Lampiran 1 Deskripsi Data Primer

Transkripsi

1 L A M P I R A N

2 53 Lampiran 1 Deskripsi Data Primer No Peubah Keterangan Persentase (%) 1 Jenis Kelamin Perempuan 60.5 laki-laki Pendidikan Ayah SD,SMP 5.30 SMA,STM,SMK 65. D3,Sarjana Muda, S1,S2 2.6 tidak ada 1.3 Pendidikan Ibu SD,SMP 11. SMA,STM,SMK 64.5 D3,Sarjana Muda, S1,S2 23. tidak ada 0 3 Pekerjaan Ayah PNS,BUMN 23. Swasta,wiraswasta 52.6 TNI,Polri 1.4 Petani/Pelaut 3. Pekerjaan Ibu Ibu rumah tangga 3. PNS,Guru 1.4 Wiraswasta 6.6 Dokter Anak ke Tinggal dengan ortu Tidak 3. Ya Ortu cerai / wafat Tidak 4. Ya 5.3 Riwayat penyakit Ada 1.3 berat Tidak ada 5.5 Status kepemilikan bukan milik pribadi 10.5 Rumah milik pribadi.5 Ikut Bimbel Tidak 25 Ya 5 10 Internet di Rumah Tidak 11. Ya.2 11 Banyak Kepemilikan Sepeda motor 12 Banyak Kepemilikan Mobil Tidak punya 3. 1 buah buah 4. 3 buah buah.2 Tidak Punya buah buah.2 13 Daya Listrik 00 MW MW MW MW MW

3 52 Lampiran 2 Deskripsi Data Sekunder Nilai Mean Variance Minimum Maximum UN IND UN ING UN MAT UN FIS UN KIM UN BIO NR IND NR ING NR MAT NR FIS NR KIM NR BIO NR AGM NR PKN NR SEJ NR SRP NR PJK NR TIK NR BAJ NUS IND NUS ING NUS MAT NUS FIS NUS KIM NUS BIO

4 53 Lampiran 3 Boxplot Data Sekunder 10.0 Boxplot of UN IND, UN ING, UN MAT, UN FIS, UN KIM.5.0 Data UN IND UN ING UN MAT UN FIS UN KIM 6 Boxplot of NR IND, NR ING, NR MAT, NR FIS, NR KIM, NR BIO 4 2 Data RT IND RT ING RT MAT RT FIS RT KIM RT BIO

5 52 Boxplot of NR AGM, NR PKN, NR SEJ, NR SRP, NR PJK, NR TIK, NR BAJ 0 5 Data 0 5 RT AGM RT PKN RT SEJ RT SRP RT PJK RT TIK RT BAJ Boxplot of NUS IND, NUS ING, NUS MAT, NUS FIS, NUS KIM, NUS BIO.0. Data NUS IND NUS ING NUS MAT NUS FIS NUS KIM NUS BIO

6 53 Lampiran 4 Hasil Uji kelinieran Scatterplot of UN IND vs RT IND, UN IND vs RT ING, UN IND vs RT MAT, U UN IND*RT IND UN IND*RT ING UN IND*RT MAT UN IND*RT FIS UN IND*RT KIM UN IND*RT BIO UN ING*RT IND UN ING*RT ING UN ING*RT MAT UN ING*RT FIS UN ING*RT KIM UN ING*RT BIO UN MAT*RT IND UN MAT*RT ING UN MAT*RT MAT UN MAT*RT FIS UN MAT*RT KIM UN MAT*RT BIO Scatterplot of UN FIS vs RT IND, UN FIS vs RT ING, UN FIS vs RT MAT, U UN FIS*RT IND UN FIS*RT ING UN FIS*RT MAT UN FIS*RT FIS UN FIS*RT KIM UN FIS*RT BIO UN KIM*RT IND UN KIM*RT ING UN KIM*RT MAT UN KIM*RT FIS UN KIM*RT KIM UN KIM*RT BIO UN BIO*RT IND UN BIO*RT ING UN BIO*RT MAT UN BIO*RT FIS UN BIO*RT KIM UN BIO*RT BIO Scatterplot of UN IND vs RT AGM, UN IND vs RT PKN, UN IND vs RT SEJ, U UN IND*RT AGM UN IND*RT PKN UN IND*RT SEJ UN IND*RT SRP UN IND*RT PJK UN IND*RT TIK UN IND*RT BAJ UN ING*RT AGM UN ING*RT PKN UN ING*RT SEJ UN ING*RT SRP UN ING*RT PJK UN ING*RT TIK UN ING*RT BAJ UN MAT*RT AGM UN MAT*RT PKN UN MAT*RT SEJ UN MAT*RT SRP UN MAT*RT PJK UN MAT*RT TIK

