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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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− üaÞýÔnÛaïma‰ïÓaìjÜÛ:

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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

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א א א :א

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160 130

175 129

157 125

130 115

150 119

152 121

162 124

125 97

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Predicted Values

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113

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ÉibÛa@Ý—ÐÛa

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( R:א)א א א א ).א

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115

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ÉibÛa@Ý—ÐÛa

116

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@ @

א א אא א א אא א א א א .א

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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@áØ¨aŠa‡®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

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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

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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

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◘ 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

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א א:)H1(א .אאא



ÉibÛa@Ý—ÐÛa

126

אא אא א :א

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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

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ÉibÛa@Ý—ÐÛa

128

:ع الحواري الذي أمامكفي المرب )2

المربع الذي إلى Standardized Residualقم بنقل المتغير )أ .Dependent Listبعنوان

.Plots اختر Displayومن الاختيارات )ب الذي الحواري نفس المربع في ..Plots الاختيار فوق أنقرثم )ج

:يلي جديد، كما حواريأمامك، سيظهر لنا مربع

:قم بما يلي: يفي هذا المربع الحوار )3




129

من خلال Normality Plots with testsنشط الاختيار )أ الضغط مرة واحدة بالماوس في المربع الأبيض الذي أمام هذا

.الاختيار ثم قم بتعطيل .Noneر اخت Boxplotsثم من الاختيارات )ب

هو، الهدف من هذه الخطوة Stem – and – Leafالاختيار .إليها حاجة المخرجات التي لسنا فيتقليل

. للعودة للمربع الحواري السابقContinueثم اضغط )ج

نافـذة المخرجـات سنحصل على النتـائج التاليـة في ، OKثم 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

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א ).אא אא:)H1(א אא(א

א ).א

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Durbin – Watson Test

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131

א אDurbin-Watsonאא א א אא،א

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òîãbrÛa@ñì©a:א א(א Durbinא

– Watson:(

א א ،Durbin – Watson

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ÉibÛa@Ý—ÐÛa

132

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ÉibÛa@Ý—ÐÛa

134

אא ،אGoldfield – Quandtא.

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135

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ÉibÛa@Ý—ÐÛa

136

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137

א א א :א

א Based on time or case rangeא

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א א א א:אא א א Continueא.א

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ÉibÛa@Ý—ÐÛa

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אא (x)(y)א

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139

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ÉibÛa@Ý—ÐÛa

140

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142

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145

א ، א א א אא א א א @OLS،@â‡Ç@@†ìuëא

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146

òîãbrÛa@ òÔí‹ÛaZא Varianceא

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

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148

א א א :א

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

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149

א א א :א

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א א .]Residualsאא. Collinearity diagnostics ]

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א . א EstimatesModel Fitא

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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

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Standardized Residual Plots.


א )5 א א ،okא:אא .א

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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.(

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@bîãbq@ZÛaÂë‹“òí‹ÄäÛa@N@ @

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א א א א א אא א א ، א א

א .אא

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

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153

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א ◘ :אא א):0H(א .אא א):1H(א .א

:א ◘א א):0H(א א

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).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:

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01

00

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− )B1:(

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11

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− )B2:(

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20

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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

: في المربع الحواري الذي أمامك )2

إلى المربع Standardized Residualالمتغير قم بنقل -أ .Dependent Listالذي بعنوان

.Plots اختر Displayومن الاختيارات -ب في نفس المربع الحواري الذي ..Plotsثم أنقر فوق الاختيار -ج

:أمامك، سيظهر لنا مربع حواري جديد، كما يلي




159

:قم بما يلي: في هذا المربع الحواري )3

من Normality Plots with testsنشط الاختيار -أ خلال الضغط مرة واحدة بالماوس في المربع الذي أمام هذا

.الاختيارثم قم بتعطيل . None اختر Boxplotsثم من الاختيارات -ب

، الهدف من هذه الخطوة Stem – and – Leafالاختيار .المخرجات التي لسنا في حاجة إليهاتقليل وه

. للعودة للمربع الحواري السابقContinueثم اضغط -ج

، سنحصل على النتـائج التاليـة في نافـذة المخرجـات OKثم 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

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א א א א א א .א

paì©aZ@ @

אאScatterאGraphsא )1 א:א




163

א א א ،Simpleא:אDefineא א א :א

א א א :אא − א א Standardא

residual [zre_1]א א .Y Axisא




164

א − א )א(א Unstandardized Predicted Valueא

[pre_1]א א .X Axisא

אokא )2 א א :א

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א א א אא א אא( א א א א א א ،)א

، א אא.א א[א ]א




165

א א א א א א.אGoldfield – Quandtא.

