21.1.2 spss 21.1.3 spss 21.1.4 21.1.5 21.2 21.2 analysis.pdf · 12/6/2006 3:18 pm 2...

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..............................................................................................................................2 21.1 ..............................................................................................................2

21.1.1 ...................................................................................................................2 21.1.2 SPSS .......................................................................3 21.1.3 SPSS .......................................................8 21.1.4 () ................................................................9 21.1.5 () .......................................................... 11

21.2 Multiple Regression Analysis ...................................................................18 21.2.1 .................................................................................................................18

21.3 () Path Diagram Analysis.........................................................................33 21.5 ............................................................................................................45 21.6 Logistic Regression Analyses ..........................................................................45

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Regression Analysis

21.1 21.1.1 ()(scale variable)()

X1 Y1

Y() = b0 + b1X() Durbin-Watson

DW =

=

=

n

ii

n

iii

E

EE

1

2

2

21)(

Where, Ei = Yi - iY , i = 1, 2, 3, , n

Yi = i

iY = E(Yi)

DW DW 2 DW 0 DW 4

()

1. ()() 02 N(0,2)

2.

3. ()

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21.1.2 SPSS 1.Analyze/Statistics() Regression() Linear()

Linear Regression() Linear Regression

Dependent: #[cost_in] Previous Block 1 of 1 Next Independent(s): #[qua]

Method: Enter Selection Variable: Rule Case Labels:

# # #[cost_in] # [qua]

WLS>> Statistics Plots Save Options

OK Paste Reset

Cancel Help

2. Linear Regression()()[cost_in] Dependent: ()[qulit_in] Independent(s): ()

3. Method:

Enter()

Stepwise

()

Remove

Backward()

Forward()()

(Best)(Optimum)

4. Statistics Linear Regression: Statistics Regression Coefficients Estimates(

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t t )Confidence intervals( 95%)

5. Linear Regression: Statistics Model fit( R R2 Anova )Descriptives()Collinearity diagnostics[(Variance inflation factor: VIF)(cross-product deviation matrix)(Condition indices)(Tolerance)]

6.Linear Regression: StatisticsResidualsDurbin-Watson( Durbin-Watson )

Linear Regression: Statistics Regression Coefficients Estimates Confidence intervals Covariance matrix

Model fit R squared change Descriptives Part and partial correlations Collinearity diagnostics

Continue Cancel Help

Residuals Durbin-Watson Casewise diagnostics

Outliers outside 3 standard deviations All cases

7. Linear Regression: Statistics Continue Linear Regression

8. Linear Regression Plots Linear Regression: Plots

Linear Regression: Plots

Previous Scatter 1 of 1 Next

Y: *ZRESID

DEPENDNT *ZPRED *ZRESID *DRESID *SRESID *SDRESID X: DEPENDNT


Standardized Residual Plots Histogram Normal probability plot

Produce all partial plots

9. Linear Regression: Plots (*)

DEPENDNT()

*ZPRED

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*ZRESID

*DRESID(deleted residual)

*ADJPRED(adjusted predicted values)

*SRESIDstudentized

*SDRESIDstudentized

10. Linear Regression: Plots *ZRESID() Y: (Y )DEPENDNT(/) X: (X )

11. Linear Regression: Plots Standardized Residuals Plots()Histogram()Normal probability plot[(P-P)]

12. Linear Regression: Plots Continues Linear Regression

13. Linear Regression OK

14.

Descriptive Statistics Mean Std. Deviation N

2971.43 1978.82 40 180.90 16.45 40

. 0.986 Correlations

Pearson Correlation 1.000 .986 .986 1.000 Sig. (1-tailed) . .000 .000 . N 40 40 40 40

Variables Entered/Removed

Model Variables Entered Variables Removed Method 1 . Enter

a. All requested variables entered. b. Dependent Variable:

.(R2) 0.973() 97.3 %

. Durbin-Watson 0.187 0

Model Summaryb

Change Statistics Model R R Square Adjusted R Square

Std. Error of the

EstimateR Square Change F Change df1 df2

Sig. F Change

Durbin-Watson

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Std. Error of the


Sig. F Change

Durbin-Watson

1 .986a .973 .972 330.83 .973 1357.320 1 38 .000 .187 a. Predictors: (Constant), b. Dependent Variable:

. P (Sig.) 0.000 0.05()()(/)

ANOVAb

Model Sum of Squares df Mean Square F Sig. 1 Regression 148554157.246 1 148554157.246 1357.320 .000a

Residual 4158974.529 38 109446.698 Total 152713131.775 39

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

.Y() = -18497.196 + 118.677*X()

. P (Sig.) 0.000 0.05 0

. Tolerance VIF 1.000 Tolerance VIF The tolerance statistics, which assess the potential impact of multicollinearity among the independent variables on the accuracy of regression coefficients, were all higher than 0.1, thereby indicating that the results were reliable.

Coefficientsa

Unstandardized Coefficients

Standardized Coefficients 95% Confidence Interval for B Collinearity StatisticsModel

B Std. Error Beta t Sig.

