Lampiran 1 Deskripsi Data Primer

L A M P I R A N

53 Lampiran 1 Deskripsi Data Primer No Peubah Keterangan Persentase (%) 1 Jenis Kelamin Perempuan 60.5 laki-laki 3.5 2 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 1.3 4 Anak ke 1 3.5 2 32.5 3 1.4 4. 5 1.3 5 Tinggal dengan ortu Tidak 3. Ya 6.1 6 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 21.1 2 buah 4. 3 buah 1.1 4-6 buah.2 Tidak Punya 53. 1 buah 36. 2-3 buah.2 13 Daya Listrik 00 MW 1. 100 MW 2.6 1200 MW 30.3 1300MW 40. 1500-2200 MW 6.6 33

52 Lampiran 2 Deskripsi Data Sekunder Nilai Mean Variance Minimum Maximum UN IND.33 0.3251.0000.4 UN ING.6033 0.116.000.2 UN MAT.243 0.160.2500.0 UN FIS.31 0.314.0000.5 UN KIM.05 0.2331.5000.5 UN BIO.430 0.24 6.5000. NR IND 0.432 4.326 6.500 5.5 NR ING.63 6.04 2.500 6.5 NR MAT.155 4.14 5.000 3.1 NR FIS.22 6.363 3.000 4 NR KIM. 2.42 5.330 2.33 NR BIO 5.4 2.626 3.10 0.5 NR AGM.140 5.46 3.330 4.5 NR PKN 0.053 3.23 6.10 4.5 NR SEJ 6.55 3.366 3.330 1.5 NR SRP 4.041.00 5.330.5 NR PJK 6.1 0.56 4.60.5 NR TIK 0.4 2.5.330 4.6 NR BAJ 0.55.24 6.000 NUS IND.425 0.035.100. NUS ING.5204 0.05.0100.03 NUS MAT.434 0.060.1100. NUS FIS.360 0.0306.000.6 NUS KIM.421 0.0164.2000. NUS BIO.415 0.052.0000.

53 Lampiran 3 Boxplot Data Sekunder 10.0 Boxplot of UN IND, UN ING, UN MAT, UN FIS, UN KIM.5.0 Data.5.0.5.0 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 0 6 4 2 RT IND RT ING RT MAT RT FIS RT KIM RT BIO

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.6.4.2.0 NUS IND NUS ING NUS MAT NUS FIS NUS KIM NUS BIO

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 6 0 4 5 0 5 5 1 5 0 5 5 1 UN IND*RT BIO UN ING*RT IND UN ING*RT ING UN ING*RT MAT UN ING*RT FIS.0.0.0.0.5.5.5.5.0.0.0.0 5.0.5 0.0 6 0 4 5 0 5 5 1 5 0 5 UN ING*RT KIM UN ING*RT BIO UN MAT*RT IND UN MAT*RT ING UN MAT*RT MAT.0.0.5.5.0.0 5 1 5.0.5 0.0 6 0 4 UN MAT*RT FIS UN MAT*RT KIM UN MAT*RT BIO 5 0 5 5 1 5 0 5 5 1 5.0.5 0.0 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 6 0 4 5 0 5 5 1 5 0 5 5 1 UN FIS*RT BIO UN KIM*RT IND UN KIM*RT ING UN KIM*RT MAT UN KIM*RT FIS.6.6.6.6 5.0.5 0.0..0 6 0 4....0.0.0 5 0 5 5 1 5 0 5 UN KIM*RT KIM UN KIM*RT BIO UN BIO*RT IND UN BIO*RT ING UN BIO*RT MAT.6.6...0.0 5 1 5.0.5 0.0 6 0 4 5 0 5 5 1 UN BIO*RT FIS UN BIO*RT KIM UN BIO*RT BIO 5 0 5 5 1 5.0.5 0.0 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.0 5 0 5 5 0 5 5.0.5 0.0 5 0 5 5.5.0.5 UN IND*RT TIK UN IND*RT BAJ UN ING*RT AGM UN ING*RT PKN UN ING*RT SEJ.0.0.0.5.5.5.0.0.0 1 4 5 0 5 5 0 5 5 0 5 5.0.5 0.0 UN ING*RT SRP UN ING*RT PJK UN ING*RT TIK UN ING*RT BAJ UN MAT*RT AGM.0.0.0.5.5.5.5.0.0.0.0 5 0 5 5.5.0.5 1 4 5 0 5 5 0 5 UN MAT*RT PKN UN MAT*RT SEJ UN MAT*RT SRP UN MAT*RT PJK UN MAT*RT TIK 5 0 5 5.0.5 0.0 5 0 5 5.5.0.5 1 4

