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47

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sÛbrÛa@Ý—ÐÛa

48

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49

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sÛbrÛa@Ý—ÐÛa

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sÛbrÛa@Ý—ÐÛa

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sÛbrÛa@Ý—ÐÛa

54

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sÛbrÛa@Ý—ÐÛa

56


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،א א א א א، א אא אא




107

אאא. אאאא .)1(30א

− üaÞýÔnÛaïma‰ïÓaìjÜÛ:

אאא אאא א،אאא .אא

אאאא )T(،)F(א،)R2(א א א ، א

א .א

− Šbjn‚gãb¤ïÓaìjÛa)Šbjn‚apbjqåíbjnÛa(Homoscedasticity:

אא אאאאאא אא א ،

א.א אbiased،אאא .)2(א

òÔibÛa@Âë‹“Ûa@Õîjm@òîÐî×@òîÜàÈÛa@òÜrßþbi@|šìäë:

Þbrß:

1 Palta, Mari, (2003)," Quantitative Methods in population health: Extensions of ordinary regression", Wiley – IEEE, p 6.

2 Berk, Richard A., (2003)," Regression analysis: a constructive critique", Sage publications Inc., p 144.



ÉibÛa@Ý—ÐÛa

108

א א א :א

Ý‚‡Ûa Úýènüa

158 127

160 130

175 129

157 125

130 115

150 119

152 121

162 124

125 97

115 92

125 85

145 114

140 118

142 110

140 119

138 104

126 102

lìÜ¾a:א .؟5٪אא

paì©a:




109

:א )1

אRegressionאא،Analyzeא )2א،Linearא א א :א



ÉibÛa@Ý—ÐÛa

110

אא :אאא . אאאConsumption[y]א

Respondent.

א . אאא Income(x)אIndependent(s).

אא،Statisticsא )3 :אא




111

א א א :א. Durbin – Watson،]

אא א א א א אא א א .]Residualsא

א . א EstimatesModel Fitא

.אContinueא . א א .א

א )4 א א Saveא:א

א א א :א



ÉibÛa@Ý—ÐÛa

112

א א א :א. Unstandardizedא א א

Predicted Values

. standardizedא א אResiduals

אContinueא . א א .א

א )5 א א אPlotsא:אא א :א




113

א א א :א. Normal probability plotא א א

Standardized Residual Plots.אContinueא . א א .א

א )6 א א ،okא:אא :،א

1 .Šbjn‚a@ïöb—ygë@‡í‡znÛa@ÝßbÈß@Þë‡uDurbin-Watson

א א א :א



ÉibÛa@Ý—ÐÛa

114

( R:א)א א א א ).א

( R Square:א)א.(

( Adjusted R Square:א .א

( Std. Error of the Estimate:אאא،א א א)MSE()1(.

( Durbin-Watson:א)א–א(א א א א א .א

2 .åíbjnÛa@ÝîÜ¥@Þë‡uANOVA:

א אא :א( Model:א א .א( Sum of Squares:א.

1 MSE א.




115

( df:א.

( Mean Square:א.

( F:)(א.

( Sig:אP.Value،אאא .א

3 .Ša‡®üa@pýßbÈß@Þë‡u:

א אא :א( Unstandardized Coefficients:א

אConstantא א ،)א א،)אא א ).Std. Error(א

( Standardized Coefficients)א).א( t:)(אא.