Lower Bound Upper Bound Tolerance VIF (Constant) -18497.196 585.067 -31.616 .000 -19681.603 -17312.789 118.677 3.221 .986 36.842 .000 112.156 125.198 1.000 1.000

a. Dependent Variable:

Collinearity Diagnosticsa

Variance Proportions Model Dimension Eigenvalue Condition Index (Constant) 1 1 1.996 1.000 .00 .00 2 4.005E-03 22.325 1.00 1.00


.()

Residuals Statistics Minimum Maximum Mean Std. Deviation N

Predicted Value -339.66 5831.53 2971.43 1951.69 40 Residual -425.94 669.66 8.19E-13 326.56 40 Std. Predicted Value -1.697 1.465 .000 1.000 40 Std. Residual -1.287 2.024 .000 .987 40


.

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Regression Standardized Residual

2.00

1.75

1.50

1.25

1.00

.75

.50

.25

0.00

-.25

-.50

-.75

-1.00

-1.25

Histogram

Dependent Variable:

Freq

uenc

y

8

6

4

2

0

Std. Dev = .99

Mean = 0.00

N = 40.00

. P-P

Normal P-P Plot of Regression Standardi

Dependent Variable:

Observed Cum Prob

1.00.75.50.250.00

Expe

cted

Cum

Pro

b

1.00

.75

.50

.25

0.00

.

.

97.3%()

.(/)()

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Scatterplot

Dependent Variable:

70006000500040003000200010000

Reg

ress

ion

Stan

dard

ized

Res

idua

l

2.5

2.0

1.5

1.0

.5

0.0

-.5

-1.0

-1.5

21.1.3 SPSS 1. () SPSS Tranform

Compute Compute Variable

2. Compute Variable Target Variable: cost_log Functions: LG10(numexpr) Numeric Expression: (cost_in) Numeric Expression: LG10(cost_in)

3. Compute Variable OK SPSS Data Editor cost_log

Compute Variable Target Variable: Numeric Expression: Cost_log =

Type&Label LG10[cost_in]

+ < > 7 8 9 Functions: - = 4 5 6 * = ~= 1 2 3 / & 0 . ** ~ [] Delete

ARTAN[numexpr] CDFNORM[zvalue] CDF.BERNOULLI[q,p] LG10[numexpr] LN[numexpr]

If

#id #[cost_in] #[quli] #item3 #item4 #item5 #item6 #item10

OK Paste Reset Cancel Help

4. ()() SPSS TranformCompute Compute Variable

5. Compute Variable Target Variable: cost_ns Numeric Expression: (cost_in) cost_in**0.5

6. Compute Variable OK

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SPSS Data Editor cost_ns

21.1.4 ()


3.3336 .3954 40 180.90 16.45 40



.(R2) 0.969() 96.9 %

. Durbin-Watson 0.142 0

Model Summaryb


Std. Error of the


Sig. F Change

Durbin-Watson

1 .984a .969 .968 7.030E-02 .969 1196.057 1 38 .000 .142 a. Predictors: (Constant), b. Dependent Variable:

. P (Sig.) 0.000 0.05()()(/)

ANOVAb


Residual .188 38 4.942E-03 Total 6.099 39


.log(Y)() = -0.949 + 2.367*10-2*X()

. P (Sig.) 0.000 0.05 0

. Tolerance VIF 1.000 Tolerance VIF

Coefficientsa


Standardized Coefficients T Sig.

95% Confidence Interval for B

Collinearity Statistics Model

B Std. Error Beta Lower Bound Upper Bound Tolerance VIF

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Standardized Coefficients T Sig.



B Std. Error Beta Lower Bound Upper Bound Tolerance VIF1 (Constant) -.949 .124 -7.632 .000 -1.200 -.697

2.367E-02 .001 .984 34.584 .000 .022 .025 1.000 1.000





Residuals Statisticsa

Minimum Maximum Mean Std. Deviation N Predicted Value 2.6732 3.9041 3.3336 .3893 40 Residual -.1546 .1158 6.661E-16 6.939E-02 40 Std. Predicted Value -1.697 1.465 .000 1.000 40 Std. Residual -2.200 1.647 .000 .987 40

a. Dependent Variable: .


1.75

1.50

1.25

1.00

.75

.50

.25

0.00

-.25

-.50

-.75

-1.00

-1.25

-1.50

-1.75

-2.00

-2.25

Histogram

Dependent Variable:

Freq

uenc

y

6

5

4

3

2

1

0

Std. Dev = .99

Mean = 0.00

N = 40.00

. P-P

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Normal P-P Plot of Regression Standard

Dependent Variable:

Observed Cum Prob

1.00.75.50.250.00

Expe

cted

Cum

Pro

b

1.00

.75

.50

.25

0.00

Scatterplot

Dependent Variable:

4.03.83.63.43.23.02.82.62.4

Regr

essi

on S

tand

ardi

zed

Resi

dual

2

1

0

-1

-2

-3

. .

21.1.5 () Descriptive Statistics

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Mean Std. Deviation N 50.8487 19.8928 40 180.90 16.45 40


Pearson Correlation 1.000 .999

.999 1.000 Sig. (1-tailed) . .000

.000 . N 40 40 40 40

.(R2) 0.997() 99.7 % . Durbin-Watson 1.333 2

Model Summaryb


Std. Error of the


Sig. F Change

Durbin-Watson

1 .999a .997 .997 1.0196 .997 14808.173 1 38 .000 1.333 a. Predictors: (Constant), b. Dependent Variable:

. P (Sig.) 0.000 0.05()()(/)

ANOVAb


Residual 39.503 38 1.040 Total 15433.290 39


. Y () = -167.693 + 1.208*X()

. P (Sig.) 0.000 0.05 0

. Tolerance VIF 1.000 Tolerance VIF

Coefficientsa

Unstandardized Coefficients Standardized Coefficients T Sig.


