دليلك إلي البرنامج الإحصائي spss الجزء 3

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= / *100=5

9.2=58. %

22.2% 80.2% .

Total Variance Explained

2.900 58.009 58.009 2.900 58.009 58.0091.110 22.207 80.216 1.110 22.207 80.216

.523 10.457 90.673

.393 7.864 98.5377.313E-02 1.463 100.000

Component12345

Total % of Variance Cumulative % Total % of Variance Cumulative %Initial Eigenvalues Extraction Sums of Squared Loadings

Extraction Method: Principal Component Analysis. Components Matrix Loadings

)( . HIGHEDUC

0.941 LITERACY DOCTORS GDP

HOSPBED. GDP HOSPBEDS , .

Component Matrix a

.477 .754

.918 1.899E-02

.941 .102

.829 -.131

.508 -.717

GDPLITIRACYHIGHEDUCDOCTORSHOSPBED

1 2Component

Extraction Method: Principal Component Analysis.

2 components extracted.a.

:

1. GDP 797.02754.02477.0 =+ .

2. )( )( :

9.22

508.02

829.02

941.02

918.02

477.0 =++++

.

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)(Component Scores Coefficients Components Martix GDP

:

165.02508.0

2829.0

2941.0

2918.0

2477.0

477.0=

++++

Component Score Coefficient Matrix

.165 .679

.316 .017

.324 .092

.286 -.118

.175 -.645

GDPLITIRACYHIGHEDUCDOCTORS

HOSPBED

1 2Component

Extraction Method: Principal Component Analysis.

Component Scores. )( :

fac_1 =0.165*GDP+0.316*LITIRACY+0.324*HIGHEDUC+0.286*DOCTORS+0.175*HOSPBED

Standardized variables )

GDPLiteracy(... Data Editor :Region gdp literacy higheduc doctors hospbed fac_1 fac_2 Dohok 17.2 30.1 1.09 17 139 -.84873 .00862

Nineveh 24.0 44.2 1.85 14 172 .05265 -.01091Arbil 22.2 35.2 1.18 13 163 -.65458 -.04095Sulayman 16.2 33.5 1.01 10 115 -1.10939 .37657Ta'meem 32.3 49.4 1.85 18 143 .37097 .54501Salah AL-Deen98.4 37.9 1.32 15 80 -.11362 3.32500Diala 23.1 44.1 1.93 10 153 -.14014 .26391Anbar 22.7 44.3 1.58 16 144 -.08303 .22314

Baghdad 75.0 61.6 4.04 36 280 3.22748 .21850Wasit 19.5 36.7 1.11 28 199 .04721 -.80457Babylon 22.8 44.1 1.82 18 145 .09229 .21066Kerbala 21.5 47.7 1.53 24 173 .41839 -.29133

Najaf 18.7 46.2 1.59 27 190 .53708 -.62771Qadisia 21.0 35.2 .95 9 195 -.81013 -.42906Muthana 21.3 33.5 .84 18 178 -.62869 -.37423Thi-Qar 18.1 33.8 .73 12 144 -1.03052 .02027Maysan 20.4 34.4 .90 11 301 -.44139 -1.76886Basrah 19.0 53.6 2.24 25 219 1.11415 -.84406

) (

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)( . )(

(-2,2) .

Scatter Plot

. : Graphs Scatter (Simple) Scatter

Plot.

Y Axis : fac_2.X Axis : fac_1.

Label Cases by : Region. OK.

) Reference Line Chart ChartEditor X Axis Y Axis ( :

REGR factor score 1 for analysis 1

43210-1-2-3-4 R E G R f a c

t o r s c o r e

2 f o r a n a

l y s

i s

1

4

3

2

1

0

-1

-2

-3

-4

Basrah

Maysan

Thi-Qar MuthanaQadisiaNajaf

Kerbala

Babylon

Wasit

Baghdad Anbar Diala

Salah AL-Deen

Ta'meemSulayman

Arbil NinevehDohok

)( (0,0).

(-2,2) ) 3( ) 3(

)(

literacy highedu doctors

)3.227.(

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Case Summaries

Statistics: Mean

61.60 4.04 36.0041.42 1.53 17.83

BaghdadTotal

LITIRACY HIGHEDUC DOCTORS

)3.325( GDP) (

HOSBED ) Components Matrix. ( :

Case Summaries

Mean

98.40 80.00

28.52 174.06

Salah AL-Deen

Total

GDP HOSPBED

:

1. ) (Eigen Vector NORM 2...22

21 n

X X X V +++= Components Matrix )( Norm

Normalized Vector UniqueVector GDP :

280.02

508.02

829.02

941.02

918.02

447.0

477.0=

++++

(2.9) (1.11) .

