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PAKET SPSS LANJUT

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PAKET SPSS LANJUT

ANOVA Menguji apakah rerata 1 variabel berbeda

secara bermakna pada lebih dari 2 kategori (beda rerata kadar kolesterol antar 3 kategori usia) Buka SPSS: file – data –dietstudy Analyze – Compare means – One-way ANOVA:

Dependent List : data rasio (wg0) Factor : lebih 2 kategori (agegroup) Option:

Statistics: descriptive dan homogeneity-of-variance Post-hoc: Bonferroni dan Tukey

Continue OK

Tests of Normality

.156 16 .200* .938 16 .320CholesterolStatistic df Sig. Statistic df Sig.

Kolmogorov-Smirnova Shapiro-Wilk

This is a lower bound of the true significance.*.

Lilliefors Significance Correctiona.

Descriptives

Cholesterol

5 187.40 29.433 13.163 150.85 223.95 158 2336 215.50 37.212 15.192 176.45 254.55 151 2575 188.80 29.987 13.410 151.57 226.03 157 222

16 198.38 33.472 8.368 180.54 216.21 151 257

<5050-60>60Total

N Mean Std. Deviation Std. Error Lower Bound Upper Bound

95% Confidence Interval forMean

Minimum Maximum

Multiple Comparisons

Dependent Variable: Cholesterol

-28.10 19.861 .362 -80.54 24.34-1.40 20.744 .997 -56.17 53.3728.10 19.861 .362 -24.34 80.5426.70 19.861 .397 -25.74 79.141.40 20.744 .997 -53.37 56.17

-26.70 19.861 .397 -79.14 25.74-28.10 19.861 .542 -82.64 26.44-1.40 20.744 1.000 -58.36 55.5628.10 19.861 .542 -26.44 82.6426.70 19.861 .605 -27.84 81.241.40 20.744 1.000 -55.56 58.36

-26.70 19.861 .605 -81.24 27.84

(J) age grouping50-60>60<50>60<5050-6050-60>60<50>60<5050-60

(I) age grouping<50

50-60

>60

<50

50-60

>60

Tukey HSD

Bonferroni

MeanDifference

(I-J) Std. Error Sig. Lower Bound Upper Bound95% Confidence Interval

ANOVA

Cholesterol

2820.250 2 1410.125 1.311 .30313985.500 13 1075.80816805.750 15

Between GroupsWithin GroupsTotal

Sum ofSquares df Mean Square F Sig.

GLM - univariat

Menguji hubungan usia dengan kadar kolesterol:Buka SPSS: file – data –dietstudyAnalyze – General Linear Model – Univariate:

Dependent variable: masukkan variabel wgt0 (data rasio)

Covariate: masukkan variabel age (data rasio) sebagai variabel independen

OK

GLM - univariat

Tests of Between-Subjects Effects

Dependent Variable: Cholesterol

306.756a 1 306.756 .260 .61812956.153 1 12956.153 10.994 .005

306.756 1 306.756 .260 .61816498.994 14 1178.500

646448.000 1616805.750 15

SourceCorrected ModelInterceptAGEErrorTotalCorrected Total

Type III Sumof Squares df Mean Square F Sig.

R Squared = .018 (Adjusted R Squared = -.052)a.

GLM – univariat (options)

Kadar kolesterol darah = 234,135 – 0,654 usia

Parameter Estimates

Dependent Variable: Cholesterol

234.135 70.614 3.316 .005 82.682 385.587-.654 1.282 -.510 .618 -3.403 2.095

ParameterInterceptAGE

B Std. Error t Sig. Lower Bound Upper Bound95% Confidence Interval

Correlations

1 -.135. .618

16 16-.135 1.618 .

16 16

Pearson CorrelationSig. (2-tailed)NPearson CorrelationSig. (2-tailed)N

Age in years

Cholesterol

Age in years Cholesterol

Linear Regression

45 50 55 60

Age in years

150

175

200

225

250

Cho

lest

erol

Cholesterol = 234.13 + -0.65 * ageR-Square = 0.02

GLM - univariat

Menguji hubungan jenis kelamin dan kategori usia dengan kadar kolesterol darah: Buka SPSS: file – data –dietstudy Analyze – General Linear Model – Univariate:

Dependent variable: masukkan variabel wgt0 (data rasio) Fixed factors: masukkan variabel jenis kelamin dan kategori

usia (data nominal/ordinal) OK

GLM - univariat

Tests of Between-Subjects Effects

Dependent Variable: Cholesterol

13794.217a 5 2758.843 9.161 .002462728.955 1 462728.955 1536.523 .00010936.955 1 10936.955 36.317 .000

37.922 2 18.961 .063 .939491.654 2 245.827 .816 .469

3011.533 10 301.153646448.000 1616805.750 15

SourceCorrected ModelInterceptGENDERAGEGROUPGENDER * AGEGROUPErrorTotalCorrected Total

Type III Sumof Squares df Mean Square F Sig.

R Squared = .821 (Adjusted R Squared = .731)a.