Collinearity Statistics

Model B Std. Error Beta Lower Bound Upper Bound Tolerance VIF1 (Constant) -167.693 1.803 -93.001 .000 -171.343 -164.043 1.208 .010 .999 121.689 .000 1.188 1.228 1.000 1.000





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Minimum Maximum Mean Std. Deviation N Predicted Value 17.1433 79.9635 50.8487 19.8674 40 Residual -2.3619 2.2282 -6.5725E-15 1.0064 40 Std. Predicted Value -1.697 1.465 .000 1.000 40 Std. Residual -2.317 2.185 .000 .987 40 a. Dependent Variable:

.


2.001.501.00.500.00-.50-1.00-1.50-2.00-2.50

Histogram

Dependent Variable:

Freq

uenc

y

10

8

6

4

2

0

Std. Dev = .99

Mean = 0.00

N = 40.00

. P-P

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Dependent Variable:

Observed Cum Prob

1.00.75.50.250.00

Expe

cted

Cum

Pro

b

1.00

.75

.50

.25

0.00

Scatterplot

Dependent Variable:

908070605040302010

Regr

essi

on S

tand

ardi

zed

Resi

dual

3

2

1

0

-1

-2

-3

. .

21.1.6 () Descriptive Statistics

Mean Std. Deviation N

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Mean Std. Deviation N 12647194.8250 12856899.1387 40 180.90 16.45 40

Correlations


Model Summaryb


Std. Error of the


Sig. F Change

Durbin-Watson

1 .923a .853 .849 5001691.8871 .853 219.693 1 38 .000 .105


ANOVAb

Model Sum of Squares df Mean Square F Sig. 1 Regression 5.496E+15 1 5.496E+15 219.693 .000a

Residual 9.506E+14 38 2.502E+13 Total 6.447E+15 39


Coefficients Unstandardized

Coefficients Standardized Coefficients t Sig.



B Std. Error Beta Lower BoundUpper Bound Tolerance VIF1 (Constant) -1.18E+08 8.85E+06 -13.333 .000 -1.36E+08 -1.00E+08 7.22E+05 4.87E+04 .923 14.822 .000 6.23E+05 8.20E+05 1.000 1.000






Minimum Maximum Mean Std. Deviation N Predicted Value -7.49E+06 3.00E+07 1.26E+07 1.19E+07 40 Residual -5.89E+06 1.09E+07 4.66E-10 4.94E+06 40 Std. Predicted Value -1.697 1.465 .000 1.000 40 Std. Residual -1.178 2.182 .000 .987 40


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2.25

2.00

1.75

1.50

1.25

1.00

.75

.50

.25

0.00

-.25

-.50

-.75

-1.00

-1.25

Histogram

Dependent Variable:

Freq

uenc

y

10

8

6

4

2

0

Std. Dev = .99

Mean = 0.00

N = 40.00


Dependent Variable:

Observed Cum Prob

1.00.75.50.250.00

Expe

cted

Cum

Pro

b

1.00

.75

.50

.25

0.00

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Scatterplot

Dependent Variable:

50000000400000003000000020000000100000000-10000000

Regr

essi

on S

tand

ardi

zed

Resi

dual

2.5

2.0

1.5

1.0

.5

0.0

-.5

-1.0

-1.5

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21.2 Multiple Regression Analysis 21.2.1

(multiple regression)

(N )

(/)

A1 A2 A3 An

Y

()(dummy variable)123

(N ) ()A1 A2 A3

An

Y1 Y2

(Logistic regression analysis)()

(N ) ()A1 A2 A3

An

Y1 Y2 Y3 Y4 Ym

()

X1X2 X1 X2 X1=a+bX2 ()

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1.(Tolerance) 1-R2 R2 R2 0 1 0

2.(variance inflation factor; VIF) VIF

3.(Condition index; CI) CI (Dimension) 0(ill condition) 15 30 CI

1.(existence) X1X2X3Xk Y ()

2.(independent) Y

3.(linearity) Y ( X1X2X3Xk ) X1X2X3Xk

4. X1X2X3Xk Y

5. X1X2X3Xk Y

Y = B0+B1X1+B2X2+B3X3++BkXk

B0 Bk

ZY = 1ZX1 + 2ZX2 + 3ZX3 ++ kZXk k

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()0101

Homd1 Homd2 1 0

1 1 0 2 0 1

3 0 0

(loc)

Locd1 Locd2 Locd3 1 0

1 1 0 0 2 0 1 0 3 0 0 1 4 0 0 0

()SPSS

1.(dummy variables) 1 2 010 1 Transform() Recode()Into Different Variables()

2.Analyze/Statistics() Regression() Linear()Linear Regression()

Linear Regression

Dependent: #[cost_in] Previous Block 1 of 1 Next Independent(s):

#[qua]#[day_n] #[p_number]

Method: Enter Selection Variable: Rule Case Labels:

# # #[cost_in] #[qua] #[day_n] #[p_number] #[ex_id]


OK Paste Reset

Cancel Help

3.()(cost_nr) Dependent: ()

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4.()(qulit_nr)(day_n)(p_number)(ex_nr)Independent(s): ()

5. Independent(s): () Methods: Enter()

6. Statistics()Linear Regression: Statistics()






7.Linear Regression: Statistics() Regression Coefficients()Estimates()Confidence intervals()

8.Linear Regression: Statistics()Model fit()R squared change()Descriptive()Collinearity diagnostics()

9.Linear Regression: Statistics() Residuals Durbin-Watson( Durbin-Watson )

10.Linear Regression: Statistics() Continue Linear Regression()

11. Linear Regression Plots Linear Regression: Plots



Y: *ZRESID

DEPENDNT *ZPRED *ZRESID *DRESID *ADJPRED *SRESID *SDRESID

X: DEPENDNT


Standardized Residual Plots Histogram


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Normal probability plot


DEPENDNT()

*ZPRED

*ZRESID



*SRESIDstudentized

*SDRESIDstudentized




16. OK()

17.


154.5152 52.4302 33 15.5606 4.7265 33 1.4545 .5056 33 3.2727 .6261 33 2.2576 .5166 33

Correlations

Pearson Correlation 1.000 .926 .473 .627 .812 .926 1.000 .524 .633 .764 .473 .524 1.000 .090 .770 .627 .633 .090 1.000 .501 .812 .764 .770 .501 1.000

Sig. (1-tailed) . .000 .003 .000 .000 .000 . .001 .000 .000 .003 .001 . .310 .000 .000 .000 .310 . .002 .000 .000 .000 .002 .

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N 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33

Variables Entered/Removedb

Model Variables Entered Variables Removed Method

1 , ,

, a. Enter

a. All requested variables entered. b. Dependent Variable:

Model Summaryb


Std. Error of the

EstimateR Square Change

F Change df1 df2

Sig. F Change

Durbin-Watson

1 .956a .914 .901 16.4829 .914 73.945 4 28 .000 2.269 a. Predictors: (Constant), , , , b. Dependent Variable:

ANOVAb


Residual 7607.177 28 271.685 Total 87965.742 32

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

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Coefficientsa


Standardized Coefficients t Sig.



B Std. Error Beta Lower BoundUpper Bound Tolerance VIF 1 (Constant) -26.178 17.998 -1.454 .157 -63.046 10.690 8.092 1.077 .729 7.516 .000 5.887 10.298 .328 3.050 -32.228 10.758 -.311 -2.996 .006 -54.265 -10.190 .287 3.486 -6.023 7.077 -.072 -.851 .402 -20.520 8.474 .432 2.313 53.759 12.945 .530 4.153 .000 27.242 80.275 .190 5.268



Variance Proportions Model Dimension Eigenvalue Condition Index (Constant)

1 1 4.867 1.000 .00 .00 .00 .00 .00 2 7.557E-02 8.025 .05 .00 .23 .05 .00 3 4.024E-02 10.998 .24 .37 .04 .00 .00 4 1.051E-02 21.519 .66 .62 .00 .44 .15 5 6.546E-03 27.268 .06 .01 .72 .50 .85

a. Dependent Variable: Residuals Statisticsa

Minimum Maximum Mean Std. Deviation N Predicted Value 31.8591 248.8520 154.5152 50.1119 33 Residual -36.5432 37.1409 2.670E-14 15.4183 33 Std. Predicted Value -2.448 1.883 .000 1.000 33 Std. Residual -2.217 2.253 .000 .935 33



2.502.001.501.00.500.00-.50-1.00-1.50-2.00

Histogram

Dependent Variable:

Freq

uenc

y

10

8

6

4

2

0

Std. Dev = .94

Mean = 0.00

N = 33.00

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Dependent Variable:

Observed Cum Prob

1.00.75.50.250.00

Expe

cted

Cum

Pro

b

1.00

.75

.50

.25

0.00

Scatterplot

Dependent Variable:

3002001000

Regr

essi

on S

tand

ardi

zed

Resi

dual

3

2

1

0

-1

-2

-3

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()SPSS 1.Analyze/Statistics() Regression() Linear()

Linear Regression()

Linear Regression

Dependent: #[cost_in] Previous Block 1 of 1 Next Independent(s):

#[qua]#[day_n] #[p_number]

Method: Stepwise Selection Variable: Rule Case Labels:

# # #[cost_in] #[qua] #[day_n] #[p_number] #[ex_id]


OK Paste Reset

Cancel Help

2.()(cost_nr) Dependent: ()

3.()(qulit_nr)(day_n)(p_number)(ex_nr)Independent(s): ()

4. Independent(s): () Methods: Stepwise()







6.Linear Regression: Statistics() Regression Coefficients()Estimates()Confidence intervals()

7.Linear Regression: Statistics()Model

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fit()R squared change()Descriptive()Collinearity diagnostics()

8.Linear Regression: Statistics() Residuals Durbin-Watson( Durbin-Watson )

9.Linear Regression: Statistics() Continue Linear Regression()

10.Linear RegressionPlotsLinear Regression: Plots



Y: *ZRESID

DEPENDNT *ZPRED *ZRESID *DRESID *ADJPRED *SRESID *SDRESID

X: DEPENDNT


Standardized Residual Plots Histogram Normal probability plot



DEPENDNT()

*ZPRED

*ZRESID



*SRESIDstudentized

*SDRESIDstudentized




15. OK()

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16.