ÉibÛa@Ý—ÐÛa

116

( Sig:אP.Value]אאא א א )].א(א

4 .@@@ïÈîàvnÛa@Þbànyüaë@‡çb“¾a@ïÈîàvnÛa@Þbànyüa@µi@òÓýÈÜÛ@ïãbîi@ÝØ’ÉÓìn¾aN@ @

@ @

א א אא א א אא א א א א .א

ÜßòÃìz:




117

، Data Viewא −א Standardized(א

Residuals(אres_1،א،pre_1א)y(א

:א

א − א א א אאא .אאאא



ÉibÛa@Ý—ÐÛa

118

، א א אא א א א א א א).א(א

א א א א א א אא .א

paì©aZ@ @

אScatterאGraphsא )1 א א:א

Simple،Defineא )2

א א א :א




119

א א א :אא ) א א Standardא

Residual [zre_1]א א .Y Axisא

א ) א א)א(אUnstandardized Predicted Value [pre_1]

א א .X Axisא

אokא )3 א א :א



ÉibÛa@Ý—ÐÛa

120

@ @

אא ،אאא א אאא א :אא

ë@wöbnäÛa@óÜÇ@ÕîÜÈnÛa@xˆì¹@òîyý–@óÜÇ@áØäŠa‡®üa@ð‰Ûa@@éÔîÏìmZ@ @

@üëc :Š‡Ô¾a@Ša‡®üa@xˆì¹Z@ @

Y = 8.149 + 0.735 X




121

א א א :،אא ◘ .0β:()8.149(א (א ◘ ).1β:()0.735(א

@ @

@bîãbq@ZÛaÂë‹“òí‹ÄäÛa@N

1( ÖbÐmg)òîÔäß(paŠb’gòàîÓëpýßbÈßŠa‡®üa:

א א ،אאא :א

אאא .).א(א . )אא(א

א א א א א ،.

א :אא

Consumption = 8.149 + 0.735 Income

אאא אא אא א .א

@òÃìzÜßòßbç:



ÉibÛa@Ý—ÐÛa

122

אא אא אא א،אאא

א אא.אא אא ،א א

א .א

2( ñŠ‡ÔÛaòíÐnÛaxˆìàäÜÛ:

אאאא אא)R-Sq(א א،]R-Sq (adj)[א.א

ÕîÜÈnÛa@òÔí‹ :א א א ،אא

א77.1٪אא א א )א(،א77.1٪ א ،)א(אאא

א77.1٪ א א אא ، אא٪)22.9( א א .א

brÛbq:Zòîšbí‹Ûa@Âë‹“Ûa@:

1( òíìäÈ¾aòîÜØÛaxˆìàäÜÛ:




123

◘ ë‹ÐÛaòîöb—ya:א):0H(אא .א

א א):1H(א .א

◘ ë‹ÐÛaÝØ“i‹‚e:א):0H(אא א

.א( (א א):1H(א אא א

).א(

◘ Íí‹ÐmwöbnäÛaÕîÜÈnÛaëbèîÜÇ:

ANOVAא

א א

אSource א

DF א

SS א

MS א

F cal

P. Value

א 0.0000 54.814 2244.036 2244.036 1א

* * 40.939 614.082 15 א

* * * 2858.118 16 א

ÕîÜÈnÛa:



ÉibÛa@Ý—ÐÛa

124

،P.Valueא:אאאא،5٪א

א אא،א אא .אא

2( òíìäÈ¾aòîö§axˆìàäÜÛ:

◘ âìèÐ¾a:א אאא אא אא.אאאא، .א

◘ ña†þa:א)(]T - test[،

◘ ÝØ’ë‹ÐÛaòîöb—ya:− )B0(:

0:

0:

01

00

≠

=

BHBH

− )B1(:

0:

0:

11

10

≠

=

BHBH

◘ Íí‹ÐmwöbnäÛaÕîÜÈnÛaëbèîÜÇ:א א




125

א

א א P. Value )(א

B0 8.149 0.569 0.578

B1 0.735 7.404 0.000

◘ ÕîÜÈnÛa:)B0(א ،P.Value0.578

אאא،5٪א א אאא .א

)B1(،אP.value0.000

אאא،5٪אא א)B1(א .א

3( Âë‹’pbÈi‹¾aô‹Ì—Ûaòí†bÈÛa.