GLM – univariat (plots)Estimated Marginal Means of Cholesterol

age grouping

>6050-60<50

Estim

ated

Mar

gina

l Mea

ns240

220

200

180

160

140

Gender

Male

Female

MANOVA (GLM-multivariate)

Variabel dependen lebih dari 1 tapi kelompok1, semisal:

Bagaimana rata-rata kadar trigliserida dan kolesterol (2 variabel dependen) berbeda secara bermakna untuk tiap kelompok usia (1 kelompok)

MANOVA (GLM-multivariate)

Variabel dependen lebih dari 1 tapi kelompok1

Buka SPSS: file – data –dietstudy Analyze – General Linear Model – Multivariate:

Dependent variables: masukkan variabel kolesterol dan trigliserida darah

Fixed factor: masukkan variabel kategori usia Options - Display: aktifkan Homogenity test Continue dan OK

MANOVA (GLM-multivariate)

Box's Test of Equality of Covariance Matricesa

.864

.1116

3329.708.995

Box's MFdf1df2Sig.

Tests the null hypothesis that the observed covariancematrices of the dependent variables are equal across groups.

Design: Intercept+AGEGROUPa.

MANOVA (GLM-multivariate)

Levene's Test of Equality of Error Variancesa

.095 2 13 .910

.617 2 13 .555CholesterolTriglyceride

F df1 df2 Sig.

Tests the null hypothesis that the error variance of the dependentvariable is equal across groups.

Design: Intercept+AGEGROUPa.

MANOVA (GLM-multivariate)

Multivariate Testsc

.982 322.080a 2.000 12.000 .000

.018 322.080a 2.000 12.000 .00053.680 322.080a 2.000 12.000 .00053.680 322.080a 2.000 12.000 .000

.231 .849 4.000 26.000 .507

.770 .838a 4.000 24.000 .515

.298 .818 4.000 22.000 .527

.293 1.905b 2.000 13.000 .188

Pillai's TraceWilks' LambdaHotelling's TraceRoy's Largest RootPillai's TraceWilks' LambdaHotelling's TraceRoy's Largest Root

EffectIntercept

AGEGROUP

Value F Hypothesis df Error df Sig.

Exact statistica.

The statistic is an upper bound on F that yields a lower bound on the significance level.b.

Design: Intercept+AGEGROUPc.

MANOVA (GLM-multivariate)Tests of Between-Subjects Effects

2820.250a 2 1410.125 1.311 .303334.204b 2 167.102 .176 .840

617839.218 1 617839.218 574.303 .000305882.549 1 305882.549 322.877 .000

2820.250 2 1410.125 1.311 .303334.204 2 167.102 .176 .840

13985.500 13 1075.80812315.733 13 947.364

646448.000 16319289.000 1616805.750 1512649.937 15

Dependent VariableCholesterolTriglycerideCholesterolTriglycerideCholesterolTriglycerideCholesterolTriglycerideCholesterolTriglycerideCholesterolTriglyceride

SourceCorrected Model

Intercept

AGEGROUP

Error

Total

Corrected Total

Type III Sumof Squares df Mean Square F Sig.

R Squared = .168 (Adjusted R Squared = .040)a.

R Squared = .026 (Adjusted R Squared = -.123)b.

MANOVA (GLM-multivariate)

Hubungan gender dengan kadar trigliserida dan kolesterol darah:

Buka SPSS: file – data –dietstudy Analyze – General Linear Model – Multivariate:

Dependent variables: masukkan variabel kolesterol dan trigliserida darah

Fixed factor: masukkan variabel gender Options - Display: aktifkan Homogenity test Continue dan OK

MANOVA (GLM-multivariate)

Box's Test of Equality of Covariance Matricesa

.864

.1116

3329.708.995

Box's MFdf1df2Sig.

Tests the null hypothesis that the observed covariancematrices of the dependent variables are equal across groups.

Design: Intercept+AGEGROUPa.

MANOVA (GLM-multivariate)

Levene's Test of Equality of Error Variancesa

1.521 1 14 .238.630 1 14 .440

CholesterolTriglyceride

F df1 df2 Sig.

Tests the null hypothesis that the error variance of the dependentvariable is equal across groups.

Design: Intercept+GENDERa.

MANOVA (GLM-multivariate)

Multivariate Testsb

.996 1564.542a 2.000 13.000 .000

.004 1564.542a 2.000 13.000 .000240.699 1564.542a 2.000 13.000 .000240.699 1564.542a 2.000 13.000 .000

.818 29.256a 2.000 13.000 .000

.182 29.256a 2.000 13.000 .0004.501 29.256a 2.000 13.000 .0004.501 29.256a 2.000 13.000 .000

Pillai's TraceWilks' LambdaHotelling's TraceRoy's Largest RootPillai's TraceWilks' LambdaHotelling's TraceRoy's Largest Root

EffectIntercept

GENDER

Value F Hypothesis df Error df Sig.

Exact statistica.

Design: Intercept+GENDERb.