Variables Entered/Removeda


1 . Stepwise (Criteria: Probability-of-F-to-enter = .100).


3 . Stepwise (Criteria: Probability-of-F-to-enter = .100). a. Dependent Variable:

F 0.05 F

0.10 Model Summaryd

Change Statistics Model R R Square

Adjusted R Square

Std. Error of the


Sig. F Change

Durbin-Watson

1 .926a .857 .853 20.1138 .857 186.433 1 31 .000 2 .940b .884 .876 18.4790 .026 6.728 1 30 .015 3 .955c .911 .902 16.4043 .028 9.068 1 29 .005 2.210

a. Predictors: (Constant), b. Predictors: (Constant), , c. Predictors: (Constant), , , d. Dependent Variable:

R(Multiple correlation coefficient) R Square(R2)(Multiple determination coefficient) Adjusted R Square

Adjusted R2 = 1-[(1-R2)1

1

pn

n ]

n p

()R2R2 R2R2R2R2

ANOVAd


Residual 12541.482 31 404.564 Total 87965.742 32

2 Regression 77721.555 2 38860.777 113.803 .000b

Residual 10244.188 30 341.473 Total 87965.742 32

3 Regression 80161.786 3 26720.595 99.295 .000c

Residual 7803.956 29 269.102 Total 87965.742 32

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a. Predictors: (Constant), b. Predictors: (Constant), , c. Predictors: (Constant), , , d. Dependent Variable:

Coefficientsa


Standardized Coefficients




Lower Bound Upper Bound Tolerance VIF1 (Constant) -5.318 12.218 -.435 .666 -30.237 19.602 10.272 .752 .926 13.654 .000 8.737 11.806 1.000 1.000

2 (Constant) -29.674 14.635 -2.028 .052 -59.563 .215 8.146 1.072 .734 7.598 .000 5.956 10.335 .416 2.406 25.442 9.809 .251 2.594 .015 5.409 45.474 .416 2.406

3 (Constant) -36.541 13.191 -2.770 .010 -63.519 -9.564 7.691 .964 .693 7.982 .000 5.720 9.662 .405 2.467 49.272 11.766 .485 4.188 .000 25.207 73.336 .228 4.394 -27.403 9.100 -.264 -3.011 .005 -46.015 -8.792 .397 2.518

a. Dependent Variable: B Beta (Tolerance)(

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Minimum Maximum Mean Std. Deviation N Predicted Value 31.4748 248.7497 154.5152 50.0505 33 Residual -35.3574 37.5252 1.938E-14 15.6165 33 Std. Predicted Value -2.458 1.883 .000 1.000 33 Std. Residual -2.155 2.288 .000 .952 33



2.502.001.501.00.500.00-.50-1.00-1.50-2.00

Histogram

Dependent Variable:

Freq

uenc

y

12

10

8

6

4

2

0

Std. Dev = .95

Mean = 0.00

N = 33.00

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Dependent Variable:

Observed Cum Prob

1.00.75.50.250.00

Expe

cted

Cum

Pro

b

1.00

.75

.50

.25

0.00

Scatterplot

Dependent Variable:

3002001000

Regr

essi

on S

tand

ardi

zed

Resi

dual

3

2

1

0

-1

-2

-3

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R

R2 R2 F F

Beta 0.412 0.170 0.170 60.877 60.877 0.501 SEXDV 0.445 0.198 0.029 36.711 10.587 0.155 0.469 0.220 0.022 27.789 8.173 -0.173 0.486 0.237 0.017 22.853 6.497 0.307 0.505 0.255 0.018 20.128 7.280 -0.210 0.519 0.269 0.014 17.986 5.676 0.176 0.529 0.280 0.010 16.187 4.211 -0.173

13 ()() 0.529 0.280()28.0% 17.0%(sexdv) 2.9%2.2% 22.0% = 0.501 + 0.155SEXDV 0.173 + 0.307 0.210

+ 0.176 0.173

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21.3 () Path Diagram Analysis () -(Enter) (Path diagram)

(Enter) (Structural Equation Models)(Simultaneous Equation Models)()

1.

()()(cause)(/)(effect)

2.( Enter )(residual coefficient)

(standardized regression coefficients) (multiple regression analysis)(Enter) (residual variable path)

= 21 R

Where R2 = 3.