Eigen Vector 2Eigen Vector 1

0.7157570.280278GDP 0.0180250.538845 LITERACY

0.0964740.552474HIGHEDUC

-0.124740.486656DOCTORS

-0.680070.298378HOSPBED

2. Orthogonal Dot Product == 02 _ *1 _ 2 _ ,1 _ fac fac fac fac

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. Components Score Coefficients .

)113( Factor Analysis Methods

. Factor Analysis

Model X1,X2.Xp ) (Common Factors .

:

pemY pmaY pa p X

emY maY a X

+++=

+++=

...........11

.........................................................

11.............1111

Yj: j) m p. (aij:Loadings j i.ei: Xi)Specific Factor. ( 2) : Method Maximum Likelihood(

1 .

Maximum Liklehood Factor Analysis : Extraction

:Factor Analysis

Communalities

.350 .999

.852 .903

.870 .935

.503 .487

.286 .227

GDP

LITIRACY

HIGHEDUCDOCTORS

HOSPBED

Initial Extract ion

Extraction Method: Maximum Likelihood.

initial Communalities R 2

)Dependent Variable( )IndependentVariables. ( 0 . 999 GDP

0.999

GDP.

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Total Variance Explained

2.900 58.009 58.009 1.413 28.254 28.254

1.110 22.207 80.216 2.138 42.756 71.009.523 10.457 90.673.393 7.864 98.537

7.313E-02 1.463 100.000

Factor 1

2345

Total % of Variance Cumulative % Total % of Variance Cumulative %Initial Eigenvalues Extraction Sums of Squared Loadings


Initial Eigenvalues

Extraction Sums Squared Loadings Initial eigenvalues

:413.1

20271.8

2275.0

2469.0

2333.0

2999.0 =+++ E

%28.2541.413 / 5*100 =

Factor Matrix a

.999 -9.49E-03

.333 .890

.469 .846

.275 .641-8.71E-02 .468

GDPLITIRACYHIGHEDUCDOCTORSHOSPBED

1 2Factor


2 factors extracted. 14 iterations required.a.

1

Goodness-of-fit Test

1.337 1 .247Chi-Square df Sig.

) ( .

2 P-value =0.247

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1% 5% . .

:

Factor Scores

Factor scores Coefficients ) ( 3

) Scores Factor Analysis: (

Regression: .

Bartlett: .Anderson-Rubins: Bartlett

) . (

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N N oo nn PP a a rr a a mm ee tt rr ii cc TT ee ss tt ss

Mean Variance Distribution Free tests.

T

. SPSS .

)121( Square-Chi

Chi-Square Observed frequencies Expected Frequencies .

K Chi-Square :

=

= k

i i E

i E iO

1

2)(2

O i Ei k-1.

1: :

type O i1 4392 1683 1334 60

800

1979 375 .

5: %

1614,

163

3,163

2,169

1:0 ==== P P P P H

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H1 ) P1 ( .

Chi-Square :

Data Editor

:type Observed1 4392 1683 1334 60

Type Value Label

Type . Observed

Data Weight Cases Weight Cases by Weight Cases Observed (Weight Cases by).

Analyze Nonparametric Tests Chi-Square Chi-Square Test :

Type test Variable List.

Expected Values :1.All categories equal: .2.Values: ) (

: 5625.0

16

91 == P Value

0.5625 Add value) . ( OK :

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TYPE

439 np1 450 -11.0168 np2 150 18.0

133 np3 150 -17.060 np4 50 10.0

800

12

34Total

Observed N Expected N Residual

450=nP1=800*0.5625 1 .

Test Statistics

6.3563

.096

Chi-Squarea

df Asymp. Sig.

TYPE

0 cells (.0%) have expected frequencies less than5. The minimum expected cell frequency is 50.0.

a.

Chi-Square 6.356 P-value0.096 5%

. P-Value Transform Compute

:1-CDF.CHISQ(6.356,3) =.096

2: 1000 Head

) Data Editor: (headno observed

0 381 1442 3423 2874 1645 25

Chi-Square Binomial 5. % Chi-Square Goodness of Fit.

)( Observed : Analyze Nonparametric Tests Chi-Square

Chi-Square Test) ( :

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headno Test Variable List. binomial

:Probability No. of heads

0.03125 00.1562310.312520.31253

0.1562340.031255

values Expected values )( .