MANOVA (GLM-multivariate)Tests of Between-Subjects Effects

13274.766a 1 13274.766 52.633 .0001627.937b 1 1627.937 2.068 .172

597334.766 1 597334.766 2368.373 .000296331.437 1 296331.437 376.396 .00013274.766 1 13274.766 52.633 .0001627.937 1 1627.937 2.068 .1723530.984 14 252.213

11022.000 14 787.286646448.000 16319289.000 1616805.750 1512649.937 15

Dependent VariableCholesterolTriglycerideCholesterolTriglycerideCholesterolTriglycerideCholesterolTriglycerideCholesterolTriglycerideCholesterolTriglyceride

SourceCorrected Model

Intercept

GENDER

Error

Total

Corrected Total

Type III Sumof Squares df Mean Square F Sig.

R Squared = .790 (Adjusted R Squared = .775)a.

R Squared = .129 (Adjusted R Squared = .066)b.

MANOVA (Factorial design)

Variabel dependen lebih dari 1 dan kelompok lebih dari 1, semisal:

Bagaimana rata-rata kadar trigliserida dan kolesterol (2 variabel dependen) berbeda secara bermakna untuk tiap kelompok usia dan jenis kelamin (2 kelompok)

MANOVA (Factorial design)

Buka SPSS: file – data –dietstudy Analyze – general linear model –

multivariate:Dependent variable: TG0 dan WTG0Fixed factor: gender dan AGEGROUPOption – homogeneity testContinue- OK

MANOVA (Factorial design)

Box's Test of Equality of Covariance Matricesa

3.502.339

6463.698

.916

Box's MFdf1df2Sig.

Tests the null hypothesis that the observed covariancematrices of the dependent variables are equal across groups.

Design: Intercept+GENDER+AGEGROUP+GENDER *AGEGROUP

a.

MANOVA (Factorial design)

Levene's Test of Equality of Error Variancesa

1.852 5 10 .1901.091 5 10 .422

CholesterolTriglyceride

F df1 df2 Sig.

Tests the null hypothesis that the error variance of the dependentvariable is equal across groups.

Design: Intercept+GENDER+AGEGROUP+GENDER *AGEGROUP

a.

MANOVA (Factorial design)Multivariate Testsc

.996 1010.769a 2.000 9.000 .000

.004 1010.769a 2.000 9.000 .000224.615 1010.769a 2.000 9.000 .000224.615 1010.769a 2.000 9.000 .000

.826 21.364a 2.000 9.000 .000

.174 21.364a 2.000 9.000 .0004.748 21.364a 2.000 9.000 .0004.748 21.364a 2.000 9.000 .000.164 .448 4.000 20.000 .773.837 .420a 4.000 18.000 .792.194 .388 4.000 16.000 .814.188 .938b 2.000 10.000 .423.199 .551 4.000 20.000 .701.808 .506a 4.000 18.000 .732.230 .459 4.000 16.000 .765.187 .933b 2.000 10.000 .425

Pillai's TraceWilks' LambdaHotelling's TraceRoy's Largest RootPillai's TraceWilks' LambdaHotelling's TraceRoy's Largest RootPillai's TraceWilks' LambdaHotelling's TraceRoy's Largest RootPillai's TraceWilks' LambdaHotelling's TraceRoy's Largest Root

EffectIntercept

GENDER

AGEGROUP

GENDER * AGEGROUP

Value F Hypothesis df Error df Sig.

Exact statistica.

The statistic is an upper bound on F that yields a lower bound on the significance level.b.

Design: Intercept+GENDER+AGEGROUP+GENDER * AGEGROUPc.

MANOVA (Factorial design)Tests of Between-Subjects Effects

13794.217a 5 2758.843 9.161 .0023458.471b 5 691.694 .753 .603

462728.955 1 462728.955 1536.523 .000234481.867 1 234481.867 255.108 .00010936.955 1 10936.955 36.317 .0002734.239 1 2734.239 2.975 .115

37.922 2 18.961 .063 .9391426.678 2 713.339 .776 .486491.654 2 245.827 .816 .469430.109 2 215.054 .234 .796

3011.533 10 301.1539191.467 10 919.147

646448.000 16319289.000 1616805.750 1512649.937 15

Dependent VariableCholesterolTriglycerideCholesterolTriglycerideCholesterolTriglycerideCholesterolTriglycerideCholesterolTriglycerideCholesterolTriglycerideCholesterolTriglycerideCholesterolTriglyceride

SourceCorrected Model

Intercept

GENDER

AGEGROUP

GENDER * AGEGROUP

Error

Total

Corrected Total

Type III Sumof Squares df Mean Square F Sig.

R Squared = .821 (Adjusted R Squared = .731)a.

R Squared = .273 (Adjusted R Squared = -.090)b.