(restrict model)

(Prototype)(Tacq1997) 1.(quantitative)

2.(identified)

3.(recursive)

4.(static)

5.(directly observed)

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(latent variables)(hypothetical constructs)(theoretical concepts)(CSA)(LSA) Jorekog (LISREL)

6.(linear)

7.(additive)

8.(standardized)

9.(multicollinearity)(standard error)

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()(response variable)(/)(multiple regression)

(N )

(/)

[(interval scale)(ratio scale)(nominal scale)(ordinal scale)](dummy variable)123 or

(N ) ()

()(Logistic regression analysis)(Linear probability model, LPM)

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()

(N ) ()

()

X1X2 X1 X2 X1=a+bX2 ()

4.(Tolerance) 1-R2 R2 R2 0 1 0

5.(variance inflation factor; VIF)

VIF

6.(Condition index; CI) CI (Dimension) 0(ill condition) 15 30 CI

6.(existence) X1X2X3Xk Y ()

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7.(independent) Y

8.(linearity) Y ( X1X2X3Xk ) X1X2X3Xk

9. X1X2X3Xk Y

10. X1X2X3Xk Y

Y = B0+B1X1+B2X2+B3X3++BkXk

B0 Bk

ZY = 1ZX1 + 2ZX2 + 3ZX3 ++ kZXk k

()0101

Homd1 Homd2 1 0

1 1 0 2 0 1

3 0 0

(loc)

Locd1 Locd2 Locd3 1 0

1 1 0 0 2 0 1 0 3 0 0 1 4 0 0 0

SPSS

18.(dummy variables) 1 2 010 1 Transform() Recode()Into Different Variables()

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19.Analyze/Statistics() Regression() Linear()Linear Regression()

Linear Regression

Dependent: Previous Block 1 of 1 Next Independent(s):

Method: Enter Selection Variable: Rule

OK Paste Reset

Cancel Help

Case Labels:

# # # # #


20.()Dependent: ()

21.()Independent(s): ()

22. Independent(s): () Methods: stepwise()






Outliners outside 3 standard deviations All cases

24.Linear Regression: Statistics() Regression Coefficients()Estimates()

25.Linear Regression: Statistics()Model fit()R squared change()Collinearity diagnostics()

26. Continue Linear Regression()

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27. OK()

Variables Entered/Removeda



2 SEXDV . Stepwise (Criteria: Probability-of-F-to-enter = .100).





7 . Stepwise (Criteria: Probability-of-F-to-enter = .100). a. Dependent Variable:

F 0.05 F0.10

Model Summary Change Statistics

Model R R Square

Adjusted R

Square

Std. Error of

the Estimate

R Square Change

F Change df1 df2

Sig. F Change

1 .412a .170 .167 9.66 .170 60.877 1 298 .000 2 .445b .198 .193 9.51 .029 10.587 1 297 .001 3 .469c .220 .212 9.40 .022 8.173 1 296 .005 4 .486d .237 .226 9.31 .017 6.497 1 295 .011 5 .505e .255 .242 9.21 .018 7.280 1 294 .007 6 .519f .269 .254 9.14 .014 5.676 1 293 .018 7 .529g .280 .262 9.09 .010 4.211 1 292 .041

a. Predictors: (Constant), b. Predictors: (Constant), , SEXDV c. Predictors: (Constant), , SEXDV, d. Predictors: (Constant), , SEXDV, , e. Predictors: (Constant), , SEXDV, , , f. Predictors: (Constant), , SEXDV, , , , g. Predictors: (Constant), , SEXDV, , , , , R(Multiple correlation coefficient) R Square(R2)(Multiple determination coefficient) Adjusted R Square

Adjusted R2 = 1-[(1-R2)1

1

pn

n ]

n p

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()R2R2 R2R2R2R2

ANOVAh

Model Sum of Squares df Mean

Square F Sig.

1 Regression 5680.605 1 5680.605 60.877 .000a Residual 27807.431 298 93.314 Total 33488.037 299

2 Regression 6637.702 2 3318.851 36.711 .000b Residual 26850.334 297 90.405 Total 33488.037 299

3 Regression 7359.181 3 2453.060 27.789 .000c Residual 26128.855 296 88.273 Total 33488.037 299

4 Regression 7922.223 4 1980.556 22.853 .000d Residual 25565.814 295 86.664 Total 33488.037 299

5 Regression 8539.993 5 1707.999 20.128 .000e Residual 24948.044 294 84.857 Total 33488.037 299

6 Regression 9014.136 6 1502.356 17.986 .000f Residual 24473.901 293 83.529 Total 33488.037 299

7 Regression 9362.054 7 1337.436 16.187 .000g Residual 24125.983 292 82.623 Total 33488.037 299

a. Predictors: (Constant), b. Predictors: (Constant), , SEXDV c. Predictors: (Constant), , SEXDV, d. Predictors: (Constant), , SEXDV, , e. Predictors: (Constant), , SEXDV, , , f. Predictors: (Constant), , SEXDV, , , , g. Predictors: (Constant), , SEXDV, , , , , h. Dependent Variable:

Coefficientsa





Tolerance VIF

1 (Constant) -.668 3.302 -.202 .840 .249 .032 .412 7.802 .000 1.000 1.000

2 (Constant) -3.126 3.337 -.937 .350 .255 .032 .422 8.106 .000 .996 1.004 SEXDV 3.580 1.100 .169 3.254 .001 .996 1.004

3 (Constant) 2.205 3.788 .582 .561

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Tolerance VIF

.257 .031 .425 8.265 .000 .996 1.004 SEXDV 3.487 1.088 .165 3.206 .001 .996 1.004 -.338 .118 -.147 -2.859 .005 .999 1.001

4 (Constant) -8.612 5.665 -1.520 .130 .316 .038 .521 8.222 .000 .644 1.553 SEXDV 3.247 1.082 .154 3.001 .003 .988 1.012 -.379 .118 -.165 -3.206 .001 .980 1.020 .347 .136 .163 2.549 .011 .634 1.578