OK :HEADNO

38 31.2 6.8144 156.2 -12.2342 312.6 29.4287 312.6 -25.6164 156.2 7.8

25 31.2 -6.2

1000 1000.0

012345

Total

Observed NExpected N

=1000*Pr Residual

) ( :

Expected Frequency.= 1000*0.03125=31.25Test Statistics

8.9205

.112

Chi-Square a

df Asymp. Sig.

HEADNO

0 cells (.0%) have expected frequencies less than5. The minimum expected cell frequency is 31.2.

a.

P-value=.112 5% Binomial.

: ) =0( ) =15(

)15( Expected Values Chi-squareTest )06(

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5% Mann-Whitney) 21 =

21 . (

: 2 Independent samplesAnalyze Non Parametric Tests 2 Independent samples Test :

Time) ( Test Variable List group Grouping Variable Define Group Group1 : 1 Group2 :2. Mann-Whitney U.

OK :

Ranks

8 12.63 101.0010 7.00 70.0018

GROUP

healthydseaseTotal

TIME

N Mean Rank Sum of Ranks

Ranks ) ( )

101 70( Mann-Whitney U :

12)1

1(

121 T N N

N N U +

+=

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N1 ) N1=8( N2 ) N2=10( T1 Sum of Ranks)T1=101.(

U Wilcoxon W

U.

Z

U

:

25.1112

)121(21 =++

= N N N N

402

21==

N N

U

222.225.11/)4015(/)( === U Z P-Value Z Transform Compute

2*CDFNORM(-2.222)=0.0263) ( P-value=0.026

5% .

Test Statistics b

15.00070.000-2.222

.026

.027a

Mann-Whitney UWilcoxon WZ

Asymp. Sig. (2-tailed)Exact Sig. [2*(1-tailedSig.)]

TIME

Not corrected for ties.a.

Grouping Variable: GROUPb.

: T2 U U

:22

)12(221 T

N N N N U ++=

N2 U .

)12 3( K Related Samples Tests- K

Mann-Whitney T Kruskall-Wallis One- Way ANOVA.

T T

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Ranks SPSS:

1.Friedman.2

.Kendalls W

. 3.Cochrans Q. 4:

Therapists a b c) 1 2 3( 5 Data Editor SPSS:Therap a b c

1 2 3 12 2 3 13 2 3 14 1 3 25 3 2 16 1 2 37 2 3 18 1 3 29 1 3 2

Friedman 5. %

:

Analyze Nonparametric Tests K-Related samples Tests for several Related Samples :

OK :

5 Daniel W.W.(1978) , Biostatistics : A Foundation for analysis in The HealthSciences , 2

ndEdition, pp.398 .

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NPar TestsFriedman Test

Ranks

1.67

2.78

1.56

A

B

C

Mean Rank

Mean Rank . Two- way ANOVA Randomized

Blocks Experiment ) (Treatments Blocks Friedman

Ranks Ordinal Friedman 2 J-1 :

2.8)1(32)1(

122 =++

= J K iT J KJ

K )( J )( T i K=9 J=3. Friedman P-Value

5% .

Test Statistics a

98.222

2.016

NChi-Squaredf

Asymp. Sig.

Friedman Testa.

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CC HH AA R R TT SS

. Bars Lines Pies

SPSS .)131( Bar Charts

1:

Sales Year: year sales1990 501991 521992 551993 601994 65

. Bar Chart. :

.Graphs Bar Bar Chart :

Values of individual cases Data in Chart are SimpleClusteredStacked Simple .

Define Define Simple Bar :

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. :Bars Represent: .

Category Labels: ) ( :Case number: .

Variable: ) Years. (

Title: Title,subtitle,Footnote.Template: .

OK SPSS Viewer : 1

T T i i t t l l ee L L i i nn ee 11

Line2

S S uu bb t t i i t t l l ee

Footnote

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Establishment Sales

by Years

for Period 1990-1994

Iraqi Dinars (Thousands)

YEAR

19941993199219911990

V a

l u e

S A L E S

70

60

50

40

SPSS Chart Editor :

SPSS Chart Editor

) Chart Editor : (

1. : ) Chart Editor : (

. Chart EditorFormat Color

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Colors :

:Fill: .

Border: . Fill Apply .

Close Colors. :

2

Establishment Sales

by Years



YEAR

19941993199219911990

V a

l u e

S A L E S

70

60

50

40

: )

. (2. Bar Style: :

Chart EditorFormat Bar Style :

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3-D effect Apply All :

3

Establishment Sales

by Years



YEAR

19941993199219911990

V a

l u e

S A L E S

70

60

50

40

:

Depth .