MANOVA (Factorial design)

Estimated Marginal Means of Cholesterol

age grouping

>6050-60<50

Estim

ated

Mar

gina

l Mea

ns

240

220

200

180

160

140

Gender

Male

Female

Estimated Marginal Means of Triglyceride

age grouping

>6050-60<50

Estim

ated

Mar

gina

l Mea

ns

180

160

140

120

100

Gender

Male

Female

PENGUKURAN BERULANG

Mengetahui apakah ada perbedaan yang bermakna pada suatu variabel yang diukur secara berulang

PENGUKURAN BERULANG Buka SPSS: file – data –dietstudy Analyze – General Linear Model – Repeated Measures:

Within subject factor name: ketik kolest Number of levels: ketik 5 (wgt0 – wgt4) Klik: Add – define:

Within subject factor: pindahkan wgt0 s/d wgt4 Between subject factor: pindahkan gender Klik: plot:

Horizontal axis: gender Separate line: kolest

Add dan Continue OK

PENGUKURAN BERULANG

Within-Subjects Factors

Measure: MEASURE_1

WGT0WGT1WGT2WGT3WGT4

KOLES12345

DependentVariable

Between-Subjects Factors

Male 9Female 7

01

GenderValue Label N

PENGUKURAN BERULANG

Multivariate Testsb

.908 27.123a 4.000 11.000 .000

.092 27.123a 4.000 11.000 .0009.863 27.123a 4.000 11.000 .0009.863 27.123a 4.000 11.000 .000.139 .444a 4.000 11.000 .775.861 .444a 4.000 11.000 .775.162 .444a 4.000 11.000 .775.162 .444a 4.000 11.000 .775

Pillai's TraceWilks' LambdaHotelling's TraceRoy's Largest RootPillai's TraceWilks' LambdaHotelling's TraceRoy's Largest Root

EffectKOLES

KOLES * GENDER

Value F Hypothesis df Error df Sig.

Exact statistica.

Design: Intercept+GENDER Within Subjects Design: KOLES

b.

PENGUKURAN BERULANG

Mauchly's Test of Sphericityb

Measure: MEASURE_1

.399 11.423 9 .252 .763 1.000 .250Within Subjects EffectKOLES

Mauchly's WApprox.

Chi-Square df Sig.Greenhouse-Geisser Huynh-Feldt Lower-bound

Epsilona

Tests the null hypothesis that the error covariance matrix of the orthonormalized transformed dependent variables isproportional to an identity matrix.

May be used to adjust the degrees of freedom for the averaged tests of significance. Corrected tests are displayed in theTests of Within-Subjects Effects table.

a.

Design: Intercept+GENDER Within Subjects Design: KOLES

b.

PENGUKURAN BERULANG

Tests of Within-Subjects Effects

Measure: MEASURE_1

639.892 4 159.973 57.534 .000639.892 3.052 209.668 57.534 .000639.892 4.000 159.973 57.534 .000639.892 1.000 639.892 57.534 .000

2.142 4 .536 .193 .9412.142 3.052 .702 .193 .9042.142 4.000 .536 .193 .9412.142 1.000 2.142 .193 .667

155.708 56 2.780155.708 42.727 3.644155.708 56.000 2.780155.708 14.000 11.122

Sphericity AssumedGreenhouse-GeisserHuynh-FeldtLower-boundSphericity AssumedGreenhouse-GeisserHuynh-FeldtLower-boundSphericity AssumedGreenhouse-GeisserHuynh-FeldtLower-bound

SourceKOLES

KOLES * GENDER

Error(KOLES)

Type III Sumof Squares df Mean Square F Sig.

PENGUKURAN BERULANGTests of Within-Subjects Contrasts

Measure: MEASURE_1

639.032 1 639.032 133.643 .000.737 1 .737 .479 .500

9.921E-05 1 9.921E-05 .000 .996.123 1 .123 .089 .770.032 1 .032 .007 .936.309 1 .309 .201 .661.500 1 .500 .146 .708

1.301 1 1.301 .947 .34766.943 14 4.78221.531 14 1.53847.994 14 3.42819.241 14 1.374

KOLESLinearQuadraticCubicOrder 4LinearQuadraticCubicOrder 4LinearQuadraticCubicOrder 4

SourceKOLES

KOLES * GENDER

Error(KOLES)

Type III Sumof Squares df Mean Square F Sig.

PENGUKURAN BERULANG

Tests of Between-Subjects Effects

Measure: MEASURE_1Transformed Variable: Average

2860620.105 1 2860620.105 2162.867 .00066062.105 1 66062.105 49.948 .00018516.483 14 1322.606

SourceInterceptGENDERError

Type III Sumof Squares df Mean Square F Sig.

PENGUKURAN BERULANGEstimated Marginal Means of MEASURE_1

KOLES

54321

Estim

ated

Mar

gina

l Mea

ns240

220

200

180

160

140

Gender

Male

Female

REGRESI BERGANDA

Memprediksi besar variabel dependen dengan menggunakan data variabel bebas yang sudah diketahui besarnya

REGRESI BERGANDA

Analyze – regression – linear:Dependent : WGT4 Independent(s): WGT0, TG0, AGECase labels: genderMethod: enterOK

REGRESI BERGANDA

Variables Entered/Removedb

Cholesterol, Age inyears,Triglyceride

a

. Enter

Model1

VariablesEntered

VariablesRemoved Method

All requested variables entered.a.