òîãbrÛa@òÔí‹Ûa@Z@òîib¨a@òÔí‹ÛaIòÔí‹ Goldfield – Quandt(

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א . א א א:א א(x3)א אא.Sort byא א א

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166

אokא . א א א(،א)x3(א) ،:

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א א.)1(א א אא א)9(،)10(،)11(،)12.(، א

א:א8 א א ،)1(א

1 )20×0.20=4(.




167

א)8(א)...........2( א א א ،).20(א)........14(،)13(

)SSE(א .3א א א ، .א

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. ، א א א א א א א א:

o אDataאSelect Casesא א א :א

o א א א :אא Based on time or case rangeא

Rangeא א א :א




168

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

א ، א א :א




169

א . א (x's)(y)א

،.א א אא :א

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170

א ).0.334(א)SSE()2(א




171

. )F(، :א

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א א Coefficientsא

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172

ÕîÜÈnÛaë@wöbnäÛa@Íí‹Ðm:

VIF Toleranceא

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X31.057 0.946

א א :אא א)5(א א ،

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bîãbqZÂbjmŠüa@òÏìÐ—ß Correlation Matrix

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Factor Analysis



176



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òÜy‹¾aòîãbrÛa:ï©a@xaë†Œüa@òÜØ“ß@†ìuë@â‡Ç@åß@‡×dnÛa:Multi-Collinearity

א א א אאא א א אא אא א ، .א

.لمطلقةة ايمقا ال النظر عن الإشارة نعني بصرف 1

ïÜßbÈÛa@ÝîÜznÛa@ @

Factor Analysis



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178

א א א א ،א.א א 0.00001א

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א א א א(0.80א (א א).Andy Field.(

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א א א :א



ÉbnÛa@Ý—ÐÛa@

180

א .2 א א :א

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181

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ÉbnÛa@Ý—ÐÛa@

182

א א א ::אא . א:Methodא

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183

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:Orthogonal Rotationאא :א



ÉbnÛa@Ý—ÐÛa@

184

o Varimax.

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אא אאא א א א א(א .)א

א א אא א(א )אא א א.א א

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א .6 אא ،Optionsא:אא א א :א




185

א א א :א

Sorted by sizeא .

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ÉbnÛa@Ý—ÐÛa@

186

õ§aÞëþa)ðìÜÈÛa:(אCorrelation،õ§aë

ïãbrÛa)ïÜÐÛa(א (א.Sig (1 – tailed))א




187

wöbnäÛa@óÜÇ@ÕîÜÈnÛa:א אא

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א א א .א

bîãbq : åß@Ý×@Šbjn‚a@wöbnã@Þë‡uKMO and Bartlett's Test

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.لمطلقةة ايمقا البصرف النظر عن الإشارة نعني 1



ÉbnÛa@Ý—ÐÛa@

188

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

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Šbjn‚üaïãbrÛa:Šbjn‚aoîÜmŠbiBartlett's Test

אא אא Correlationא

Matrix1א identity Matrix.א :אא .אא:א

مصفوفة الوحدة هي المصفوفة التي تتكون جميع عناصرها من الصفر، فيما عدا العناصر الموجـودة 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:

א)2(

אdf

אP.Value

108.896550.000

ÕîÜÈnÛa:א ، א א א P.Valueא

א5٪אא א א ،א א ، א .א

òÜØ“ßxaë†Œüaï©aMulti-Collinearity:

א א א אא.אא א

)א(0.0000310.00001

א א א א .א.א

òÃìzÜßòßbç:



ÉbnÛa@Ý—ÐÛa@

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 التنازلي يرجع إلى أننا طلبنا من البرنامج فى المربع الحواري 1

: ، كما هو موضح بالشكل التاليSorted by sizeالمتغيرات 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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