5 (Constant) -5.066 5.758 -.880 .380 .288 .039 .476 7.339 .000 .601 1.663 SEXDV 3.359 1.071 .159 3.135 .002 .987 1.014 -.327 .119 -.142 -2.761 .006 .954 1.048 .582 .160 .273 3.629 .000 .446 2.241 -.323 .120 -.199 -2.698 .007 .465 2.150

6 (Constant) -7.265 5.787 -1.256 .210 .227 .047 .376 4.878 .000 .420 2.378 SEXDV 3.194 1.065 .151 2.998 .003 .982 1.018 -.355 .118 -.154 -3.005 .003 .945 1.058 .683 .165 .321 4.147 .000 .417 2.400 -.325 .119 -.200 -2.734 .007 .465 2.150 .318 .133 .179 2.383 .018 .443 2.258

7 (Constant) -5.110 5.850 -.874 .383 .303 .059 .501 5.114 .000 .257 3.893 SEXDV 3.279 1.060 .155 3.093 .002 .981 1.020 -.397 .119 -.173 -3.329 .001 .917 1.090 .653 .164 .307 3.973 .000 .413 2.418 -.342 .118 -.210 -2.883 .004 .463 2.160 .313 .133 .176 2.363 .019 .443 2.259 -.409 .199 -.173 -2.052 .041 .347 2.878


B Beta (Tolerance)(

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Collinearity Statistics Model Beta In t Sig. Partial Correlation Tolerance VIF Minimum Tolerance

-.022a -.359 .720 -.021 .719 1.392 .719 -.160a -1.709 .089 -.099 .317 3.157 .317 .063a .840 .402 .049 .496 2.015 .496 .137a 2.014 .045 .116 .594 1.685 .594 -.123a -1.424 .156 -.082 .373 2.683 .373 .102a 1.354 .177 .078 .488 2.048 .488 -.152a -2.910 .004 -.167 .999 1.001 .999 -.076a -1.191 .235 -.069 .680 1.471 .680 SEXDV .169a 3.254 .001 .186 .996 1.004 .996

2 -.088b -1.410 .160 -.082 .691 1.448 .691 -.058b -1.023 .307 -.059 .850 1.177 .850 -.072b -1.246 .214 -.072 .804 1.244 .804 .135b 2.100 .037 .121 .646 1.549 .646 -.046b -.744 .457 -.043 .709 1.410 .709 -.101b -1.074 .284 -.062 .303 3.301 .303 .045b .604 .547 .035 .493 2.027 .492 .109b 1.603 .110 .093 .582 1.718 .582 -.131b -1.540 .125 -.089 .372 2.685 .371 .094b 1.262 .208 .073 .488 2.050 .486 -.147b -2.859 .005 -.164 .999 1.001 .996 -.076b -1.215 .225 -.070 .680 1.471 .678

3 -.053c -.846 .398 -.049 .661 1.514 .661 -.038c -.675 .500 -.039 .836 1.196 .836 -.049c -.838 .403 -.049 .786 1.272 .786

.163c 2.549 .011 .147 .634 1.578 .634 -.015c -.245 .807 -.014 .686 1.457 .686 -.119c -1.278 .202 -.074 .302 3.315 .302 .040c .542 .589 .032 .493 2.028 .491 .159c 2.321 .021 .134 .554 1.806 .554 -.188c -2.201 .029 -.127 .357 2.803 .357 .106c 1.444 .150 .084 .486 2.057 .486 .138c 1.444 .150 .084 .289 3.461 .289

4 -.199d -2.698 .007 -.155 .465 2.150 .446 -.144d -2.265 .024 -.131 .629 1.590 .477 -.140d -2.201 .029 -.127 .635 1.576 .512 -.218d -2.662 .008 -.153 .378 2.645 .349 -.021d -.206 .837 -.012 .245 4.077 .245 .003d .043 .965 .003 .474 2.111 .338 .108d 1.457 .146 .085 .471 2.121 .323 -.160d -1.861 .064 -.108 .349 2.864 .324 .178d 2.340 .020 .135 .443 2.258 .441 .230d 2.340 .020 .135 .263 3.800 .263

5 -.068e -.874 .383 -.051 .419 2.388 .310

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Collinearity Statistics Model Beta In t Sig. Partial Correlation Tolerance VIF Minimum Tolerance

-.051e -.611 .542 -.036 .370 2.706 .271 -.114e -.818 .414 -.048 .131 7.628 .131 -.071e -.689 .492 -.040 .238 4.205 .238 .040e .534 .594 .031 .459 2.180 .306 .121e 1.652 .100 .096 .469 2.130 .306 -.176e -2.073 .039 -.120 .347 2.878 .318 .179e 2.383 .018 .138 .443 2.258 .417 .232e 2.383 .018 .138 .263 3.800 .263