Dependent Variable: Final cholesterolb.

REGRESI BERGANDA

Model Summary

.997a .994 .992 2.953Model1

R R SquareAdjustedR Square

Std. Error ofthe Estimate

Predictors: (Constant), Cholesterol, Age in years,Triglyceride

a.

REGRESI BERGANDA

ANOVAb

16736.790 3 5578.930 639.737 .000a

104.648 12 8.72116841.438 15

RegressionResidualTotal

Model1

Sum ofSquares df Mean Square F Sig.

Predictors: (Constant), Cholesterol, Age in years, Triglyceridea.

Dependent Variable: Final cholesterolb.

REGRESI BERGANDACoefficientsa

3.375 8.574 .394 .701-.164 .111 -.034 -1.477 .165-.010 .027 -.009 -.373 .716.995 .024 .994 42.243 .000

(Constant)Age in yearsTriglycerideCholesterol

Model1

B Std. Error

UnstandardizedCoefficients

Beta

StandardizedCoefficients

t Sig.

Dependent Variable: Final cholesterola.

Persamaan regresi:

Kadar kolesterol akhir = 3,375 – 0,164 usia – 0,10 kadar trigliserida awal + 0,995 kadar kolesterol awal

REGRESI BERGANDA

Residuals Statisticsa

142.66 249.33 190.31 33.403 16-5.05 4.88 .00 2.641 16

-1.426 1.767 .000 1.000 16-1.712 1.652 .000 .894 16

Predicted ValueResidualStd. Predicted ValueStd. Residual

Minimum Maximum Mean Std. Deviation N

Dependent Variable: Final cholesterola.

REGRESI BERGANDAVariables Entered/Removedb

Cholesterola . Enter

Model1

VariablesEntered

VariablesRemoved Method

All requested variables entered.a.

Dependent Variable: Final cholesterolb.

Model Summaryb

.996a .993 .992 2.986Model1

R R SquareAdjustedR Square

Std. Error ofthe Estimate

Predictors: (Constant), Cholesterola.

Dependent Variable: Final cholesterolb.

REGRESI BERGANDA

ANOVAb

16716.618 1 16716.618 1874.976 .000a

124.819 14 8.91616841.438 15

RegressionResidualTotal

Model1

Sum ofSquares df Mean Square F Sig.

Predictors: (Constant), Cholesterola.

Dependent Variable: Final cholesterolb.

REGRESI BERGANDACoefficientsa

-7.536 4.630 -1.628 .126.997 .023 .996 43.301 .000

(Constant)Cholesterol

Model1

B Std. Error

UnstandardizedCoefficients

Beta

StandardizedCoefficients

t Sig.

Dependent Variable: Final cholesterola.

Persamaan regresi:

Kadar kolesterol akhir = -7,536 + 0,997 kadar kolesterol awal

Correlations

1 .996**. .000

16 16.996** 1.000 .

16 16

Pearson CorrelationSig. (2-tailed)NPearson CorrelationSig. (2-tailed)N

Cholesterol

Final cholesterol

CholesterolFinal

cholesterol

Correlation is significant at the 0.01 level (2-tailed).**.

Linear Regression

150 175 200 225 250

Cholesterol

150

175

200

225

250

Fina

l cho

lest

erol

Final cholesterol = -7.54 + 1.00 * w gt0R-Square = 0.99

Uji regresi logistik binari

Ingin memprediksi variabel dependen yang berskala binari (ya=1 dan tidak=0) dengan menggunakan data variabel independen yang sudah diketahui besarnya

Uji regresi logistik binari

Buka SPSS: file – data –dietstudy Analyze – Regression – Binary logistic:

Dependent: cholst0 (status kadar kolesterol awal, 1=tinggi, 0=normal)

Covariates: age dan TG0Options: Homer-Lemeshow goodness of fitOK

Uji regresi logistik binari

Case Processing Summary

16 100.00 .0

16 100.00 .0

16 100.0

Unweighted Cases a

Included in AnalysisMissing CasesTotal

Selected Cases

Unselected CasesTotal

N Percent

If weight is in effect, see classification table for the totalnumber of cases.

a.

Uji regresi logistik binari

Omnibus Tests of Model Coefficients

1.902 2 .3861.902 2 .3861.902 2 .386

StepBlockModel

Step 1Chi-square df Sig.

Uji regresi logistik binariModel Summary

20.028 .112 .150Step1

-2 Loglikelihood

Cox & SnellR Square

NagelkerkeR Square

Hosmer and Lemeshow Test

9.129 6 .166Step1

Chi-square df Sig.

