Mlr Tutorial Mr2 Spss

7/21/2019 Mlr Tutorial Mr2 Spss

1/51

SPSS

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2/51

SPSS

- 120000 .

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PB

P

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1 , -

, , - -. Chart Builder SPSS (Graphs -> Chart Build-er(Scatter/DotSimple Scatter)).

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2


3/51

SPSS

Statistics : -, , - , - , , . . Sta-tistics :

Estimates , - , , t- - p-value.

Model Fit -: , - , ANOVA , .

Part and Partial Correlations (Ze-ro-order), (Part; Semipartial) / (Partial) - .

Collinearity d iagnostics , - .

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3


4/51

SPSS

Plots , , -

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Save : (1) - - ; (2) , -, , -; (3)

( , - ()); (4) , 2.

2 (3) (4) , .- (outliers) , ( -). - (. 1. . -). . , , -

2. ( leverage) , .

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5/51

SPSS

Enter ( , , -), SPSS 3.

. , - - . - 0 ( ) 1 ( ). , , (2k+2)/n, , n

( (2.3+2)/16 = 0,5). (influential) , - . - , -. - , - -. 4/n ( 4/16=0,25). -, , dfbeta (, - , .. )., dfbeta 2/sqrt(n), (

5,04/216/2 ==

).3 - , - . Syntax_outlier_case.sps.

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5


6/51

SPSS

-. , , :

- (R = 0,959)

.

- (R2 = 0,919). , 91,9 % , - . 9,1 % .

Model Summary(b)

Model R R SquareAdjustedR Square

Std. Error ofthe Estimate

1 .959(a) .919 .899 4546.245

a Predictors: (Constant), , -, b Dependent Variable:

. F-. - , ,

. , Sig.=0,000 < 0,05, , . , .

ANOVAb

2808359494.7 3 936119831.6 45.292 .000a

248020088.34 12 20668340.70

3056379583.0 15

Regression

ResidualTotal

Model

1

Sum of

Squares df Mean Square F Sig.

Predictors: (Constant), , ,

a.

Dependent Variable: b.

: ; - -. , , :

. - , 2: -

6


7/51

SPSS

(b)4 , p-value t- - 0,05. , , .

(b) , ( , - ).

- (be-ta5,6 = -0,725), - (beta = 0,665) (beta = 0,242). , - .

-

(VIF) , - ( VIF -, ). , VIF>107.

Coefficientsa

210159.4 23729.909 8.856 .000

6723.478 840.997 .665 7.995 .000 .542 .918 .657 .976 1.024

-3832.503 444.013 -.725 -8.632 .000 -.668 -.928 -.710 .958 1.044

.069 .024 .242 2.903 .013 .303 .642 .239 .972 1.029

(Constant)

Model

1

B Std . Er ror

Unstandardized

Coefficients

Beta

Standardized

Coefficients

t Sig. Zero-order Partial Part

Correlations

Tolerance VIF

Collinearity Statistics

Dependent Variable: a.

. , (). -, -, . - , - ( , -, -, , ).

4 b , -.5 beta . - beta . .6 beta - - . - .

7 , VIF>2, VIF>3, VIF>4, VIF>5. - 10.

. - , 2: -

7


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SPSS

- .

, 8:1. .

(RES2) - (RES_1).

2. (SF2, P2, PB2).

8 , :http://www.spsstools.net/Syntax/RegressionRepeatedMeasure/WhiteTestStatisticsAndSignificance.txt.

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8
http://www.spsstools.net/Syntax/RegressionRepeatedMeasure/WhiteTestStatisticsAndSignificance.txthttp://www.spsstools.net/Syntax/RegressionRepeatedMeasure/WhiteTestStatisticsAndSignificance.txthttp://www.spsstools.net/Syntax/RegressionRepeatedMeasure/WhiteTestStatisticsAndSignificance.txt


9/51

SPSS

3. () - . m -

:2

)1( =

mmcp

, , 3. SFxP, SFxPB, PxPB.