3. Bar Label Style: :

Format Bar Label Style

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:Framed Apply All

:

4Establishment Sales

by Years



YEAR

19941993199219911990

V a

l u e

S A L E S

70

60

50

40

65

60

55

5250

: . Chart Chart Editor

Outer Frame . . Chart Chart Editor

Inner Frame .4. Scale Axis

: )40 50 60( Chart Editor

Scale Axis :

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OK:


by Years



YEAR

19941993199219911990

V a

l u e

S A L E S

70

60

50

40

65

60

55

5250

J.Labels: Labels :-Decimal Places:

:40.050.060.070.0.-Leading Character:

D :40D50DD6070D.-Trailing Character:

% :40%50%60%70. %-

1000 S Separator: 1000

) Comma Period. (

K.Bar Origin Line: Origin Line

. Bar Origin Line 57 Scale Axis 4) (

:

Axis Title Axis Label

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by Years



YEAR

19941993199219911990

V a

l u e

S A L E S

70

60

50

40

140

130

120

110

100

90

80

65

60

55

5250

Labels . Derived Axis.

Match ) ( . 50 70

1 Definition Derived Axis :

Scale Axis Derived AxisRatio Units Equal Units

Match Value Equal Value

Continue OK : 8

Establishment Sales

by Years



YEAR

19941993199219911990

V a l u e S A L E S

70

60

50

40

90

80

70

60

65

60

55

5250

50 70 60 80 .

1 1

7050

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)132( Chart Template:

Template

. 4 ) . ( x)

80 88 110 90 77 9094( : 4 File Save Chart Template Chart

Editor sct cht. Graphs Bar Values of Individual Cases

(simple) x year Values of Individual Cases. template Chart Specification from File

cht Values of Individual Cases :

Ok x: 11

Establishment Sales

by Years



YEAR

19941993199219911990

V a

l u e

X

120

110

100

90

80

7077

90

110

88

80

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)133( Bar Line

1 Bar Line : Chart Editor.

Line Simple

Gallery

Line

Charts)Simple Sales ) ( Multiple. (

Replace Line : 12

Establishment Sales

by Years



YEAR

19941993199219911990

V a

l u e

S A L E S

70

60

50

40

Markers : Format Interpolation

) Chart Editor( Interpolation :

Check Box Display Markers ApplyAll :

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by Years



YEAR

19941993199219911990

V a

l u e

S A L E S

70

60

50

40

: Chart Editor .

Format Markers Markers:

. Apply :


by Years



YEAR

19941993199219911990

V a

l u e

S A L E S

70

60

50

40

: ) ( Apply All.

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)134( Bar Pie

1 Bar Pie : Chart Editor.

Pie

Gallery

Pie Charts

Simple Replace Pie : 15

Establishment Sales

by Years



1994

1993

1992

1991

1990

1994 :

1994 Chart Editor.

Format Explode Slice

: 16

Establishment Sales

by Years



1994

1993

1992

1991

1990

Format Explode slice .

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Inside Slice : Chart Options) Chart Editor(

Pie Options :

Percents Labels Text . Format Label Format :

:

Position: Value Text)inside Outside JustifiedOutsideBest fitnumbers inside Texts Outside. ( Inside

.Connecting Line for Outside Labels: .

Arrowhead on Line: .Display Frame Around: .

Values: 1000 . Continue Ok :

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17

2: 19982002.

year Exports Impots1998 190 1801999 220 2002000 219 2152001 245 2302002 250 244

:1. )(Clustered Bars.2. Stacked Bars.

1. : Graph Bar Bar Chart

Values of Individual Cases / Clustered Define Clustered Bar / Values of Individual Cases :

Establishment Sales

by Years



23.0%

21.3%

19.5%

18.4%

17.7%

1994

1993

1992

1991

1990

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Ok : 18

2. Stacked Bar 2 Stacked Clustered Bar Charts :

YEAR

20022001200019991998

V a

l u e

260

240

220

200

180

160

EXPORTS

IMPORTS

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19

Imports ) ( Exports

) ( 1998 Chart Editor Series Displayed

:

YEAR

20022001200019991998

V a l u e

600

500

400

300

200

100

0

IMPORTS

EXPORTS

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Exports Display) ( ) ( Omit) ( Exports Omit Display) (

Display. 1998 Display) (

Omit. :

OK : 20

Exports Line Imports Bar Bar/Line/Area Displayed Data :