6 -.060f -.772 .441 -.045 .418 2.393 .309 -.046f -.556 .579 -.033 .369 2.708 .271 -.101f -.732 .465 -.043 .131 7.639 .131 -.067f -.651 .516 -.038 .238 4.206 .238 .030f .411 .682 .024 .457 2.187 .258 .124f 1.713 .088 .100 .469 2.131 .248 -.173f -2.052 .041 -.119 .347 2.878 .257 .f . . . .000 . .000 7 -.075g -.968 .334 -.057 .414 2.414 .250 -.065g -.795 .427 -.047 .365 2.742 .253 -.134g -.973 .331 -.057 .129 7.737 .129 -.064g -.625 .532 -.037 .238 4.207 .182 -.014g -.181 .856 -.011 .420 2.382 .152 .073g .900 .369 .053 .377 2.653 .124 .g . . . .000 . .000

a Predictors in the Model: (Constant), b Predictors in the Model: (Constant), , SEXDV c Predictors in the Model: (Constant), , SEXDV, d Predictors in the Model: (Constant), , SEXDV, , e Predictors in the Model: (Constant), , SEXDV, , , f Predictors in the Model: (Constant), , SEXDV, , , , g Predictors in the Model: (Constant), , SEXDV, , , , , h Dependent Variable:



SEXD

V

1 1 1.986 1.000 .01 .01 2 1.437E-02 11.755 .99 .99

2 1 2.609 1.000 .00 .00 .05 2 .377 2.629 .01 .01 .93 3 1.406E-02 13.623 .99 .98 .02

3 1 3.516 1.000 .00 .00 .03 .01 2 .417 2.905 .00 .00 .94 .01 3 5.499E-02 7.996 .03 .15 .01 .86 4 1.271E-02 16.628 .97 .85 .03 .12

4 1 4.424 1.000 .00 .00 .02 .00 .00

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SEXD

V

2 .426 3.221 .00 .00 .96 .01 .00 3 9.006E-02 7.008 .00 .06 .01 .02 .41 4 5.392E-02 9.057 .01 .06 .01 .95 .02 5 5.954E-03 27.256 .98 .88 .00 .01 .57

5 1 5.327 1.000 .00 .00 .01 .00 .00 .00 2 .435 3.499 .00 .00 .97 .01 .00 .00 3 .149 5.988 .00 .04 .00 .03 .05 .16 4 5.405E-02 9.928 .01 .05 .01 .92 .03 .00 5 3.009E-02 13.305 .00 .02 .00 .03 .67 .79 6 5.734E-03 30.479 .98 .89 .00 .01 .25 .04

6 1 6.238 1.000 .00 .00 .01 .00 .00 .00 .00 2 .443 3.752 .00 .00 .97 .00 .00 .00 .00 3 .207 5.485 .00 .01 .01 .00 .04 .10 .03 4 5.842E-02 10.334 .00 .01 .01 .98 .02 .02 .03 5 3.314E-02 13.719 .01 .00 .00 .00 .47 .80 .06 6 1.409E-02 21.041 .09 .25 .00 .01 .28 .03 .86 7 5.676E-03 33.152 .90 .73 .00 .01 .19 .05 .02

7 1 7.179 1.000 .00 .00 .01 .00 .00 .00 .00 .00 2 .452 3.987 .00 .00 .96 .00 .00 .00 .00 .00 3 .238 5.487 .00 .00 .02 .00 .03 .09 .02 .01 4 6.324E-02 10.655 .00 .00 .01 .89 .03 .02 .00 .02 5 3.351E-02 14.637 .00 .00 .00 .01 .41 .74 .11 .01 6 2.144E-02 18.297 .00 .01 .00 .03 .19 .10 .63 .24 7 7.808E-03 30.321 .56 .09 .00 .06 .28 .01 .17 .49 8 5.100E-03 37.520 .43 .89 .00 .00 .06 .03 .07 .24

a Dependent Variable:

R

R2 R2 F F

Beta 0.412 0.170 0.170 60.877 60.877 0.501 SEXDV 0.445 0.198 0.029 36.711 10.587 0.155 0.469 0.220 0.022 27.789 8.173 -0.173 0.486 0.237 0.017 22.853 6.497 0.307 0.505 0.255 0.018 20.128 7.280 -0.210 0.519 0.269 0.014 17.986 5.676 0.176 0.529 0.280 0.010 16.187 4.211 -0.173

13 ()() 0.529 0.280()28.0% 17.0%(sexdv) 2.9%2.2% 22.0% = 0.501 + 0.155SEXDV 0.173 + 0.307 0.210

+ 0.176 0.173

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45

21.5 Sulek, J. M. and Hensley, R. L. 2004. The relative importance of food, atmosphere, and fairness of

wait: the case of a full-service restaurant. Cornell Hotel and Restaurant Administration Quarterly, 45(3):235-247.

Chang, C. P. 2006. A multilevel exploration of factors influencing the front-line employees service quality in international tourist hotels. Journal of American Academy of Business, 9(2):285-293.

Mahon, D. and Cowan, C. 2004. Irish consumers perception of food safety risk in minced beef. British Food Journal, 106(4):301-312.

21.6 Logistic Regression Analyses Juric, B., Cornwell, T. B. and Mather, D. 2002. Exploring the usefulness of an ecotourism interest

scale. Journal of Travel Research, 40:259-269. Knight, A. and Warland, R. 2004. The relationship between sociodemographics and concern about

food safety issues. The Journal of Consumer Affairs, 38(1):107-120.

21.1.2 spss 21.1.3 spss 21.1.4 21.1.5 21.2 21.2 analysis.pdf · 12/6/2006 3:18 pm 2...

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