4. , , - (RES2), : (SalesForce, Price, PromoBudget);

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9


10/51

SPSS

(SF2, P2, PB2)9 - (SFxP, SFxPB, PxPB).

Model Summary


Std. Error of theEstimate

1 .846(a) .716 .290 12051207.228

a Predictors: (Constant), PxPB, , , SFxPB,

SF2, PB2, , SFxP, P2

, , .

WH

P

NRtestWhite

critical

Pd

RES

f

0

2

)9;05,0(2

)2(2

919,16

45842,118257161513741,0.16

=

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11


12/51

SPSS

10, (stem-and-leaf) .

, ,

. , (16 11

), -12. - - (0,191> 0,05),

10 450- .11 , -, , -. - -,

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KurtosisSkewness

NtestBeraJarque df

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2.12 Explore (Analyze -> Descriptive Statis-tics -> Explore ).

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12


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SPSS

. -, .

Tests of Normality

.163 16 .200* .923 16 .191Unstandardized Res idual

Statistic df Sig. Statistic df Sig.

Kolmogorov-Smirnova

Shapiro-Wilk

This is a lower bound of the true s ignificance.*.

Lilliefors Significance Correctiona.

. - , 2: -

13


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SPSS

-

, : , . :

PBPSFS 660571.0,069193962487.3832,50331-5647.6723,477517074210159,443 ++=

- (-, ). - . -

(.. - ) -. .

6605710,06919396

12487-3832,5033

56476723,47751

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2

1

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bPB

S

bP

S

bSF

S

, - - ( !)13.

13 PS/ ,

SFS/ , PBS/ .

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14


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SPSS

SPB

SPB

PBS

S

P

S

P

P

S

S

SF

S

SF

SF

S

PBS

PS

SFS

.b.

.b.

.b.

3/

2/

1/

=

=

=

=

=

=

SPSS, (SF_elasticity, P_elasticity, PB_elasticity).

, .

16;

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N

N

N

iPBSi

PBS

N

iPSi

PS

N

iSFSi

SFS

. - , 2: -

15


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SPSS

Statistics

16 16 16

0 0 0

.4480 -2.2825 .2617

Valid

Missing

N

Mean

SF_elasticity P_elas ticity PB_elasticity

0,45, (-2,28), 0,26. ,

1 %, 2,28% ( !).

.

, , , - . , , -.

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16


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SPSS

PBbPbSFbbS

PBPSFbS

PBPSFbS

PBPSFbS

bbb

bbb

bbb

lnlnlnlnln

lnlnlnlnln

)ln(ln

3210

0

0

0

321

321

321

+++=

+++=

=

=

, - .

1

30

1

20

1

10

321

321

321

=

=

=

bbb

bbb

bbb

PBPSFbb

PB

S

PBPSFbbP

S

PBPSFbbSF

S

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0

1

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2

0

120/

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321

321

321

321

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.

.

bPBPSFb

PBPBPSFbb

S

PB

PB

S

bPBPSFb

PPBPSFbbSP

PS

bPBPSFb

SFPBPSFbb

S

SF

SF

S

bbb

bbb

PBS

bbbbbb

PS

bbb

bbb

SFS

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=

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=

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=

, - . , - (lnS, lnSF, lnP, lnPB).

. - , 2: -

17


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SPSS

SPSS .

.

Model Summary b

.963a .928 .909 .05198

Model

1

R R Square

Adjusted

R Square

Std. Error of

the Estimate

Predictors: (Constant), lnPB, lnSF, lnPa.

Dependent Variable: lnSb.

, , , : ,

(92,8 %) - .

, -( -!).

. - , 2: -

18


19/51

SPSS

, F- - (=0,05).

ANOVAb

.415 3 .138 51.231 .000a

.032 12 .003

.448 15

Regression

Residual

Total

Model

1

Sum of


Predictors: (Constant), lnPB, lnSF, lnPa.

Dependent Variable: lnSb.

, . , - :

( ), ( ) - ( ). - -.