Bar Imports Line Exports OK :

YEAR

2002200120001999 V a

l u e

600

500

400

300

200

100

0

EXPORTS

IMPORTS

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21

:

Line Pie Bar Bar :

Graph Line LineGraph Pie PieGraph Area Area

3) Summaries of Separate Variables( x1 x2 g Data Editor

SPSS : x1 x2 g

100 10 a 200 20 b300 30 a 400 40 b500 50 b

:1. Bar x1 x2.2. Clustered Bar )a b( x1 x2.1. Bar x1 x2 :

Graph Bar Bar Charts

Summaries of Separate Variables / Simple. Define

YEAR

2002200120001999 V a

l u e

260

250

240

230

220

210

200

190

EXPORTS

IMPORTS

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Define Simple Bar : Summaries of Separate Variables :

x1 x2

Change Summary Median,Mode,No.of Cases ,Min. Value,Max. Value . OK :

22

x1 300 x2 30.

2. Clustered Bar )a b( x1 x2 :

Graph Bar Bar Charts

Summaries of Separate Variables / Clustered. Define Define Clustered Bar : Summaries of Separate Variables :

X2X1 M e a n

400

300

200

100

030

300

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Ok :

23

4:) Summaries of Groups of Cases( salary deg gender.

deg gender salaryFirst Male 90First Female 70Third Male 56Second Male 65First Male 85Second Female 60Second Male 69Third Male 57Third Female 50First Female 75Second Female 62Third Female 51First Male 85

G

ba M e a n

400

300

200

100

0

X1

X237

20

367

200

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:1. Bars .2. Bars

. 1. : Graphs Bar Bar Charts

Summaries for Group of Cases/Simple. Define Define Simple Bar :

Summaries for Group of Cases :

OK : 24

GENDER

MaleFemale M e a n

S A L A R Y

74

72

70

68

66

64

62

60

72

61

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2. : Graphs Bar Bar Charts

Summaries for Group of Cases/Clustered

Clusters

. Define Define Clustered Bar: Summaries for Group of Cases :

OK :

25

DEG

ThirdSecondFirst M e a n

S A L A R Y

90

80

70

60

50

40

GENDER

Female

Male

57

67

87

51

61

73

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SPSS EXCEL

.

)13 5( Histogram

Bar Line pie Row Data Grouped Data

Ungrouped SPSS Frequency Table

) ( .

5

tall Data Editor) )41( . (

:

Graph Histogram Histogram :

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OK : 26

Midpoints Classes Interval Width 10. :

Histogram Chart Editor. :

TALL

100.0

95.0

90.0

85.0

80.0

75.0

70.0

65.0

60.0

55.0

50.0

45.0

40.0

35.0

30.0

10

8

6

4

2

0

Std. Dev = 17.17

Mean = 68.2N = 56.00

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Custom Intervals Define Define Custom Intervals :

10 Interval width 30

100 7 :7

1070

Width(Interval)Class lasses. ===

RangeC of No

continue Interval axis Labels Labels :

:1.Range Midpoint Type

.2. Decimal Places.3. OrientationDiagonal .

Continue Interval axis OK

: 27

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Chart Options Chart Editor Normalcurve Display Display Normal Curve Histogram

. )136( Box Plot

Median) 61 . (

6: Data Editor SPSS :

x y cat z g gender 30 47 a 20 a f 20 66 a 60 b f 31 77 a 50 a m35 90 a 80 b m

100 55 a 100 b m80 62 a 70 a m

79 80 b 89 b m55 99 b 35 a f 50 43 b 65 b f 60 87 b 40 a f 95 92 b 55 b f 45 70 b 69 b m39 40 b 40 a m

1. Box Plots x.2. Box plot x y.3. Box Plot x y a b cat.4. Box Plot z)a b( g.

TALL

9 0 - 1 0 0

8 0 - 9 0

7 0 - 8 0

6 0 - 7 0

5 0 - 6 0

4 0 - 5 0

3 0 - 4 0

16

14

12

10

8

6

4

2

0

Std. Dev = 17.17

Mean = 68

N = 56.00

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5. Box Plot z)a b( g m f .

1. :. .

Graphs Box plot Boxplot:

summaries of Separate Variables Simple) Clustered x. ( Define Define Simple boxplot:

Summaries of Separate Variables :

Boxes Represent Label Cases by Label ) ( .