7 52 Scatterplot of UN MAT vs RT BAJ, UN FIS vs NUS IND, UN FIS vs NUS ING, UN MAT*RT BAJ UN FIS*NUS IND UN FIS*NUS ING UN FIS*NUS MAT UN FIS*NUS FIS UN FIS*NUS KIM UN FIS*NUS BIO UN KIM*NUS IND UN KIM*NUS ING UN KIM*NUS MAT UN KIM*NUS FIS.6 UN KIM*NUS KIM.6 UN KIM*NUS BIO UN BIO*NUS IND UN BIO*NUS ING UN BIO*NUS MAT UN BIO*NUS FIS UN BIO*NUS KIM UN BIO*NUS BIO Scatterplot of UN FIS vs NUS IND, UN FIS vs NUS ING, UN FIS vs NUS MAT UN FIS*NUS IND UN FIS*NUS ING UN FIS*NUS MAT UN FIS*NUS FIS UN FIS*NUS KIM UN FIS*NUS BIO UN KIM*NUS IND UN KIM*NUS ING UN KIM*NUS MAT UN KIM*NUS FIS UN KIM*NUS KIM UN KIM*NUS BIO UN BIO*NUS IND UN BIO*NUS ING UN BIO*NUS MAT UN BIO*NUS FIS UN BIO*NUS KIM UN BIO*NUS BIO

8 53 Lampiran 5 Sintaks dan Hasil Program SAS untuk Uji Kenormalan Ganda NUN /* Program uji kenormalan multivariate UN GANTI*/ title Output skewness dan kurtosis mardia utk UN ; options ls = 64 ps=45 nodate nonumber; /* Program ini menguji kenormalan multivariate menggunakan Mardia s skewness and kurtosis measures */ proc iml ; y ={...Data /*Untuk menentukan banyaknya data dan dimensi dari vektornya. Peubah dfchi adalah derajat bebas untuk pendugaan Chi-kuadrat dari skewness (kemenjuluran) multivariate. */ n = nrow(y) ; p = ncol(y) ; dfchi = p*(p+1)*(p+2)/6 ; /* q is projection matrix, s is the maximum likelihood estimate of the variance covariance matrix, g_matrix is n by n the matrix of g(i,j) elements, beta1hat and beta2hat are respectively the Mardia s sample skewness and kurtosis measures, kappa1 and kappa2 are the test statistics based on skewness and kurtosis to test for normality and pvalskew and pvalkurt are corresponding p values. */ q = i(n) - (1/n)*j(n,n,1); s = (1/(n))*y`*q*y ; s_inv = inv(s) ; g_matrix = q*y*s_inv*y`*q; beta1hat = ( sum(g_matrix#g_matrix#g_matrix) )/(n*n); beta2hat =trace( g_matrix#g_matrix )/n ; kappa1 = n*beta1hat/6 ; kappa2 = (beta2hat - p*(p+2) ) /sqrt(*p*(p+2)/n) ; pvalskew = 1 - probchi(kappa1,dfchi) ; pvalkurt = 2*( 1 - probnorm(abs(kappa2)) ); print s ; print s_inv ; print beta1hat kappa1 pvalskew; print beta2hat kappa2 pvalkurt; Output skewness dan kurtosis mardia utk UN Interpretasi: BETA1HAT KAPPA1 PVALSKEW BETA2HAT KAPPA2 PVALKURT Karena (pvalskewness = ) > (α = 0.05) maka terima H Karena (pvalkurtosis = ) > (α = 0.05) maka terima H Jadi nilai UN berdistribusi Normal (Khatree, 1) 0 0