Coefficientsa

16.981 1.493 11.372 .000

.400 .047 .674 8.565 .000 .547 .927 .665 .974 1.026

-2.339 .248 -.749 -9.440 .000 -.669 -.939 -.733 .959 1.042

.217 .082 .207 2.647 .021 .279 .607 .206 .983 1.017

(Constant)

lnSF

lnP

lnPB

Model

1

B Std . Err or

Unstandardized

Coefficients

Beta

Standardized

Coefficients


Correlations

Tolerance VIF


Dependent Variable: lnSa.

, , p-value - 0,05 , - .

Model Summary

. - , 2: -

19


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SPSS


Std. Error ofthe Estimate

1 .713(a) .508 .262 .00240

a Predictors: (Constant), lnPxlnPB, lnSF2, lnPB2, lnSFxlnPB, lnSFxlnP

valuep

NRtestWhite

dfWh

RES

0

)9;1293,8(

)2_2_(2

05,05212,0

5212,0

129349491,8318370,50808434.16

>

=

===

==

- 0,05 , - .

. - , 2: -

20


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SPSS

Tests of Normality

.137 16 .200* .968 16 .803Unstandardized Residual

Statistic df Sig. Statistic df Sig.

Kolmogorov-Smirnova

Shapiro-Wilk

This is a lower bound of the true s ignificance.*.

Lilliefors Significance Correctiona.

- , - . , - ( - ); ( , - ); ( -, , ).

. - , 2: -

21


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SPSS

. b014. :

o

A

o beAb ==ln

SPSS .

- :

548270,2168934961831-2,3394799227330,39951578PB.P.SF82352.23692308,1S =

, :

14 b0, -.

. - , 2: -

22


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SPSS

548270,21689349

61831-2,3394799

227330,39951578

/

/

/

=

=

=

PBS

PS

SFS

- , .

c

S

PBSFSKScKK

PSU

K

U

G

KUG

v

f

vf

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+=

=

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..

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max

- . - .

0..

0..

0..

max],),,,([),,(.

=

=

=

=

=

+=

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PB

K

PB

S

S

K

PB

SP

PB

G

SF

K

SF

S

S

K

SF

SP

SF

G

P

S

S

K

P

SPS

P

G

PBSFPBPSFSKPBPSFSPG

PB

K

PB

S

S

KP

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K

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KP

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K

P

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+

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23


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SPSS

=

++=+=

+

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+

v

vvf

PS

PS

G

PSPS

PSPS

cS

K

ScPBSFSScKK

LINSK

S

KP

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KP

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P

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S

K

S

P

P

SP

S

PS

S

P

P

S

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K

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b1

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MULTS

opt+

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S

SF

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KcP

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15 , .. 1/


25/51

SPSS

SPB

PBKcP

SKP

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PB

PB

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PB

S

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KP

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PB

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=

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PB

PB.KSF

SF.K

S

PB.

PB

KS

SF.

SF

K

].cP[

].cP[

S

PB.

PB

K].cP[

S

SF.

SF

K].cP[

PB/S

SF/S

PB/Sv

SF/Sv

PB/Sv

SF/Sv

=

=

=

=

PB

SF.S

PB

PB.KSF

SF.K

1PB

K

SSF

K

S.cPBSF.SS.cKK

..,LIN)S(K

PB/S

SF/S

vvf

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- ( - -. - : - ).

. - , 2: -

25


26/51

SPSS

3

1

b

b

omoBudgetPr

onBudgetDistributi

MULTS

=

, , 3 ( ) , Solver.

1.