OK :

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28

2. : Graphs Box plot Boxplot

:

Define Define Simple Box plot : Summaries of Separate Variables :

OK :

13N =

X

120

100

80

60

40

20

0

Max. = 100

Q3 =79

Median =50

Q1=35

Min. = 2

I Q R =

Q 3 - Q

1 =

4 4

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29

3. : Graphs

Boxplot Box Plot Summaries of Separate Variables Clustered Defined

Define Clustered Box Plot : Summaries of Separate Variables : Box Represent Category Axis

. OK : 30

1313N =

YX

120

100

80

60

40

20

0

76 76N =

CAT

ba

120

100

80

60

40

20

0

X

Y

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4. : Graphs Boxplot Box Plot

Summaries for Group of Cases Simple Defined

Define Simple Box Plot : Summaries for Group of

Cases :

OK : 31

5. : Graphs Boxplot Box Plot

Summaries for Group of Cases Clustered Defined Define Clustered Box Plot : Summaries for Group of

:

76N =

G

ba

Z

120

100

80

60

40

20

0

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OK : 32

)137( Scatterplot

.

7:

Data Editor SPSS :

43 33N =

G

ba

Z

120

100

80

60

40

20

0

GENDER

f

m

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y x1 x2 x3 marker label

79 26 6 60 a c174 29 15 52 a c2

104 56 8 20 a c387 31 8 47 a c495 52 6 33 a c5

109 55 9 22 a c6102 71 17 6 b c772 31 22 44 b c893 54 18 22 b c9

115 47 4 26 b c1083 40 23 34 b c11

113 66 9 12 b c12109 68 8 12 b c13

:1. Simple

y x1 : Graphs Scatter Scatterplot:

Simple Define Simplescatterplot :

:Y Axis: .X Axis: .

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Set Marker by: Marker .

Label Cases by: Labels

. Set Markers by label Cases byOptional .

OK : 33

Label :

Chart Editor. Chart Editor Chart Options

Options :

X1

80706050403020

Y

120

110

100

90

80

70

MARKER

b

a

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b( ) b a ( .

35

3: 11B 0BY X += Y

X1

95

% Y

)0X

1B

0B)

0E(Y +=

( : ) Chart Editor(Chart Options

OptionsScatterplot :

Total Fit Line

a b Marker.

X1

80706050403020 Y

120

110

100

90

80

70

MARKER

Total Population

c13

c12

c11

c10

c9

c8

c7

c6

c5

c4

c3

c2

c1

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Fit Options Fit Line :

Linear Regression ) (

95% Mean Regression Prediction Line) Individual 0010B0Y e X B ++=. (

continue OK ) Case Labels: (

36

: Subgroups Fit Line Scatterplot : Options ) Marker(

Show Subgroups Display Options .

X1

80706050403020 Y

120

110

100

90

80

70

MARKER

Total Population

Rsq = 0.6648

95%

95%

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2. Overlay Y

X1 X2 X3 :

Graph Scatter

Scatterplot

Overlay Define Overlay Scatterplot :

Y-X Pairs ) Y X1: ( Y . X1. Y-X Pairs.

Y X1 X2 X3.

Swap Pair: . Options Display Chart with Case Labels .

OK Overlay Scatterplot : 37

80706050403020100

120

100

80

60

40

20

0

X2

X3

Y

X1

c13c12

c11

c10

c9c8

c7

c6c5 c4c3

c2

c1

c13c12

c11

c10

c9

c8

c7

c6

c5

c4

c3

c2

c1

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3. Matrix . Matrix

X1 X2 X3 :

Graph Scatter

Scatterplot

Matrix Scatterplot Matrix :

marker Label .

OK : 38

Axis Titles Chart Editor Chart Axis Scatterplot Matrix

Scale Axes Display Axis Titles :

X1

X2

X3

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Edit Edit SelectedAxis Center Justification Continue OK

:

X1 X 1

X 2 X2

X1

X 3

X2

X3

X3

4. D-3

. 3-D Y X1 X2 :

Graph Scatter Scatterplot 3-

D 3-D Scatterplot :

39

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OK : 40

1: Chart Editor

Format 3D-Rotation . 2:

Chart Options Chart Editor 3-D Scatterplot Options Floor

Spikes OK : 41

Y

3080

70

80

70

90

100

110

2060

120

X2 X150

104030

Spikes 3-D Scatterplot Options:

Centroid: Centroid.Origin: Origin.

Y

3080

70

80

70

90

100

110

2060

120

X2X150

104030

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DDa a tta a EExxcchha a nnggee

SPSS : Lotus 1-2-3 Excel.

dBase. Tab-delimited, ASCII. SPSS . SYSTAT.