Uji regresi logistik binari

Contingency Table for Hosmer and Lemeshow Test

2 1.570 0 .430 22 1.516 0 .484 21 1.453 1 .547 20 1.118 2 .882 20 1.024 2 .976 22 .920 0 1.080 21 .787 1 1.213 21 .613 1 1.387 2

12345678

Step1

Observed Expected

cholesterol status =normal

Observed Expected

cholesterol status =high

Total

Uji regresi logistik binari

Classification Tablea

5 4 55.64 3 42.9

50.0

Observednormalhigh

cholesterol status

Overall Percentage

Step 1normal highcholesterol status Percentage

Correct

Predicted

The cut value is .500a.

Uji regresi logistik binari

Variables in the Equation

.042 .079 .277 1 .598 1.043

.025 .020 1.527 1 .217 1.025-5.970 5.416 1.215 1 .270 .003

AGETG0Constant

Step1

a

B S.E. Wald df Sig. Exp(B)

Variable(s) entered on step 1: AGE, TG0.a.

Penafsiran dan prediksi:

Kadar kolesterol tinggi = -5,970 + 0,42 usia + 0,025 kadar trigliserida

REGRESI BERGANDA – variabel dummy Memprediksi besar variabel dependen

dengan menggunakan data variabel bebas dimana satu atau lebih variabel bebas adalah variabel dummy (ya=1 dan tidak=0)

REGRESI BERGANDA – variabel dummy Buka SPSS: file – data –dietstudy Analyze – Regression – Linier:

Dependent: wgt4 Independent: gender, cholst0, trigst0Methods: enterOK

REGRESI BERGANDA – variabel dummy

Variables Entered/Removedb

triglyceridestatus,cholesterolstatus,Gender

a

. Enter

Model1

VariablesEntered

VariablesRemoved Method

All requested variables entered.a.

Dependent Variable: Final cholesterolb.

REGRESI BERGANDA – variabel dummy

Model Summary

.932a .869 .836 13.551Model1

R R SquareAdjustedR Square

Std. Error ofthe Estimate

Predictors: (Constant), triglyceride status, cholesterolstatus, Gender

a.

REGRESI BERGANDA – variabel dummy

ANOVAb

14637.729 3 4879.243 26.569 .000a

2203.709 12 183.64216841.438 15

RegressionResidualTotal

Model1

Sum ofSquares df Mean Square F Sig.

Predictors: (Constant), triglyceride status, cholesterol status, Gendera.

Dependent Variable: Final cholesterolb.

REGRESI BERGANDA – variabel dummy

Coefficientsa

194.020 10.219 18.987 .000-35.437 10.971 -.542 -3.230 .00730.003 10.877 .459 2.758 .017-3.039 7.100 -.046 -.428 .676

(Constant)Gendercholesterol statustriglyceride status

Model1

B Std. Error

UnstandardizedCoefficients

Beta

StandardizedCoefficients

t Sig.

Dependent Variable: Final cholesterola.

REGRESI BERGANDA – variabel dummy

Coefficientsa

192.500 9.276 20.752 .000-34.786 10.518 -.532 -3.307 .00629.786 10.518 .455 2.832 .014

(Constant)Gendercholesterol status

Model1

B Std. Error

UnstandardizedCoefficients

Beta

StandardizedCoefficients

t Sig.

Dependent Variable: Final cholesterola.

Penafsiran dan prediksi:

Kadar kolesterol akhir = 192,500 – 34,786 gender + 29,786 kadar kolesterol awal

Uji analisis faktor

Ingin diketahui kadar kolesteol yang mana yang menentukan kadar kolesterol akhir dari beberapa data kadar kolesterol yang ada?

Uji analisis faktor

Buka SPSS: file – data –dietstudy Analyze – Data reduction - Factor:

Wgt0 s/d wgt4Descriptives:

Correlation matrix: KMO and Bartletts’s test of spherity Anti image

Continue dan OK

Uji analisis faktor

KMO and Bartlett's Test

.894

294.19810

.000

Kaiser-Meyer-Olkin Measure of SamplingAdequacy.

Approx. Chi-SquaredfSig.

Bartlett's Test ofSphericity

Uji analisis faktor

Anti-image Matrices

.003 .000 -.001 -.001 .001

.000 .003 -.001 .000 -1.847E-05-.001 -.001 .001 .000 -.001-.001 .000 .000 .003 -.001.001 -1.847E-05 -.001 -.001 .003.899a -.092 -.536 -.248 .301

-.092 .935a -.516 .065 -.007-.536 -.516 .820a -.246 -.512-.248 .065 -.246 .937a -.378.301 -.007 -.512 -.378 .889a

Cholesterol1st interim cholesterol2nd interim cholesterol3rd interim cholesterolFinal cholesterolCholesterol1st interim cholesterol2nd interim cholesterol3rd interim cholesterolFinal cholesterol

Anti-image Covariance

Anti-image Correlation

Cholesterol1st interimcholesterol

2nd interimcholesterol

3rd interimcholesterol

Finalcholesterol

Measures of Sampling Adequacy(MSA)a.

Uji analisis faktor

Communalities

1.000 .9981.000 .9981.000 .9991.000 .9981.000 .998

Cholesterol1st interim cholesterol2nd interim cholesterol3rd interim cholesterolFinal cholesterol

Initial Extraction

Extraction Method: Principal Component Analysis.