Þëþa@Â‹“Ûa:òîÛa‡nÇa@ÉíŒìnÛa@ïÛbànyüa@ïÓaìjÜÛNormality Test

◘ ë‹ÐÛaaòîöb—y:א):H0(אא אא .א

א א:)H1(א .אאא



ÉibÛa@Ý—ÐÛa

126

אא אא א :א

¶ëþa@òÔí‹Ûa@Zbîãbîi@ @

א א א א א، א א א א אא א

אא א א א א א א א אא א א א،א א

אא.א .א

א אא א ، אא א ، א (א

א ).א




127

@ @òîãbrÛa@òÔí‹Ûa@Z@Šbjn‚a@åß@Ý×@Šbjn‚a@âa‡ƒnbi@bîibyI@Òë‹uìßìÜ×–Òë‹ä@H@L

@Šbjn‚aëI@ëib’–ÙîÜíë@HZ@ @

paì©a@ @

Descriptive א א ، Analyze א )1

Statistics א Explore ، א א :א א



ÉibÛa@Ý—ÐÛa

128

:ع احلواري الذي أمامكيف املرب )2

املربع الذي إىل Standardized Residualقم بنقل املتغري )أ .Dependent Listبعنوان

.Plots اختر Displayومن االختيارات )ب الذي احلواري نفس املربع يف ..Plots االختيار فوق أنقرمث )ج

:يلي جديد، كما حواريأمامك، سيظهر لنا مربع

:قم مبا يلي: ييف هذا املربع احلوار )3




129

من خالل Normality Plots with testsنشط االختيار )أ الضغط مرة واحدة باملاوس يف املربع األبيض الذي أمام هذا

.االختيار مث قم بتعطيل .Noneر اخت Boxplotsمث من االختيارات )ب

هو، اهلدف من هذه اخلطوة Stem – and – Leafاالختيار .إليها حاجة املخرجات اليت لسنا يفتقليل

. للعودة للمربع احلواري السابقContinueمث اضغط )ج

نافـذة املخرجـات سنحصل على النتـائج التاليـة يف ، OKمث اضغط )4Output:

◘ ÕîÜÈnÛaë@wöbnäÛa@Íí‹Ðm: אא אאאא

א א

P. value

0.20 17 0.129–א

0.780 17 0.970–א



ÉibÛa@Ý—ÐÛa

130

◘ ÕîÜÈnÛa@òÔí‹ :א אP.Valueא

אאאא،א0.05 א،א אא א אא.א א[א ]אאאא

א א א .אא

@Â‹“ÛaïãbrÛa@ZüaÞýÔn@ïma‰ÛaÓaìjÜÛ@ï@Z

◘ òîöb—ya@ë‹ÐÛa:אא:)H0(אא אא(א

א ).אא אא:)H1(א אא(א

א ).א

◘ áØä@ña†c:אאאא אא

Durbin – Watson Test

◘ @‰îÐäm@paì‚Šbjn‚üa:©a¶ëþa@ñì:א)DW(:




131

א אDurbin-Watsonאא א א אא،א

)2.224.(

òîãbrÛa@ñì©a:א א(א Durbinא

– Watson:(

א א ،Durbin – Watson

א: א،)dL(א א)du.(א א א)K(א ،)n.(

:،k=1،n=15א

36.108.1

==

u

L

dd

òrÛbrÛa@ñì©a:א א :א

אא אאאאא ،אא:אא

א ))11 :אא ).( dL<DW<4-4א:אא ).DW<dL>0(א:א

א ))22 :אא ).DW<4-du>2(א:א



ÉibÛa@Ý—ÐÛa

132

א ).du<DW<2 (א:א

،א ))33 א א:אא

א (א :4-du<DW<4-dL.().dL<DW<du(:אא

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.DW2.224א −− du 1.36)א – Durbinא

Watson(]Kא)א א א،)א(15א)א

)2.64 =4-1.36(א.]5٪− )2<DW<4-du(،2<DW(2.22417)<2.64.