) ( ) ( .)141( Importing Data files 1) : 97EXCEL(

Test merge Excel97 :

Test Excel SPSS.

: SPSSData File Open Open :

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Test File Name Files of Type Excel xls

SPSS(*.SAV)) SPSS Windows SAV( SPSS

DOS

SPSS/PC+(*.SYS)

Lotus(*.W*)

dBase(*.dbf)

Text(*.txt) . open :

:

Read Variable Names From The First Row of data : Excel.

Worksheet: SPSS.Range: SPSS.

OK ExcelSPSS Data Editor :

SPSS SAV. :

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1. Range Range Opening ExcelData Source) A1:A3( :

2. Sheet2 Sheet1 Worksheet

Opening Excel Data Source Range .

3. Read Variable Names From The First Row of data

Excel SPSS .

4

. Excel Unique SPSS Excel Characters 8 8

Label SPSS.

5. Excel Excel Edit Copy SPSS Edit Paste

Data View SPSS

SPSS Numeric String Data Editor .

2) : Text Files(

EditorMS-DOS

MS-DOS Var.txt Spaces :

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x1 x2 x312 30 33

16 45 6029 64 88

SPSS : File Read Text Data Open File

:

Data File Open Text Files of type .

Open Text Import wizard(step 1of 6) :

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Next 2 :

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How are your Variables arranged? Delimited ) .. (

Are Variables Names included at the top of your file? yes.

Next

3

:

the first Case of data Begins on Which Line Number ? . How are Your Cases represented? Each Line Represents a Case ) (

. How Many Cases Do you want to Import ?

.

Next Wizard :

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Delimiter Edit Space)

. ( Next wizard

Name & Format Data Preview Variable Name Data Format.

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1.SPSS(*.SAV) .2.SPSS 7.0(*.SAV) .3.SPSS/PC+(*.SYS) SPSS DOS.4.Tab-delimited(*.dat) Text.5.Fixed ASCII(*.dat) 6.Excel(*.xls)7.Lotus1-2-3(*.w*) 1 2 3.8.dBase(*,dbf) II III IV.

3) : ( household.sav Data Editor SPSS:

:1. Excel.2. MS-Editor.

1. Excel : File Save As SAVE AS

: File Name Save as Type

Excel xls.

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write variable names to spreadsheet Excel .

Save Excel merg1

household

sav

xls

. household Excel File Open Path Excel :

2. MS-Editor ) Excel( Tab-delimited Save as Type Save Data As

household. Save Tab-delimited merg1 household dat household MS-Editor .

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3. Durbin-Watson. :

1. Analyze Regression Linear Linear Regression

:

2. Statistics Statistics:Estimate .Model Fit ANOVA R 2.Durbin - Watson DW.

3. Paste Linear Regression Syntax File

) ( :

Syntax EditorFile Save As )SPSS Syntax File( SPS.

)1522( Log Log

log :

) (

R 2 ANOVA DW

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Data EditorEdit Options Options. Viewer Options Viewer.

Display Commands in the Log

. Viewer :

OK. Viewer.

2:

Log.

1 2 OK LinearRegression viewer :

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File New Syntax SPSS Viewer.

Log SPSS Viewer . ) . (

ViewerEdit Copy.

SyntaxEdit Paste

. Syntax .)1523( Journal File

Journal File spss.jnl ) C:\WINDOWS\TEMP(

Syntax SPS .

: Journal File Edit Options General Data Editor .

Journal File Record Syntax in Journal) ( :

Append: .Overwrite: SPSS.

Log

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3: Journal File. 1 2 OK Linear

Regression SPSS Viewer. SPSS ViewerFile Open Other

spss.jnl C:\WINDOWS\TEMP :

spss.jnl ) ( . Syntax

SPS . : journal file Overwrite) . (

)153( To Run command syntax ) ( :

Syntax File File Open Syntax .

SPSS Syntax EditorRun :

All: .Selection: )(Highlighted.

Current: Cursor.To End: .

4: ) X Y( :

1. CoefficientsY/X ANOVA Durbin-Watson.

Regression

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2. Scatterplot X Y.3. X Y.

) X Y Data Editor

SPSS( : Syntax File File New Syntax.

1 2 Paste LinearRegression .

Scatterplot X Y Data EditorGraphs Scatter Simple Define Simple

Scatterplot. Simple Scatterplot Y Y Axis :

X X Axis :. Paste Scatterplot .