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 5 0 5.1.4..0.5.0.0.5.0.0.4. UN FIS*NUS KIM UN FIS*NUS BIO UN KIM*NUS IND UN KIM*NUS ING UN KIM*NUS MAT.6.6.6....0.0.0.20.45.0.0.5.0.1.4..0.5.0.0.5.0.6 UN KIM*NUS FIS.6 UN KIM*NUS KIM.6 UN KIM*NUS BIO UN BIO*NUS IND UN BIO*NUS ING....0.0.0.0.4..20.45.0.0.5.0.1.4..0.5.0 UN BIO*NUS MAT UN BIO*NUS FIS UN BIO*NUS KIM UN BIO*NUS BIO.0.5.0.0.4..20.45.0.0.5.0 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.1.4..0.5.0.0.5.0.0.4..20.45.0 UN FIS*NUS BIO UN KIM*NUS IND UN KIM*NUS ING UN KIM*NUS MAT UN KIM*NUS FIS.6.6.6.6..0..0.0.5.0.1.4..0.5.0...0.0.0.5.0.0.4. UN KIM*NUS KIM UN KIM*NUS BIO UN BIO*NUS IND UN BIO*NUS ING UN BIO*NUS MAT.6.6...0.0.20.45.0.0.5.0.1.4..0.5.0.0.5.0 UN BIO*NUS FIS UN BIO*NUS KIM UN BIO*NUS BIO.0.4..20.45.0.0.5.0

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 4.30606 54.55163 0.5241 BETA2HAT KAPPA2 PVALKURT 45.50623-1.1005 0.265145 Karena (pvalskewness = 0.5241) > (α = 0.05) maka terima H Karena (pvalkurtosis = 0.265145) > (α = 0.05) maka terima H Jadi nilai UN berdistribusi Normal (Khatree, 1) 0 0

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 5.2533 6.11463 0.146 BETA2HAT KAPPA2 PVALKURT 4.05646-0.415 0.646632 Karena (pvalskewness = 0.146) > (α = 0.05) maka terima H Karena (pvalkurtosis = 0.646632) > (α = 0.05) maka terima H Jadi nilai Raport UN berdistribusi Normal (Khatree, 1) 0 0

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.322614 2.5365 0.24062 BETA2HAT KAPPA2 PVALKURT 63.444 0.161 0.52303 Karena (pvalskewness = 0.24062) > (α = 0.05) maka terima H Karena (pvalkurtosis = 0.52303) > (α = 0.05) maka terima H 0 Jadi nilai Raport Non UN berdistribusi Normal (Khatree, 1) 0

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 5.5364 0.26 0.0466 BETA2HAT KAPPA2 PVALKURT 45.345-1.155 0.23155 Interpretasi: Karena (pvalskewness = 0.0466) > (α = 0.05) maka terima H Karena (pvalkurtosis = 0.23155) > (α = 0.05) maka terima H 0 Jadi nilai NS UN berdistribusi Normal (Khatree, 1) 0