)PB.P.SF82352.23692308,1.(30PBSF.120000

/.30c

/.120000S

PB.P.SF82352.23692308,1S

548270,2168934918312,33947996-227330,39951578

v

548270,2168934961831-2,3394799227330,39951578

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max

=

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PB

K

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S

S

K

PB

S

PPB

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SF

K

SF

S

S

K

SF

SP

SF

G

P

S

S

K

P

SPS

P

G

PBSFPBPSFSKPBPSFSPG

KUG

01)PB.P.SF7.689349548282352.0,2123692308,1.(30

)PB.P.SF7.689349548282352.0,2123692308,1.(PPB

G

0120000)PB.P.SF3.951578227382352.0,3923692308,1.(30

)PB.P.SF3.951578227382352.0,3923692308,1.(PSF

G

0)PB.P.SF2.2308,1823561831.2369-2,3394799.(30

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PB.P.SF82352.23692308,1P

G

1-548270,2168934918312,33947996-227330,39951578

1-548270,2168934918312,33947996-227330,39951578

548270,2168934918312,33947996-1-227330,39951578

548270,2168934918312,33947996-1-227330,39951578

548270,216893491-18312,33947996-227330,39951578

548270,216893491-18312,33947996-227330,39951578

548270,2168934918312,33947996-227330,39951578

=

=

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- - :

. - , 2: -

26


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SPSS

227330,39951578SF54827.0,21689349.120000PB

548270,21689349

227330,39951578

PB

SF.120000

/.39675162,5218311,33947996-

18312,33947996-.30

118312,33947996-

18312,33947996-.30P

max

max

G

G

=

=

==+

=

=

=

=

+

01)227330,39951578

SF54827.0,21689349.120000.(P

.SF954827..0,216893423692308,2.30)

227330,39951578

SF54827.0,21689349.120000(

.P.SF7.689349548282352.0,2123692308,1

01)PB.P.SF7.689349548282352.0,2123692308,1.(30

)PB.P.SF7.689349548282352.0,2123692308,1(P

227330,39951578

SF54827.0,21689349.120000PB

1-548270,2168934918312,33947996-

227330,399515781-548270,21689349

118312,33947996-227330,39951578

1-548270,2168934918312,33947996-227330,39951578

1-548270,2168934918312,33947996-227330,39951578

Gmax

=

01)

227330,39951578

SF54827.0,21689349.120000(

.39675162,52.SF7.689349548282352.0,2123692308,1.30

)227330,39951578

SF54827.0,21689349.120000(

.39675162,52.SF7.689349548282352.0,2123692308,1

451730,78310650-

18312,33947996-227330,39951578

451730,78310650-

61831-1,3394799227330,39951578

=

01

SF5996952,05135754

2326670003485377,0.2559291320000949996,0.SF.131711,154161226

SF5996952,05135754

2326670003485377,0.85823450049776717,0.SF77237.5138707,53

451730,78310650-227330,39951578

451730,78310650-227330,39951578

93410256022559,5850515383078894,0SF

850515383078894,0SF1SF.85378576729405,1

01

SF.SF.49784883156795,2SF.SF013515.4,34598862

22440,38359072-

22440,38359072-

22440,38359072-

045173-0,7831065227330,39951578045173-0,7831065227330,39951578

==

=

=

=

. - , 2: -

27


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SPSS

.467258476,327402227330,39951578

548270,21689349.270719209,603072PB

548270,21689349

227330,39951578

PB

270719209,603072

PB

SF.S

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93410256022559,5850515383078894,0SF

850515383078894,0SF

1SF.85378576729405,1

01

SF.SF.49784883156795,2SF.SF013515.4,34598862

max

max

max

G

PB/S

SF/S

G

22440,38359072-G

22440,38359072-

22440,38359072-

045173-0,7831065227330,39951578045173-0,7831065227330,39951578

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673995240806416,67398467258476,327402

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max

548270,21689349

61831-2,3394799227330,39951578

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=++=

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2. Solver

Solver : - ( - -). !16

2.1. -

16 Solver .

. - , 2: -

28


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SPSS

. - , 2: -

29


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SPSS

2.2. - .

. - , 2: -

30


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SPSS

. - , 2: -

31


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SPSS

.

Metro. .

DM

PB

P

SF

S

DMPBPSFfS

= ),,,(

bbbbb

DMbPBbPbSFbbS

++++=

43210

43210

,,,,

....

( ) , Enter.

. - , 2: -

32


33/51

SPSS

.

Model Summaryb

,965a ,931 ,906 4371,811

Model

1

R R Square

Adjust ed

R Square

Std. Error of

the Estimate

Predictors: (Constant), ,

, ,

a.