áq@åßë@Ša‹ÔÛa@æìØí:

אאאאא א(אאא א).א אא[א ]אא

א א א .אא

@@sÛbrÛa@Â‹“Ûa@ZŠbjn‚a@ãb¤@ïÓaìjÛa)אא:(

א :א




133

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א(א،א :)א

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@@@@bäç@Åyýíæc:א אאאאאאא(א אאא ،)אא

א ،אא.א א[א

א]א א א א .א



ÉibÛa@Ý—ÐÛa

134

אא ،אGoldfield – Quandtא.

òîãbrÛa@òÔí‹Ûa@Z@òîibä@òÔí‹ÛaIòÔí‹ Goldfield – Quandt(

paì©aZ@ @

א .1 אא א :.א

אSort CasesאDataא . א א :א

א . אIncome (x)א א Sortא

by.א א א א אSort Orderא (Ascendingא

.(




135

אokא . א א(א،א)א(א) ،:

א20٪א .2 א א)1(א ،3.4

א.א3، א אא א).10(،)9(،)8(א

) مشاهدة3.4 = 0.20 × 17( 1



ÉibÛa@Ý—ÐÛa

136

א:א7، א א אא)7(א...........)2(،)1( א א א ،

).17(א........)12(،)11(

)SSE(א .3א ، אא .א

א :א

א)( א א א א א، א :א

אSelect CasesאDataא א א :א




137

א א א :א

א Based on time or case rangeא

Rangeא א א :א

א א א א:אא א א Continueא.א

א ، א א א،okא، א א א א א א א

:א



ÉibÛa@Ý—ÐÛa

138

אא (x)(y)א

،.א א אא :א




139

א )SSE()1(א:א).411.690(א

I2HISSE@H@אא )(

@Zא @



ÉibÛa@Ý—ÐÛa

140

).44.824(א)SSE()2(אא

)()F(א،:

( )( ) 1088.0

690.411824.44ˆ

1

2 ===SSESSE

F




141

)()F(אאאא א)α(א ،)F(א)F(

א א אאא ، .א

@bäçë@‡−æc)F(א0.1088א)א )05.0,5,5Fא5.05א א، אא

א.א אא .א



ÉibÛa@Ý—ÐÛa

142

@ @

@ @

@ @

@ @



143

@Ý—ÐÛaåßbrÛa @ @

@ @

@ @

@ @

@ @

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@ @

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144



Ša‡®üa@ÝîÜ¥†‡Èn¾a@ï©a@

145

א ، א א א אא א א א @OLS،@â‡Ç@@†ìuëא

xaë†Œa@ó‚@µi@paÌn¾a@òíÐnÛaMulticollinearity.

ó©a@xaë†Œübi@‡—ÔíZ@ @

אאא א .אא א אא אא א א

א:א אא ، א א .)1(אא

א א א :אא

¶ëþa@ òÔí‹ÛaZ@ ، א א א אאא אא א א א.0.7−،0.7+א

1 Makridakis, Spyros, (1998), " Forecasting: methods & applications", 3 rd Edition, John Wiley & sons Inc., p 288.

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Multiple Linear Regression@




146

òîãbrÛa@ òÔí‹ÛaZא Varianceא

inflation factor (VIF)א א א.א(VIF))5(אא .א

Þbrß:אא @:א @

@ @

lìÜ¾a:א ،א ٪.5א




147

paì©a:

:א )1

אAnalyzeא )2 א ،RegressionאאLinearא א א א ،:




148

א א א :א

א . א[y]א א .Respondentאא . א אא(x1, X2, X3)א א

Independent(s).