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 1 0.564246 0.4504 0.00 0.3134 2 0.4450 0.40365 0.041 0.22515 3 0.25125 0.06146 0.105413 0.00 4 0.246051. 0.104 0.060541 5 0.220313. 0.1065 0.0453 6 0.05026. 0.11506 0.00344 Eigenvalues of Inv(E)*H = CanRsq/(1-CanRsq) Eigenvalue Difference Proportion Cumulative 1 0.461 0.165 0.405 0.405 2 0.206 0.152 0.2 0.5 3 0.054 0.0310 0.02 0.6 4 0.0644 0.0134 0.0663 0.43 5 0.0510 0.045 0.0525 0.64 6 0.0035 0.0036 1.0000 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 1 0.42443 1.6 36 23. 0.0120 2 0.6300332 1.2 25 242. 0.165 3 0.131625 0. 16 202.2 0.56 4 0.04536 0. 163.21 0.541 5 0.41436 0.2 4 136 0.455 6 0.65156 0.24 1 6 0.624

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 0.42443 1.6 36 23. 0.0120 Pillai's Trace 0.431266 1.63 36 414 0.0145 Hotelling-Lawley Trace 0.202552 1.6 36 14.1 0.013 Roy's Greatest Root 0.460010 5.3 6 6 0.0001 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 y1 0.3205-0.44 0.600-0.1643 0.44-0.424 y2 0.430 0.212-0.4303 0.3511 0.361 0.54 y3 0.42-0.23 0.401 0.412-0.512 0.40 y4-0.064-0.21-0.01 0.550 0.426-0.2260 y5 0.402-0.216-0.4012 0.2430-0.420-0.33 y6-0.051 0.1 0.46 0.10 0.1314-0.306 Standardized Canonical Coefficients for the WITH Variables W1 W2 W3 W4 W5 W6 x1 0.0121 0.31 0.545-0.0444-0.106-1.025 x2 0.30-0.25-0.32-0.0465 0.53 0.104 x3-0.440-1.200 0.342 0.131 0.360 0.124 x4 0.336 0.154 0.14 1.162-0.6016 0.1114 x5-0.60 0.542-0.5016 0.00 0.100 0.314 x6 0.1562 0.401 0.636-0.061-0.102 0.4650

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 0.0544 0.00 0.104 0.116 0.1262 0.1266 1 0.110 0.110 0.314 0.0544 2 0.161 0.332 0.2252 0.0365 3 0.154 0.423 0.01 0.013 4 0.2115 0.03 0.0605 0.012 5 0.12 0.20 0.045 0.00 6 0.110 1.0000 0.0035 0.0004 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 0.0323 0.0511 0.00 0.042 0.04 0.0 1 0.1014 0.1014 0.314 0.0323 2 0.035 0.14 0.2252 0.01 3 0.24 0.423 0.01 0.025 4 0.111 0.6014 0.0605 0.002 5 0.21 0.32 0.045 0.0132 6 0.126 1.0000 0.0035 0.0004

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 1 2 3 4 5 6 y1 0.0 0.110 0.1252 0.1253 0.1445 0.1446 y2 0.1004 0.1066 0.11 0.1261 0.1423 0.1425 y3 0.0030 0.016 0.042 0.0622 0.032 0.035 y4 0.0 0.064 0.065 0.1316 0.135 0.135 y5 0.0632 0.0633 0.03 0.016 0.02 0.043 y6 0.0022 0.150 0.162 0.16 0.16 0.161 Squared Multiple Correlations Between the WITH Variables and the First M Canonical Variables of the VAR Variables M 1 2 3 4 5 6 x1 0.0015 0.013 0.0441 0.0453 0.055 0.050 x2 0.141 0.141 0.144 0.145 0.102 0.102 x3 0.0130 0.0335 0.022 0.06 0.024 0.025 x4 0.0121 0.021 0.0535 0.021 0.026 0.030 x5 0.014 0.061 0.063 0.030 0.0 0.0 x6 0.0106 0.024 0.002 0.023 0.03 0.045 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;

