Data Regresi Suhu Dingin

REGRESSION /MISSING LISTWISE /STATISTICS COEFF OUTS R ANOVA COLLIN TOL /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT X /METHOD=ENTER Y /RESIDUALS DURBIN NORM(ZRESID) /SAVE RESID SDRESID.

Data

X

Y

RES_1

SDR_1

5.00

60.00

-1.27778

-.15458

5.00

52.00

-3.83333

-.45980

5.00

50.00

-4.47222

-.55619

5.00

48.00

-5.11111

-.67258

5.00

50.00

-4.47222

-.55619

28.00

50.00

18.52778

5.00

66.00

.63889

.09436

5.00

64.00

.00000

.00000

Regression

Notes

Output Created

29-Oct-2015 09:33:33

Comments

Input

Active Dataset

DataSet0

Filter

Weight

Split File

N of Rows in Working Data File

8

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.

Syntax

REGRESSION

/MISSING LISTWISE

/STATISTICS COEFF OUTS R ANOVA COLLIN TOL

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

/NOORIGIN

/DEPENDENT X

/METHOD=ENTER Y

/RESIDUALS DURBIN NORM(ZRESID)

/SAVE RESID SDRESID.

Resources

Processor Time

0:00:02.496

Elapsed Time

0:00:02.807

Memory Required

1356 bytes

Additional Memory Required for Residual Plots

312 bytes

Variables Created or Modified

RES_1

Unstandardized Residual

SDR_1

Studentized Deleted Residual

[DataSet0]

Variables Entered/Removedb

Model

Variables Entered

Variables Removed

Method

1

Ya

.

Enter

a. All requested variables entered.

b. Dependent Variable: X

Model Summaryb

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

Durbin-Watson

1

.282a

.079

-.074

8.42752

2.011

a. Predictors: (Constant), Y


ANOVAb

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

36.736

1

36.736

.517

.499a

Residual

426.139

6

71.023

Total

462.875

7

a. Predictors: (Constant), Y


Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

B

Std. Error

Beta

t

Sig.

1

(Constant)

25.444

24.610

1.034

.341

Y

-.319

.444

-.282

-.719

.499

a. Dependent Variable: X

Coefficientsa

Model

Collinearity Statistics

Tolerance

VIF

1

Y

1.000

1.000


Collinearity Diagnosticsa

Model

Dimension

Variance Proportions

Eigenvalue

Condition Index

(Constant)

Y

1

1

1.993

1.000

.00

.00

2

.007

16.459

1.00

1.00


Residuals Statisticsa

Minimum

Maximum

Mean

Std. Deviation

N

Predicted Value

4.3611

10.1111

7.8750

2.29085

8

Std. Predicted Value

-1.534

.976

.000

1.000

8

Standard Error of Predicted Value

3.264

5.723

4.143

.822

8

Adjusted Predicted Value

3.8144

11.9173

7.8664

3.11729

8

Residual

-5.11111

18.52778

.00000

7.80237

8

Std. Residual

-.606

2.198

.000

.926

8

Stud. Residual

-.706

2.449

.000

1.034

8

Deleted Residual

-6.91729

23.00000

.00860

9.73030

8

Stud. Deleted Residual

-.673

.094

-.329

.304

7

Mahal. Distance

.175

2.353

.875

.734

8

Cook's Distance

.000

.724

.116

.248

8

Centered Leverage Value

.025

.336

.125

.105

8


Charts

SAVE TRANSLATE OUTFILE='C:\Users\Acer\Documents\Yuli\data regresi suhu dingin.xls' /TYPE=XLS /VERSION=2 /MAP /REPLACE /FIELDNAMES. Data written to C:\Users\Acer\Documents\Yuli\data regresi suhu dingin.xls. 4 variables and 8 cases written to range: SPSS. Variable: X Type: Number Width: 8 Dec: 2 Variable: Y Type: Number Width: 8 Dec: 2 Variable: RES_1 Type: Number Width: 11 Dec: 5 Variable: SDR_1 Type: Number Width: 11 Dec: 5