Uji analisis faktor

Total Variance Explained

4.991 99.816 99.816 4.991 99.816 99.816.004 .080 99.896.003 .059 99.954.002 .033 99.988.001 .012 100.000

Component12345

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

Extraction Method: Principal Component Analysis.

Uji analisis faktor

Scree Plot

Component Number

54321

Eige

nval

ue6

5

4

3

2

1

0

Uji analisis faktor

Component Matrixa

.999

.9991.000.999.999

Cholesterol1st interim cholesterol2nd interim cholesterol3rd interim cholesterolFinal cholesterol

1

Component

Extraction Method: Principal Component Analysis.1 components extracted.a.

ANALISIS DISKRIMINAN

Ingin membuat model yang bisa secara jelas menunjukkan perbedaan antar isi variabel dependen, misal:Kadar kolesterol dan trigliserida pada

kelompok laki-laki (=0) dan perempuan (=1)

ANALISIS DISKRIMINAN

Buka SPSS: file – data –dietstudy Analyze – Clasify - Discriminant:

Grouping variable: gender Define range: 0 dan 1 Independent: age, wgt0, tg0, wgt4 dan tg4 Statistics:

Descriptives: Means Function coefficients: Fisher’s dam Unstandardized

ANALISIS DISKRIMINAN

Use stepwise method Method: Mahalanobis distance Criteria: use probability of F

Clasify: Display: Casewise results, Leave-one-out-

classificationContinue dan OK

ANALISIS DISKRIMINANGroup Statistics

54.00 7.036 9 9.000147.33 26.847 9 9.000223.78 18.754 9 9.000117.11 28.790 9 9.000215.67 18.076 9 9.00055.57 7.208 7 7.000

127.00 29.597 7 7.000165.71 10.935 7 7.000133.71 29.607 7 7.000157.71 12.932 7 7.00054.69 6.916 16 16.000

138.44 29.040 16 16.000198.38 33.472 16 16.000124.38 29.412 16 16.000190.31 33.508 16 16.000

Age in yearsTriglycerideCholesterolFinal triglycerideFinal cholesterolAge in yearsTriglycerideCholesterolFinal triglycerideFinal cholesterolAge in yearsTriglycerideCholesterolFinal triglycerideFinal cholesterol

GenderMale

Female

Total

Mean Std. Deviation Unweighted WeightedValid N (listwise)

ANALISIS DISKRIMINAN

Variables Entered/Removeda,b,c,d

Cholesterol 13.367 Male and

Female 52.633 1 14.000 4.192E-06

Step1

Entered StatisticBetweenGroups Statistic df1 df2 Sig.

Exact F

Min. D Squared

At each step, the variable that maximizes the Mahalanobis distance between the two closestgroups is entered.

Maximum number of steps is 10.a.

Maximum significance of F to enter is .05.b.

Minimum significance of F to remove is .10.c.

F level, tolerance, or VIN insufficient for further computation.d.

ANALISIS DISKRIMINAN

Variables in the Analysis

1.000 .000CholesterolStep1

ToleranceSig. of F to

Remove

ANALISIS DISKRIMINANVariables Not in the Analysis

1.000 1.000 .668 .049 Male andFemale

1.000 1.000 .172 .525 Male andFemale

1.000 1.000 .000 13.367 Male andFemale

1.000 1.000 .277 .325 Male andFemale

1.000 1.000 .000 12.998 Male andFemale

.995 .995 .977 13.368 Male andFemale

.945 .945 .178 16.003 Male andFemale

.957 .957 .869 13.404 Male andFemale

.034 .034 .953 13.372 Male andFemale

Age in years

Triglyceride

Cholesterol

Final triglyceride

Final cholesterol

Age in years

Triglyceride

Final triglyceride

Final cholesterol

Step0

1

ToleranceMin.

ToleranceSig. of Fto Enter

Min. DSquared

BetweenGroups

ANALISIS DISKRIMINANWilks' Lambda

1 .210 1 1 14 52.633 1 14.000 .000Step1

Number ofVariables Lambda df1 df2 df3 Statistic df1 df2 Sig.

Exact F

Eigenvalues

3.760a 100.0 100.0 .889Function1

Eigenvalue % of Variance Cumulative %CanonicalCorrelation

First 1 canonical discriminant functions were used in theanalysis.

a.

Wilks' Lambda

.210 21.062 1 .000Test of Function(s)1

Wilks'Lambda Chi-square df Sig.

ANALISIS DISKRIMINAN

Structure Matrix

1.000.983

-.234-.207-.069

CholesterolFinal cholesterola

Triglyceridea

Final triglyceridea

Age in yearsa

1Function

Pooled within-groups correlations between discriminatingvariables and standardized canonical discriminant functions Variables ordered by absolute size of correlation within function.

This variable not used in the analysis.a.