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 1 0.423601 0.230226 0.0450 0.143 2 0.34532 0.22445 0.10166 0.11530 3 0.15313. 0.11121 0.03035 4 0.15153. 0.1121 0.022 5 0.133. 0.11321 0.013 6 0.104253. 0.114215 0.0106 Eigenvalues of Inv(E)*H = CanRsq/(1-CanRsq) Eigenvalue Difference Proportion Cumulative 1 0.21 0.02 0.41 0.41 2 0.135 0.1040 0.306 0.05 3 0.031 0.002 0.021 0. 4 0.0235 0.0043 0.0535 0.313 5 0.012 0.002 0.043 0.50 6 0.0110 0.0250 1.0000 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 1 0.66361 0.65 42 2.5 0.54 2 0.0121 0.4 30 25 0.2 3 0.10234 0.2 20 216.53 0.2 4 0.416514 0.30 12 14.1 0.4 5 0.0463 0.34 6 134 0.161 6 0.1312 0.3 2 6 0.6

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 0.66361 0.65 42 2.5 0.54 Pillai's Trace 0.3242160 0.66 42 40 0.46 Hotelling-Lawley Trace 0.43500 0.65 42 13.23 0.524 Roy's Greatest Root 0.216650 2.12 6 0.0525 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 0.0331 0.0515 0.055 0.056 0.0636 0.0652 1 0.146 0.146 0.14 0.0331 2 0.1535 0.332 0.115 0.014 3 0.144 0.42 0.030 0.0044 4 0.1611 0.643 0.0230 0.003 5 0.211 0.55 0.01 0.0040 6 0.1443 1.0000 0.010 0.0016

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 0.033 0.0511 0.0545 0.050 0.060 0.061 1 0.16 0.16 0.14 0.033 2 0.145 0.3335 0.115 0.014 3 0.1116 0.4451 0.030 0.0034 4 0.1524 0.56 0.0230 0.0035 5 0.14 0.455 0.01 0.002 6 0.01 0.236 0.010 0.000 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;

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 1 0.553405 0.3352 0.00106 0.30625 2 0.524422. 0.0314 0.2501 3 0.4624. 0.001 0.2152 4 0.25526 0.1511 0.1063 0.06354 5 0.06604. 0.11432 0.00332 6 0.0156. 0.115434 0.00030 Eigenvalues of Inv(E)*H = CanRsq/(1-CanRsq) Eigenvalue Difference Proportion Cumulative 1 0.4415 0.0621 0.331 0.331 2 0.33 0.00 0.3206 0.63 3 0.203 0.201 0.230 0.30 4 0.022 0.062 0.0610 0.1 5 0.004 0.001 0.000 0. 6 0.0003 0.0003 1.0000 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 1 0.362350 2.05 36 23. 0.000 2 0.5230161 1.5 25 242. 0.00 3 0.2142035 1.43 16 202.2 0.1321 4 0.236540 0.60 163.21 0.45 5 0.03611 0.1 4 136 0.55 6 0.6142 0.02 1 6 0.44

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 0.362350 2.05 36 23. 0.000 Pillai's Trace 0.22351 1. 36 414 0.0010 Hotelling-Lawley Trace 1.13016 2.06 36 14.1 0.0011 Roy's Greatest Root 0.4414523 5.0 6 6 0.0002 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 y1 0.4142 0.0041-0.462 0.345-0.3231 0.4 y2-0.106 0.5164-0.516-0.65 0.30-0.4204 y3 0.350-0.145 0.1621-0.3 0.233 0.3412 y4-0.330 0.10 0.233 0.56 0.0630 0.206 y5 0.230-0.425-0.201 0.362 0.450-0.316 y6 0.25 0.4250 0.306-0.0102-0.32-0.203 Standardized Canonical Coefficients for the WITH Variables W1 W2 W3 W4 W5 W6 x1 0.513 0.11 0.1354 0.401-0.3616-0.163 x2 0.241 0.2433-0.444-0.0322 0.5232-0.24 x3 0.520-0.35 0.54-0.511 0.14-0.6233 x4 0.541-0.365-0.114 0.1063-0.5245 0.21 x5-0.325 0.63 0.1505-0.514 0.1443 0.46 x6-0.42-0.104 0.2643 0.540 0.33 0.253