X Y Analyze Descriptive Statistics Frequencies

Frequencies. Frequencies X Y Variable(s)

Display Frequency Tables. Statistics Statistics.

Statistics Sum. Paste Frequencies Frequencies .

Syntax Editor Syntax1 File Save As

) three jobs( SPS :

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Run All :Regression

Model Summary b

.900 a .810 .787 8.82 1.885Model1

R R Square

Adjusted

R Square

Std. Error of

the Estimate

Durbin-W

atson

Predictors: (Constant), Xa.

Dependent Variable: Yb.

ANOVA b

2661.050 1 2661.050 34.174 .000 a

622.950 8 77.8693284.000 9

RegressionResidualTotal

Model1

Sum of Squares df Mean Square F Sig.

Predictors: (Constant), Xa.

Dependent Variable: Yb.

Coefficients a

85.044 9.970 8.530 .000

1.140 .195 .900 5.846 .000

(Constant)

X

Model1

B Std. Error

UnstandardizedCoefficients

Beta

Standardized

Coefficients

t Sig.

Dependent Variable: Ya.

Graph

X

706050403020

Y

180

170

160

150

140

130

120

110

Frequencies

Statistics

10 100 0

491 1410

ValidMissing

N

Sum

X Y

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Regression Run Selection

.

Graph

Run Current

Scatterplot . Run To End Frequency Sum.

: X1 Y1 X Y

: X1 Y1 Data Editor.

three jobs. X X1 Y Y1.

Run.

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M M uu l l t t i i p p l l ee R R ee s s p p oonn s s ee A A nn aa l l y y s s i i s s )

( .

Frequencies :1. Multiple response Frequencies:

.2

. Multiple Response Crosstabs: .)161( Multiple response Frequencies

1

) ( )

( :1. Multiple dichotomy method: ) ( ) (

. Data Editor.

Babylon jamhorya aliraq qadissya thowra 1 0 1 1 01 1 0 0 00 0 1 0 01 0 0 1 10 1 1 0 0

1 0 0 1 11 0 1 0 0

: Data EditorAnalyze Multiple Response Define

sets

Define Multiple Response Sets :

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:

Set definition Variables in set . ) ( Dichotomies Counted value. Name

paper) Label . ( Add Mult response sets

Close paper

. Analyze Multiple Response Frequencies Multiple Response Frequencies :

$paper Tables for. OK :

Multiple ResponseGroup $PEPER

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(Value tabulated = 1)

Pct of Pct ofDichotomy labelName Count Responses Cases

BABYLON 5 31.3 71.4JAMHORYA 2 12.5 28.6ALIRAQ 4 25.0 57.1QADISSYA 3 18.8 42.9THOWRA 2 12.5 28.6

------- ----- -----Total responses 16 100.0 228.6

0 missing cases; 7 valid cases :1. Data Editor Babylon Jamhorya ...

Elementary Variables $paper Multiple response Set .

2. Labels

.2. Multiple Category Method:

) ( X1 X2 X3 :

1 = Babylon , 2 = Jamhorya , 3 = Aliraq , 4 = Qadissya , 5 = Thowra

X1 1 X2 3 X3 4

1 X1 2 X2X3 Missing Value. X1 X2 X3 Data Editor

:X1 X2 X31 3 41 2 .3 . .1 4 52 3 .1 4 51 3 .

) (

: Data EditorAnalyze Multiple Response Define sets

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Define Multiple Response Sets : X1 X2 X3 Set definition

Variables in set .

Categories Variables Are Coded As 1 5)Range 1 through 5. ( Name

paper) Label . ( Add Mult response sets

Close paper .

Analyze Multiple Response Frequencies

Multiple Response Frequencies $paper Tables for.

OK : Group $PAPER

Pct of Pct ofCategory label

Code Count Responses Cases1 5 31.3 71.42 2 12.5 28.63 4 25.0 57.14 3 18.8 42.95 2 12.5 28.6

------- ----- -----Total responses 16 100.0 228.6

0 missing cases; 7 valid cases

. : X1 X2 X3 Value Label .

)162 ( Multiple Response Crosstabs Elementary Variables

Multiple response Sets . 2:

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:1.

responses respondents) Cases( Options Multiple Response Crosstabs

Percentages Based on:Cases: Cases

Respondents) 7. (Responses:

Responses) 16

. (2. Options

Multiple Response Crosstabs Cell Percentages:Row: .Column: .Total: .

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$PAPER (tabulating 1)by COLOREDby GENDER

Category = 1 Male Percents and totals based on respondents

)( 4)( 3) . (

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