9 52 Lampiran 6 Sintaks dan Hasil Program SAS untuk Uji Kenormalan Ganda NR UN /* Program uji kenormalan multivariate RAPORT UN GANTI*/ title Output skewness dan kurtosis mardia utk RAPORT UN ; options ls = 64 ps=45 nodate nonumber; /* Program ini menguji kenormalan multivariate menggunakan Mardia s skewness and kurtosis measures */ proc iml ; y ={ Data /*Untuk menentukan banyaknya data dan dimensi dari vektornya. Peubah dfchi adalah derajat bebas untuk pendugaan Chi-kuadrat dari skewness (kemenjuluran) multivariate. */ n = nrow(y) ; p = ncol(y) ; dfchi = p*(p+1)*(p+2)/6 ; /* q is projection matrix, s is the maximum likelihood estimate of the variance covariance matrix, g_matrix is n by n the matrix of g(i,j) elements, beta1hat and beta2hat are respectively the Mardia s sample skewness and kurtosis measures, kappa1 and kappa2 are the test statistics based on skewness and kurtosis to test for normality and pvalskew and pvalkurt are corresponding p values. */ q = i(n) - (1/n)*j(n,n,1); s = (1/(n))*y`*q*y ; s_inv = inv(s) ; g_matrix = q*y*s_inv*y`*q; beta1hat = ( sum(g_matrix#g_matrix#g_matrix) )/(n*n); beta2hat =trace( g_matrix#g_matrix )/n ; kappa1 = n*beta1hat/6 ; kappa2 = (beta2hat - p*(p+2) ) /sqrt(*p*(p+2)/n) ; pvalskew = 1 - probchi(kappa1,dfchi) ; pvalkurt = 2*( 1 - probnorm(abs(kappa2)) ); print s ; print s_inv ; print beta1hat kappa1 pvalskew; print beta2hat kappa2 pvalkurt; Output skewness dan kurtosis mardia utk RAPORT UN Interpretasi: BETA1HAT KAPPA1 PVALSKEW BETA2HAT KAPPA2 PVALKURT Karena (pvalskewness = 0.146) > (α = 0.05) maka terima H Karena (pvalkurtosis = ) > (α = 0.05) maka terima H Jadi nilai Raport UN berdistribusi Normal (Khatree, 1) 0 0

10 53 Lampiran Sintaks dan Hasil Program SAS untuk Uji Kenormalan Ganda NR non UN /* Program uji kenormalan multivariate RAPORT NON UN GANTI*/ title Output skewness dan kurtosis mardia utk RAPORT NON UN ; options ls = 64 ps=45 nodate nonumber; /* Program ini menguji kenormalan multivariate menggunakan Mardia s skewness and kurtosis measures */ proc iml ; y ={ data /*Untuk menentukan banyaknya data dan dimensi dari vektornya. Peubah dfchi adalah derajat bebas untuk pendugaan Chi-kuadrat dari skewness (kemenjuluran) multivariate. */ n = nrow(y) ; p = ncol(y) ; dfchi = p*(p+1)*(p+2)/6 ; /* q is projection matrix, s is the maximum likelihood estimate of the variance covariance matrix, g_matrix is n by n the matrix of g(i,j) elements, beta1hat and beta2hat are respectively the Mardia s sample skewness and kurtosis measures, kappa1 and kappa2 are the test statistics based on skewness and kurtosis to test for normality and pvalskew and pvalkurt are corresponding p values. */ q = i(n) - (1/n)*j(n,n,1); s = (1/(n))*y`*q*y ; s_inv = inv(s) ; g_matrix = q*y*s_inv*y`*q; beta1hat = ( sum(g_matrix#g_matrix#g_matrix) )/(n*n); beta2hat =trace( g_matrix#g_matrix )/n ; kappa1 = n*beta1hat/6 ; kappa2 = (beta2hat - p*(p+2) ) /sqrt(*p*(p+2)/n) ; pvalskew = 1 - probchi(kappa1,dfchi) ; pvalkurt = 2*( 1 - probnorm(abs(kappa2)) ); print s ; print s_inv ; print beta1hat kappa1 pvalskew; print beta2hat kappa2 pvalkurt; Output skewness dan kurtosis mardia utk RAPORT NON UN Interpretasi: BETA1HAT KAPPA1 PVALSKEW BETA2HAT KAPPA2 PVALKURT Karena (pvalskewness = ) > (α = 0.05) maka terima H Karena (pvalkurtosis = ) > (α = 0.05) maka terima H 0 Jadi nilai Raport Non UN berdistribusi Normal (Khatree, 1) 0

