . Enter

a. Dependent Variable: Dividend Payout

b. All requested variables entered.

Model Summaryb

Model R R Square Adjusted R

Square

Std. Error of the

Estimate

Change Statistics

R Square

Change

F Change df1

1 .882a .777 .703 .047786 .777 10.472 1

Model Summaryb

Model Change Statistics

df2 Sig. F Change

1 3a .048

a. Predictors: (Constant), Likuiditas

b. Dependent Variable: Dividend Payout

ANOVAa

Model Sum of Squares df Mean Square F Sig.

1 Regression .024 1 .024 10.472 .048b

Residual .007 3 .002

Page 4: Output SPSS Regresi

Total .031 4

a. Dependent Variable: Dividend Payout

b. Predictors: (Constant), Likuiditas

Coefficientsa

Model Unstandardized Coefficients Standardized

Coefficients

t Sig.

B Std. Error Beta

1(Constant) .079 .067 1.179 .323

Likuiditas 4.378E-006 .000 .882 3.236 .048

Coefficientsa

Model 95.0% Confidence Interval for B

Lower Bound Upper Bound

1(Constant) -.134 .291

Likuiditas .000 .000

a. Dependent Variable: Dividend Payout

Residuals Statisticsa

Minimum Maximum Mean Std. Deviation N

Predicted Value .18465 .36445 .28360 .077319 5

Residual -.047449 .052374 .000000 .041384 5

Std. Predicted Value -1.280 1.046 .000 1.000 5

Std. Residual -.993 1.096 .000 .866 5

a. Dependent Variable: Dividend Payout

Charts

Page 5: Output SPSS Regresi
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