אStatisticsא . א א א ،:




149

א א א :א

. Durbin – Watson،]א א א א א א א

א א .]Residualsאא. Collinearity diagnostics ]

א א א א א א א א א אא ].א

א . א EstimatesModel Fitא

..אאContinueא . א .א




150

א )3 א א Saveא:א

א א א :א

א א א :א. Unstandardizedא א א

Predicted Values

. standardizedא א אResiduals





151

א )4 א א Plotsא:א

א א א :א

א א א :א. Normal probability plotא א א

Standardized Residual Plots.


א )5 א א ،okא:אא .א

@ð‰Ûa@Ša‡®üa@xˆì¹@òîyý–@óÜÇ@áØäë@wöbnäÛa@óÜÇ@ÕîÜÈnÛaéÔîÏìm@Z

üëc@ZŠ‡Ô¾a@Ša‡®üa@xˆì¹Z@ @

Y = 4.421 + 0.345 X1 – 1.367 X2 + 0.209 X3




152

א א א :،אא ◘ .0β:()4.421(א (א ◘ א א

o )1β()0.345.(

o )2β()-1.367.(

o )3β()0.209.(

@bîãbq@ZÛaÂë‹“òí‹ÄäÛa@N@ @

1( @ÖbÐmaIòîÔäß@ëc@HŠa‡®üa@pýßbÈß@òàîÓë@paŠb’g:

א א א א א אא א א ، א א

א .אא

2( ñŠ‡ÔÛaòíÐnÛaxˆìàäÜÛ:א א א א א

א א ، א65.2٪א ،א א אX1, X2, X3(65.2٪(א א אא א)Y(א א٪)34.8(، אא .א

brÛbq@ZZòîšbí‹Ûa@Âë‹“ÛaN@ @




153

1H @xˆìàäÜÛ@òîÜØÛa@òíìäÈ¾aZ

א ◘ :אא א):0H(א .אא א):1H(א .א

:א ◘א א):0H(א א

.א( (א א):1H(א א א א

).א(

:אא ◘

:אאא א א:א

P.Valueא ،5٪،א ، א א א א א

אא א .א




154

ANOVAא

א אא

אSource א

DF א

SS

אMS

אF cal

P. Value

א 0.000 12.874 2.292 6.877 3א

* * 0.178 2.849 16 א

* * * 9.726 19 א

2( xˆìàäÜÛ@òîö§a@òíìäÈ¾a:

א ◘ :א− )B0:(

0:

0:

01

00

≠

=

BHBH

− )B1:(

0:

0:

11

10

≠

=

BHBH

− )B2:(

0:

0:

21

20

≠

=

BHBH




155

− )B3:(

0:

0:

31

30

≠

=

BHBH

א ◘ :א

אא אא

א

א א P. Value )(א

B0 4.421 11.895 0.000

B1 0.345 0.931 0.365

B2 -1.367 -5.545 0.000

B3 0.209 3.854 0.001

:א)B0:(אP.Value،א5٪א א א א، א

א א .א




156

)B1:(אP.Value0.365

א5٪א א א ،)B1(א א .א

)B2:(אP.Value،א5٪א א א ،

)B2(א א .א

)B3(،אP.Value0.001،א5٪א א א ،

)B3(א א .א

3( Âë‹’pbÈi‹¾aô‹Ì—Ûaòí†bÈÛa.

Â‹“Ûa Þëþa:òîÛa‡nÇa nÛaÉíŒì ïÛbànyüa ïÓaìjÜÛNormality Test

א ◘ :אא א):H0(א א א .אא א):H1(א א א .א

א א א א א :א

¶ëþa@òÔí‹Ûa :bîãbîi@ @




157

אא א א אא א א א .א

א:א א אא א ، א(א ).א

òÔí‹Ûa Ûaòîãbr :bîiby âa‡ƒnbi Šbjn‚a Ý× åß Šbjn‚a) Òë‹ªìÜ× – Òìã‹àî( ،Šbjn‚aë) ëib’ – ÙîÜíë:(@ @

paì©a@ @




158

Descriptive א א ، Analyze א )1

Statistics א Explore ، א א :א א

: يف املربع احلواري الذي أمامك )2

إىل املربع Standardized Residualاملتغري قم بنقل -أ .Dependent Listالذي بعنوان

.Plots اختر Displayومن االختيارات -ب يف نفس املربع احلواري الذي ..Plotsمث أنقر فوق االختيار -ج