11 52 Lampiran Sintaks dan Hasil Program SAS untuk Uji Kenormalan Ganda NUS UN /* Program uji kenormalan multivariate NS UN GANTI*/ title Output skewness dan kurtosis mardia utk NS UN ; options ls = 64 ps=45 nodate nonumber; /* Program ini menguji kenormalan multivariate menggunakan Mardia s skewness and kurtosis measures */ proc iml ; y ={ data /*Untuk menentukan banyaknya data dan dimensi dari vektornya. Peubah dfchi adalah derajat bebas untuk pendugaan Chi-kuadrat dari skewness (kemenjuluran) multivariate. */ n = nrow(y) ; p = ncol(y) ; dfchi = p*(p+1)*(p+2)/6 ; /* q is projection matrix, s is the maximum likelihood estimate of the variance covariance matrix, g_matrix is n by n the matrix of g(i,j) elements, beta1hat and beta2hat are respectively the Mardia s sample skewness and kurtosis measures, kappa1 and kappa2 are the test statistics based on skewness and kurtosis to test for normality and pvalskew and pvalkurt are corresponding p values. */ q = i(n) - (1/n)*j(n,n,1); s = (1/(n))*y`*q*y ; s_inv = inv(s) ; g_matrix = q*y*s_inv*y`*q; beta1hat = ( sum(g_matrix#g_matrix#g_matrix) )/(n*n); beta2hat =trace( g_matrix#g_matrix )/n ; kappa1 = n*beta1hat/6 ; kappa2 = (beta2hat - p*(p+2) ) /sqrt(*p*(p+2)/n) ; pvalskew = 1 - probchi(kappa1,dfchi) ; pvalkurt = 2*( 1 - probnorm(abs(kappa2)) ); print s ; print s_inv ; print beta1hat kappa1 pvalskew; print beta2hat kappa2 pvalkurt; Output skewness dan kurtosis mardia utk NS UN BETA1HAT KAPPA1 PVALSKEW BETA2HAT KAPPA2 PVALKURT Interpretasi: Karena (pvalskewness = ) > (α = 0.05) maka terima H Karena (pvalkurtosis = ) > (α = 0.05) maka terima H 0 Jadi nilai NS UN berdistribusi Normal (Khatree, 1) 0

12 53 Lampiran Hasil Output Analisis Kanonik NUN dan NR UN Hasil Analisis Korelasi Kanonik Nilai UN DAN Raport UN The CANCORR Procedure Canonical Correlation Analysis Adjusted Approximate Squared Canonical Canonical Standard Canonical Correlation Correlation Error Correlation Eigenvalues of Inv(E)*H = CanRsq/(1-CanRsq) Eigenvalue Difference Proportion Cumulative Test of H0: The canonical correlations in the current row and all that follow are zero Likelihood Approximate Ratio F Value Num DF Den DF Pr > F

13 52 Multivariate Statistics and F Approximations S=6 M=-0.5 N=31 Statistic Value F Value Num DF Den DF Pr > F Wilks' Lambda Pillai's Trace Hotelling-Lawley Trace Roy's Greatest Root NOTE: F Statistic for Roy's Greatest Root is an upper bound. Hasil Analisis Korelasi Kanonik Nilai UN DAN Raport UN The CANCORR Procedure Canonical Correlation Analysis Standardized Canonical Coefficients for the VAR Variables V6 V1 V2 V3 V4 V5 y y y y y y Standardized Canonical Coefficients for the WITH Variables W1 W2 W3 W4 W5 W6 x x x x x x

14 53 Hasil Analisis Korelasi Kanonik Nilai UN DAN Raport UN The CANCORR Procedure Canonical Redundancy Analysis Standardized Variance of the VAR Variables Explained by Their Own The Opposite Canonical Variables Canonical Variables Canonical Variable Cumulative Canonical Cumulative Number Proportion Proportion R-Square Proportion Proportion Standardized Variance of the WITH Variables Explained by Their Own The Opposite Canonical Variables Canonical Variables Canonical Variable Cumulative Canonical Cumulative Number Proportion Proportion R-Square Proportion Proportion