-. , - ( 0,906, - - (0,899)) - ( !) (R2=0,909). , , (

F 0,000 < 0,05).

ANOV Ab

2846139532.978 4 711534883.2 37.228 .000a

210240050.022 11 19112731.82

3056379583.000 15

Regression

Residual

Total

Model

1

Sum of Squares df Mean Square F Sig.

Predictors: (Constant), , , ,

a.


- , , -

. - , 2: -

33


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SPSS

. , , , , (-

!) ( - !), - . , - ( , ) . (. !), . - . - ( -

VIF 53, 10) ( (r= -0,99)).

, - , .. - ( , .. ) , . , . - . - , -

. - , 2: -

34


35/51

SPSS

()(), () ( , , , ). . .

( , - - - , ) - . , - . SPSS - , SPSS (Ridge regression.sps). , - .

17:

1. Syntax_ridge_case.sps. (Case_ridge.sav)!18

2. . , () Ridge regression.sps.

INCLUDE 'C:\Program Files\SPSS Evaluation\Ridge regression.sps '.

17 , , . , - , SPSS. Edit -> Options Viewer 133 -. SPSS 16.

18 , , - SPSS, - -, Case_ridge.sav. Case_Multiple_regression.sav, , , -

, . , - (sales, sforce,price, pbudget, dmail).

. - , 2: -

35


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SPSS

3. .

RIDGEREG DEP=sales /ENTER = sforce price pbudget dmail/.

SPSS , - - . .. . - . -

, ( ), -1920. =0,05., , - , - =1/F, F F , . =1/37.228319318166086 = 0.02686127169625.

19 , NCSS, -, . , VIF.20 , ,

( -).

. - , 2: -

36


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SPSS

4. (Case_ridge.sav) , .

RIDGEREG DEP=sales /ENTER = sforce price pbudget dmail/k=.05.

SPSS .

. - , 2: -

37


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SPSS

V.

- , , . - n , n-1 - () . (), . - - , . .

(D1) (D2). D1 -, , 1. - ( ) 0. . - !

. - , 2: -

38


39/51

SPSS

. , . - - (R2 change). .. (-) . -

. - , ( Enter) (Remove) . , - ( 1 , - , , ; 2 1, ( )) . , , ( ) -. Statistics, Plots, Save

. - , 2: -

39


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SPSS

, R squared change.

. - , 2: -

40


41/51

SPSS

- .

Model Summaryc

,959a ,919 ,899 4546,245 ,919 45,292 3 12 ,000

,965b ,932 ,898 4565,206 ,013 ,950 2 10 ,419

Model1

2

R R Square

Adjust ed

R Square

Std. E rror of

the Estimate

R Square

Change F Change df1 df2 Si g. F Change

Change Statistics

Predictors: (Constant), , , a.

Predictors: (Constant), , , , dummy_medium firms, dummy_

little firms

b.

Dependent Variable: c.

ANOVAc

2846139532,978 4 711534883,2 37,228 ,000a

210240050,022 11 19112731,82

3056379583,000 15

2881830408,300 6 480305068,0 24,765 ,000b

174549174,700 9 19394352,74

3056379583,000 15

Regression

Residual

Total

Regression

Residual

Total

Model

1

2



a.


, dummy_medium firms, dummy_little firms

b.


Coefficients a

210159,4 23729,909 8,856 ,000

6723,478 840,997 ,665 7,995 ,000 ,542 ,918 ,657 ,976 1,024

-3832,503 444,013 -,725 -8,632 ,000 -,668 -,928 -,710 ,958 1,044

,069 ,024 ,242 2,903 ,013 ,303 ,642 ,239 ,972 1,029

218059,3 24819,085 8,786 ,000

6061,229 1036,479 ,600 5,848 ,000 ,542 ,880 ,483 ,648 1,543

-3924,542 509,403 -,743 -7,704 ,000 -,668 -,925 -,636 ,734 1,363

,070 ,026 ,245 2,663 ,024 ,303 ,644 ,220 ,808 1,238

-2939,397 3945,131 -,092 -,745 ,473 -,466 -,229 -,062 ,446 2,240

1700,677 3331,032 ,061 ,511 ,621 -,111 ,159 ,042 ,477 2,096

(Constant)