:أمامك، سيظهر لنا مربع حواري جديد، كما يلي




159

:قم مبا يلي: يف هذا املربع احلواري )3

من Normality Plots with testsنشط االختيار -أ خالل الضغط مرة واحدة باملاوس يف املربع الذي أمام هذا

.االختيارمث قم بتعطيل . None اختر Boxplotsمث من االختيارات -ب

، اهلدف من هذه اخلطوة Stem – and – Leafاالختيار .املخرجات اليت لسنا يف حاجة إليهاتقليل وه

. للعودة للمربع احلواري السابقContinueمث اضغط -ج

، سنحصل على النتـائج التاليـة يف نافـذة املخرجـات OKمث اضغط )4Output:




160

א ◘ :אא א א א א א

א

אP. value

0.149 20 0.166 –א

0.189 20 0.929 –א

:א ◘א אP.Valueא

א0.05 א א א א ، אא א ، א אא

א.א א[א א א א ]אא א א א .אא

Â‹“Ûa@ïãbrÛa@ZÞýÔnüa@ïma‰Ûa@ïÓaìjÜÛ@Z@ @

א ◘ :אא א:)H0(א א אא(א

א ).אא א):H1(א א א(א א

א ).א




161

א ◘ :אא א א א א א

Durbin – Watson Test.

א ◘ :א):DW(א:א א

א אא ،א−אא)DW()1.488.(

א:א א א(א אDurbin – Watson:(

א :k=3،n=16א

73.186.0

==

u

L

dd

א:א א א :אא א אא ،א א א א אא :א

א ))11 :אא ).( dL<DW<4-4א:אא ).DW<dL>0(א:א




162

א ))22 :א

א ).DW<4-du>2(א:אא ).du<DW<2 (א:א

33(( ، א א אא :א

א (א :4-du<DW<4-dL.(א ).dL<DW<du(:א

:dL<DW<du،)0.86<DW<1.73(.−א−)א–א( א א א

אאא.א א .א

@sÛbrÛa@Â‹“Ûa@@Z@ïÓaìjÛa@ãb¤@Šbjn‚aIåíbjnÛa@pbjq@Šbjn‚aZH@ @

א :א

òÔí‹Ûa ¶ëþa :ïãbîjÛa@á‹Ûa@Þý‚@åß:

א א א א א א .א

paì©aZ@ @

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163

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164

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165

א א א א א א.אGoldfield – Quandtא.

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א:א8 א א ،)1(א

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167

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Rangeא א א :א




168

o א א א א:א א א א،Continueא.א א א אאokא א א א א ،

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169

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170

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Factor Analysis



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178

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179

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א אFactorא א א א ،:



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181

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183

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ÉbnÛa@Ý—ÐÛa@

184

o Varimax.

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אא אאא א א א א(א .)א

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185

א א א :א

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187

wöbnäÛa@óÜÇ@ÕîÜÈnÛa:א אא

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188

אReliabilityא ، א א אא. א)Kaiser 1974(א א

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Matrix1א identity Matrix.א :אא .אא:א

مصفوفة الوحدة هي املصفوفة اليت تتكون مجيع عناصرها من الصفر، فيما عدا العناصر املوجـودة 1

ة الثالثـة على القطر الرئيسي تساوي الواحد الصحيح، وفيما يلي شكل مصفوفة الوحدة من الدرج ) أعمدة3 صفوف و 3أي (

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡=

100010001

A




189

א .אא:א

Íí‹ÐmöbnäÛaw:

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אP.Value

108.896550.000

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òÜØ“ßxaë†Œüaï©aMulti-Collinearity:

א א א אא.אא א

)א(0.0000310.00001

א א א א .א.א

òÃìzÜßòßbç:



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190

א .1 א אאא אא א א

.א

א .2 אאא ، א א א

.א@ @@brÛbq@Z‹Ð¾a@ïÜØÛa@åíbjnÛa@Þë‡uTotal Variance Explained

א :א3אÞëþa@õ§a@Z@@@òîö‡j¾a@òäßbØÛa@Šë‰§aInitial Eigenvalues،:א




191

א אא א א אא אא א .אא אא א א :א

1. †ìàÇTotal:، א א א א، א א א א

:4.436 + 2.019 + 1.651 + …… + 0.01958 = 11

2. †ìàÇ% of Variance:، א א:

(א א= א א ÷אא .100×)א

א א א א ،) =4.43559÷11(×100=40.323.



ÉbnÛa@Ý—ÐÛa@

192

òÃìzÜß:א .3אא

א א אא .א

3. @†ìàÇCumulative٪:، א א א א

א of %אא

Variance.ïãbrÛa@õ§a@Zò—Üƒn¾a@pýîàznÛa@pbÈi‹ß@Êìàª@]‹íë‡nÛa@ÝjÓ[Extraction

Sums of Squared Loadings ،א:

، א א א א א אא אא.א א א א Totalא




193

Eigenvaluesא א א.א א א אא א א .א

æa@bäç@ÞaûÛa:aˆb¾‡yaìÛa|îz—Ûa؟א א א א)Extraction(א א אא א א ،)אא(א

א א א א א א א א א.א

@@sÛbrÛa@õ§aZ@@@@@‹íë‡nÛa@‡Èi@pýîàznÛa@pbÈi‹ß@Êìàª@@Rotation Sums of

Squared Loadingsא א ،:



ÉbnÛa@Ý—ÐÛa@

194

א א א א א א א א،א אא Rotationא

א א .Varimaxא

@†ìã@bäçëñŠb’ac@¶g@åí‹ß:

א.1 א אא א.א א א،

א40.323٪ אא א אא.א א א

א30.876٪ א א א.א

א.2 ،Noneא،Rotationאא[ א א א ،]א




195

אא א .א.א

a‡u@âbç:א א א א א א א א

א א א ، א(א א )אא א ، א א א

א א Extraction Methodא

Principle ComponentאVarimax–א.

bÈiaŠ@ZØ¾a@òÏìÐ—ß@Þë‡upbãìComponent Matrix)א אא):א :א



ÉbnÛa@Ý—ÐÛa@

196

א א א אא אאאÝjÓא א

א .א.אא

א א(0.35א א א אאOptionsא )א

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Matrix)א א):אא :א




197

، א א א א א א אא א א אא א

.אÈi‡אא א א א ،א א

א)X9 , X10, X11(אא ، א אא א א א א.א

א אא1א אא :א

أن يقوم بترتيـب Options الترتيب التنازيل يرجع إىل أننا طلبنا من الربنامج ىف املربع احلواري 1

: ، كما هو موضح بالشكل التايلSorted by sizeاملتغريات حسب ارتباطها بكل عامل



ÉbnÛa@Ý—ÐÛa@

198

א:א .)x9 x10 x11(אא:א ).x4x1x3(אא א:א .)x6x7x2(אא א א א:א א ).x8x5(א

Û@óàß@ÉšëßaìÈÜÝZ@ @

א א א א אא א.א א א א

א א א א א.א א אא אא אא א א

א.א א א א א:א،א)9( א)10(אא،)11(א א אא

א א א ، א א .א

א א א אא .אא




199

òíbèäÛa@Àë:

א א אאא א"אא"א א :אא

Field, Andy P., (2005). Discovering statistics using spss (2nd edition), London, Sage. (Chapter 15).

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