15 52 Hasil Analisis Korelasi Kanonik Nilai UN DAN Raport UN The CANCORR Procedure Canonical Redundancy Analysis Squared Multiple Correlations Between the VAR Variables and the First M Canonical Variables of the WITH Variables M y y y y y y Squared Multiple Correlations Between the WITH Variables and the First M Canonical Variables of the VAR Variables M x x x x x x Syntax : options ps=100 ls=6 nonumber nodate; title' '; data UN_DAN_RAPORT_UN; input y1 y2 y3 y4 y5 y6 x1 x2 x3 x4 x5 x6; datalines; DATA ; Title 'Hasil Analisis Korelasi Kanonik Nilai UN DAN Raport UN'; proc cancorr redundancy corr data=un_dan_raport_un; var y1-y6; with x1-x6; run;

16 53 Lampiran 10 Hasil Output Analisis Kanonik NUN dan NR non UN Hasil Analisis Korelasi Kanonik Nilai UN DAN Raport Non UN The CANCORR Procedure Canonical Correlation Analysis Adjusted Approximate Squared Canonical Canonical Standard Canonical Correlation Correlation Error Correlation Eigenvalues of Inv(E)*H = CanRsq/(1-CanRsq) Eigenvalue Difference Proportion Cumulative Test of H0: The canonical correlations in the current row and all that follow are zero Likelihood Approximate Ratio F Value Num DF Den DF Pr > F

17 52 Multivariate Statistics and F Approximations S=6 M=0 N=30.5 Statistic Value F Value Num DF Den DF Pr > F Wilks' Lambda Pillai's Trace Hotelling-Lawley Trace Roy's Greatest Root NOTE: F Statistic for Roy's Greatest Root is an upper bound. Hasil Analisis Korelasi Kanonik Nilai UN DAN Raport Non UN The CANCORR Procedure Canonical Redundancy Analysis Standardized Variance of the VAR Variables Explained by Their Own The Opposite Canonical Variables Canonical Variables Canonical Variable Cumulative Canonical Cumulative Number Proportion Proportion R-Square Proportion Proportion

18 53 Standardized Variance of the WITH Variables Explained by Their Own The Opposite Canonical Variables canonical Variables Canonical Variable Cumulative Canonical Cumulative Number Proportion Proportion R-Square Proportion Proportion Syntax : options ps=100 ls=6 nonumber nodate; title' '; data UN_DAN_RAPORT_NON_UN; input y1 y2 y3 y4 y5 y6 x1 x2 x3 x4 x5 x6 x; datalines;.data ; Title 'Hasil Analisis Korelasi Kanonik Nilai UN DAN Raport Non UN'; proc cancorr redundancy corr data=un_dan_raport_non_un; var y1-y6; with x1-x; run;

19 52 Lampiran 11 Hasil Output Analisis Kanonik NUN dan NUS UN Hasil Analisis Korelasi Kanonik Nilai UN DAN NS UN The CANCORR Procedure Canonical Correlation Analysis Adjusted Approximate Squared Canonical Canonical Standard Canonical Correlation Correlation Error Correlation Eigenvalues of Inv(E)*H = CanRsq/(1-CanRsq) Eigenvalue Difference Proportion Cumulative Test of H0: The canonical correlations in the current row and all that follow are zero Likelihood Approximate Ratio F Value Num DF Den DF Pr > F

20 53 Multivariate Statistics and F Approximations S=6 M=-0.5 N=31 Statistic Value F Value Num DF Den DF Pr > F Wilks' Lambda Pillai's Trace Hotelling-Lawley Trace Roy's Greatest Root NOTE: F Statistic for Roy's Greatest Root is an upper bound. Hasil Analisis Korelasi Kanonik Nilai UN DAN NS UN The CANCORR Procedure Canonical Correlation Analysis Standardized Canonical Coefficients for the VAR Variables V1 V2 V3 V4 V5 V6 y y y y y y Standardized Canonical Coefficients for the WITH Variables W1 W2 W3 W4 W5 W6 x x x x x x