(Constant)

dummy_little firms

dummy_medium firms

Model

1

2

B Std . Er ror

UnstandardizedCoefficients

Beta

StandardizedCoefficients


Correlations

Tolerance VIF



Excluded Variablesb

-,129a -1,326 ,212 -,371 ,674 1,483 ,663

,113a 1,184 ,261 ,336 ,721 1,388 ,721

dummy_little firms

dummy_medium firms

Model

1

Beta In t Sig.

Partial

Correlation Tolerance VIF

Minimum

Tolerance


Predictors in the Model: (Constant), , , a.


-

t -, (-

. - , 2: -

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) F , . p-value F (F = [(R22- R12)/(k2- k1)]/[(1-R22)/(n - k2-1)], 1 , 2 ; ). ,

( ) . p-value = 0,419 > 0,05, , .. - - . - -, ( 1,3 %). :

267693030817001396922512939

10420699054105054243470139242291677096061281895218059

.D,.D,-

.PB-,.P,.SF-,,S

+

++=

- . . , - ( !), -, . b4 = -2939,4 , 2939,4 - ( -!), b5 = 1700,7 1700,7 . , .

PBPSFS

PBPSFS

PBPSFS

051042.0,069905414701.3924,54243-7709.6061,22916895218059,2810)D20;(D1

051042.0,069905414701.3924,54243-7709.6061,22916825308219759,958

1)D20;(D1

051042.0,069905414701.3924,54243-7709.6061,22916977249215119,884

0)D21;(D1

++=

==

++=

==

++=

==

, - , , . - , . (.. -), . -

, - , .

. - , 2: -

42


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..,-M

-K

-N

ANOVA!-SquaresofSumResidual

SquaresofSumResidual

SquaresofSumResidualSquaresofSumResidual

SquaresofSumResidual

M

SquaresofSumResidualSquaresofSumResidual

modelnedunconstrai

modelnedunconstraimodeldconstraine

modelnedunconstrai

modelnedunconstraimodeldconstraine

>

=

=

=

M

KNKN

FtestChow

.

:

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0371772,02

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7004174549174,

7004174549174,-0221210240050,)(

0308.D21700,67693251.D12939,39692-

-051042.0,069905414701.3924,54243-7709.6061,22916895218059,281

model)ined(unconstra,

0,069.3832,503.-6723,478.210159,4

model)ed(constrain

=

=

+

++=

++=

FtestChow

PBPSFS

PBPSFS

0)bb:(H

05,09636,0)11;2;0371772,0(

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>==== HdfdfChowvaluep

, ,

.

V.

- , , . - . - , - - , . - , ,

, ,. - . - . . , . : ( SPSS Forward), ( Backward) - ( Stepwise). . , - . , , F ( -

. - , 2: -

43


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SPSS

(FIN) (PIN)). F > FIN ( FIN=3,84, - Analyze -> Regression -> Linear -> Op-tions), PIN ( PIN=0,05).

, . , - , - . - R-squared change Statistics

. - .

Model Summary

,668a ,447 ,407 10991,826 ,447 11,297 1 14 ,005

,928b ,862 ,841 5698,479 ,415 39,089 1 13 ,000

,959c ,919 ,899 4546,245 ,057 8,425 1 12 ,013

Model1

2

3

R R Square

Adjusted

R Square

Std. Error of

the Estimate

R Square

Change F Change df1 df2 Sig. F Change

Change Statistics

Predictors: (Constant), a.

Predictors: (Constant), , b.

Predictors: (Constant), , , c.

, , (.. , ) - - ( ),

41,5 %. -

. - , 2: -

44


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, p-value F - - 0,05. - , ( 2) 5,7 %.