ANALISIS DISKRIMINANCanonical Discriminant Function Coefficients

.063-12.491

Cholesterol(Constant)

1Function

Unstandardized coefficients

Skor Z = -12,491 + 0,063 kadar kolesterol awal

ANALISIS DISKRIMINAN

Functions at Group Centroids

1.600-2.057

GenderMaleFemale

1Function

Unstandardized canonical discriminantfunctions evaluated at group means

ANALISIS DISKRIMINAN

Prior Probabilities for Groups

.500 9 9.000

.500 7 7.0001.000 16 16.000

GenderMaleFemaleTotal

Prior Unweighted WeightedCases Used in Analysis

ANALISIS DISKRIMINANClassification Function Coefficients

.887 .657-99.967 -55.134

Cholesterol(Constant)

Male FemaleGender

Fisher's linear discriminant functions

Skor kolesterol pada laki-laki = -99,967 + 0,887 kolesterol awal

Skor kolesterol pada perempuan = -55,134 + 0,657 kolesterol awal

Selisih antar keduanya (skor Z) = -44,833 + 0,23 kolesterol awal

Skor Z sebelumnya = -12,491 + 0,063 kolesterol awal

ANALISIS DISKRIMINANCasewise Statistics

0 0 .105 1 .679 2.635 1 .321 4.133 -.0240 0 .405 1 1.000 .693 1 .000 20.148 2.4320 0 .561 1 1.000 .337 1 .000 17.951 2.1801 1 .403 1 .974 .700 0 .026 7.950 -1.2200 0 .764 1 .996 .091 1 .004 11.258 1.2991 1 .836 1 .997 .043 0 .003 11.897 -1.8500 0 .911 1 .998 .013 1 .002 12.561 1.4881 1 .935 1 .998 .007 0 .002 12.782 -1.9760 0 .119 1 .727 2.434 1 .273 4.393 .0390 0 .561 1 1.000 .337 1 .000 17.951 2.1801 1 .403 1 .974 .700 0 .026 7.950 -1.2201 1 .627 1 1.000 .236 0 .000 17.155 -2.5421 1 .583 1 1.000 .301 0 .000 17.681 -2.6050 0 .624 1 .993 .240 1 .007 10.026 1.1100 0 .036 1 1.000 4.376 1 .000 33.040 3.6911 1 .354 1 1.000 .858 0 .000 21.001 -2.9830 0 .047 1 .615 3.928 2 .385 4.8680 0 .353 1 1.000 .863 2 .000 19.8130 0 .523 1 1.000 .407 2 .000 17.1331 1 .332 1 .969 .939 1 .031 7.8390 0 .743 1 .995 .107 2 .005 10.5301 1 .816 1 .996 .054 1 .004 11.0870 0 .903 1 .997 .015 2 .003 11.6761 1 .927 1 .997 .008 1 .003 11.8750 0 .059 1 .681 3.556 2 .319 5.0710 0 .523 1 1.000 .407 2 .000 17.1331 1 .332 1 .969 .939 1 .031 7.8391 1 .581 1 1.000 .304 1 .000 16.2491 1 .532 1 1.000 .390 1 .000 16.8400 0 .592 1 .990 .287 2 .010 9.4930 0 .005 1 1.000 7.932 2 .000 47.3201 1 .280 1 1.000 1.169 1 .000 21.003

Case Number1234567891011121314151612345678910111213141516

Original

Cross-validated a

Actual GroupPredicted

Group p dfP(D>d | G=g)

P(G=g | D=d)

SquaredMahalanobisDistance to

Centroid

Highest Group

Group P(G=g | D=d)

SquaredMahalanobisDistance to

Centroid

Second Highest Group

Function 1

DiscriminantScores

For the original data, squared Mahalanobis distance is based on canonical functions.For the cross-validated data, squared Mahalanobis distance is based on observations.

Cross validation is done only for those cases in the analysis. In cross validation, each case is classified by the functions derived from all cases other than that case.a.

ANALISIS DISKRIMINANClassification Resultsb,c

9 0 90 7 7

100.0 .0 100.0.0 100.0 100.09 0 90 7 7

100.0 .0 100.0.0 100.0 100.0

GenderMaleFemaleMaleFemaleMaleFemaleMaleFemale

Count

%

Count

%

Original

Cross-validated a

Male Female

Predicted GroupMembership

Total

Cross validation is done only for those cases in the analysis. Incross validation, each case is classified by the functions derivedfrom all cases other than that case.

a.

100.0% of original grouped cases correctly classified.b.

100.0% of cross-validated grouped cases correctly classified.c.

ANALISIS DISKRIMINAN Kesimpulan:

Analisis Wilk’s Lambda (sig <0.001) Variable in analysis (Variabel yang membedakan

gender laki-laki dan perempuan adalah kadar kolesterol awal)

Model diskriminannya:Skor Z = -12,491 + 0,063 kadar kolesterol awal Model di atas mempunyai ketepatan

mengklasifikasikan gender sebesar 100% (ketepatan sangat tinggi), dan model dapat digunakan untuk mengklasifikasikan gender dari data kolesterol awal