ANOVAd

1364896388,053 1 1364896388 11,297 ,005a

1691483194,947 14 120820228,2

3056379583,000 15

2634234994,787 2 1317117497 40,561 ,000b

422144588,213 13 32472660,63

3056379583,000 15

2808359494,659 3 936119831,6 45,292 ,000c

248020088,341 12 20668340,70

3056379583,000 15

Regression

Residual

Total

Regression

Residual

Total

Regression

Residual

Total

Model

1

2

3





Dependent Variable: d.

Coefficientsa

254548,8 50837,204 5,007 ,000

-3531,561 1050,719 -,668 -3,361 ,005 -,668 -,668 -,668 1,000 1,000

241732,4 26435,070 9,144 ,000

-4023,777 550,383 -,761 -7,311 ,000 -,668 -,897 -,754 ,980 1,021

6579,154 1052,302 ,651 6,252 ,000 ,542 ,866 ,644 ,980 1,021

210159,4 23729,909 8,856 ,000

-3832,503 444,013 -,725 -8,632 ,000 -,668 -,928 -,710 ,958 1,044

6723,478 840,997 ,665 7,995 ,000 ,542 ,918 ,657 ,976 1,024

,069 ,024 ,242 2,903 ,013 ,303 ,642 ,239 ,972 1,029

(Constant)

(Constant)

(Constant)

Model

1

2

3

B Std . Er ror

Unstandardized

Coefficients

Beta

Standardized

Coefficients


Correlations

Tolerance VIF



- . .

Excluded Variablesd

,651a

6,252 ,000 ,866 ,980 1,021 ,980

,203a 1,007 ,332 ,269 ,975 1,026 ,975

,648a 6,224 ,000 ,865 ,986 1,015 ,986

,242b 2,903 ,013 ,642 ,972 1,029 ,958

,290b ,389 ,704 ,112 ,020 48,981 ,020

,809c 1,406 ,187 ,390 ,019 52,962 ,019

Model

1

2

3

Beta In t Sig.

Partial

Correlation Tolerance VIF

Minimum

Tolerance

Collinearity Statisti cs

Predictors in the Model: (Constant), a.

Predictors in the Model: (Constant), , b.

Predictors in the Model: (Constant), , , c.


. - , 2: -

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-. , . , - . -, , F

( FOUT=2,71, POUT=0,10, !).- - , - .

Model Summary

,965a ,931 ,906 4371,811 ,931 37,228 4 11 ,000

,965b ,931 ,914 4196,281 ,000 ,056 1 11 ,818

Model

1

2

R R Square

Adjusted

R Square

Std. Error of

the Estimate

R Square


Change Statistics

Predictors: (Constant), , , , a.

Predictors: (Constant), , , b.

ANOVAc

2846139533,0 4 711534883,2 37,228 ,000a

210240050,022 11 19112731,82

3056379583,0 15

2845074244,3 3 948358081,4 53,857 ,000b

211305338,736 12 17608778,233056379583,0 15

Regression

Residual

Total

Regression

ResidualTotal

Model

1

2

Sum of



a.

Predictors: (Constant), , , b.


Coefficientsa

203009,3 23379,266 8,683 ,000

-1372,882 5815,151 -,136 -,236 ,818

-3711,737 435,531 -,702 -8,522 ,000

,078 ,024 ,274 3,288 ,007

7,949 5,654 ,809 1,406 ,187

204126,4 21976,139 9,289 ,000

-3733,676 408,418 -,707 -9,142 ,000

,077 ,022 ,269 3,484 ,005

6,627 ,755 ,675 8,781 ,000

(Constant)

(Constant)

Model

1

2

B St d. E rror

Unstandardized

Coefficients

Beta

Standardized

Coefficients

t Sig.


. - , 2: -

46


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Ex cluded Variabl esb

-,136a

-,236 ,818 -,071 ,019

Model

2

Beta In t Sig.

Partial

Correlation Tolerance

Collinearity

Statistics

Predictors in the Model: (Constant), , , a.


- . () () , . . . - -

.Model Summary

,668a ,447 ,407 10991,826 ,447 11,297 1 14 ,005

,928b ,862 ,841 5698,479 ,415 39,089 1 13 ,000

,959c ,919 ,899 4546,245 ,057 8,425 1 12 ,013

Model

1

2

3

R R Square

Adjusted

R Square

Std. Error o f

the Estimate

R Square


Change Statistics




ANOVAd

1364896388,1 1 1364896388 11,297 ,005a

1691483194,9 14 120820228,2

3056379583,0 15

2634234994,8 2 1317117497 40,561 ,000b

422144588,21 13 32472660,63

3056379583,0 15

2808359494,7 3 936119831,6 45,292 ,000c

248020088,34 12 20668340,70

3056379583,0 15

Regression

Residual

Total

Regression

Residual

Total

Regression

Residual

Total

Model

1

2

3

Sum of





c.


. - , 2: -

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Coefficientsa

254548,8 50837,204 5,007 ,000

-3531,561 1050,719 -,668 -3,361 ,005241732,4 26435,070 9,144 ,000

-4023,777 550,383 -,761 -7,311 ,000

6579,154 1052,302 ,651 6,252 ,000

210159,4 23729,909 8,856 ,000

-3832,503 444,013 -,725 -8,632 ,000

6723,478 840,997 ,665 7,995 ,000

,069 ,024 ,242 2,903 ,013

(Constant)

(Constant)

(Constant)

Model

1

2

3

B Std. Error

Unstandardized

Coefficients

Beta

Standardized

Coefficients

t Sig.


Ex cluded Variabl esd

,651a

6,252 ,000 ,866 ,980

,203a 1,007 ,332 ,269 ,975

,648a 6,224 ,000 ,865 ,986

,242b 2,903 ,013 ,642 ,972

,290b ,389 ,704 ,112 ,020

,809c 1,406 ,187 ,390 ,019

Model

1

2

3

Beta In t Sig.

Partial

Correlation Tolerance

Collinearity

Statistics

Predictors in the Model: (Constant), a.

Predictors in the Model: (Constant), , b.

Predictors in the Model: (Const ant), , ,

c.


, . - . . - , , . - . SPSS - , , . ( Enter) ( Remove) . , - -.

. - , 2: -

48


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. - , 2: -

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Model Summary

,542a

,294 ,244 12414,764 ,294 5,830 1 14 ,030,928b ,862 ,841 5698,479 ,568 53,449 1 13 ,000

,959c ,919 ,899 4546,245 ,057 8,425 1 12 ,013

Model

12

3

R R Square

Adjusted

R Square

Std. Error of

the Estimate

R Square


Change Statistics




. - , 2: -

50


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ANOV Ad

898610450,157 1 898610450,2 5,830 ,030a

2157769132,8 14 154126366,6

3056379583,0 152634234994,8 2 1317117497 40,561 ,000b

422144588,213 13 32472660,63

3056379583,0 15

2808359494,7 3 936119831,6 45,292 ,000c

248020088,341 12 20668340,70

3056379583,0 15

Regression

Residual

TotalRegression

Residual

Total

Regression

Residual

Total

Model

1

2

3

Sum of





c.


Coefficients a

53454,950 12997,217 4,113 ,001

5478,706 2268,980 ,542 2,415 ,030 ,542 ,542 ,542 1,000 1,000

241732,4 26435,070 9,144 ,000

6579,154 1052,302 ,651 6,252 ,000 ,542 ,866 ,644 ,980 1,021

-4023,777 550,383 -,761 -7,311 ,000 -,668 -,897 -,754 ,980 1,021

210159,4 23729,909 8,856 ,000

6723,478 840,997 ,665 7,995 ,000 ,542 ,918 ,657 ,976 1,024

-3832,503 444,013 -,725 -8,632 ,000 -,668 -,928 -,710 ,958 1,044

,069 ,024 ,242 2,903 ,013 ,303 ,642 ,239 ,972 1,029

(Constant)

(Constant)

(Constant)

Model

1

2

3

B Std . Er ror

Unstandardized

Coefficients

Beta

Standardized

Coefficients


Correlations

Tolerance VIF



Mlr Tutorial Mr2 Spss

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