Lampiran 1 Deskripsi Data Primer

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33

Lampiran 1 Deskripsi Data Primer

No Peubah Keterangan Persentase (%)

1 Jenis Kelamin Perempuan 60.5

laki-laki 39.5 2 Pendidikan Ayah SD,SMP 5.30 SMA,STM,SMK 65.8 D3,Sarjana Muda, S1,S2 27.6 tidak ada 1.3 Pendidikan Ibu SD,SMP 11.8 SMA,STM,SMK 64.5 D3,Sarjana Muda, S1,S2 23.7 tidak ada 0

3 Pekerjaan Ayah PNS,BUMN 23.7

Swasta,wiraswasta 52.6

TNI,Polri 18.4

Petani/Pelaut 3.9

Pekerjaan Ibu Ibu rumah tangga 73.7

PNS,Guru 18.4 Wiraswasta 6.6 Dokter 1.3 4 Anak ke 1 39.5 2 32.5 3 18.4 4 7.9 5 1.3

5 Tinggal dengan ortu Tidak 3.9

Ya 96.1

6 Ortu cerai / wafat Tidak 94.7

Ya 5.3 7 Riwayat penyakit berat Ada 1.3 Tidak ada 85.5 8 Status kepemilikan Rumah

bukan milik pribadi 10.5

milik pribadi 89.5

9 Ikut Bimbel Tidak 25

Ya 75

10 Internet di Rumah Tidak 11.8

Ya 88.2 11 Banyak Kepemilikan Sepeda motor Tidak punya 3.9 1 buah 21.1 2 buah 48.7 3 buah 17.1 4-6 buah 9.2 12 Banyak Kepemilikan Mobil Tidak Punya 53.9 1 buah 36.8 2- 3 buah 9.2 13 Daya Listrik 900 MW 19.7 100 MW 2.6 1200 MW 30.3 1300MW 40.8 1500-2200 MW 6.6

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Lampiran 2 Deskripsi Data Sekunder

Nilai _Mean _Variance _Minimum _Maximum

UN IND 8.3389 0.3251 7.0000 9.4 UN ING 8.6033 0.1196 7.8000 9.2 UN MAT 8.2743 0.1860 7.2500 9.0 UN FIS 8.3871 0.3147 7.0000 9.75 UN KIM 8.8095 0.2331 7.5000 9.5 UN BIO 7.8430 0.2487 6.5000 8.9 NR IND 80.432 4.326 76.500 85.5 NR ING 78.639 6.094 72.500 86.5 NR MAT 78.155 4.814 75.000 83.17 NR FIS 78.228 6.363 73.000 84 NR KIM 77.778 2.492 75.330 82.33 NR BIO 75.849 2.626 73.170 80.5 NR AGM 78.140 5.468 73.330 84.5 NR PKN 80.053 3.238 76.170 84.5 NR SEJ 76.595 3.366 73.330 81.5 NR SRP 84.041 7.800 75.330 88.5 NR PJK 86.971 0.569 84.670 88.5 NR TIK 80.847 2.598 77.330 84.67 NR BAJ 80.559 7.247 76.000 88 NUS IND 8.4725 0.0357 8.1800 8.88 NUS ING 8.5204 0.0579 8.0100 9.03 NUS MAT 8.4374 0.0608 8.1100 8.97 NUS FIS 8.3607 0.0306 8.0700 8.86 NUS KIM 8.4291 0.0164 8.2000 8.78 NUS BIO 8.4158 0.0527 8.0000 8.98

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Lampiran 3 Boxplot Data Sekunder

UN KIM UN FIS UN MAT UN ING UN IND 10.0 9.5 9.0 8.5 8.0 7.5 7.0 D at a

Boxplot of UN IND, UN ING, UN MAT, UN FIS, UN KIM

RT BIO RT KIM RT FIS RT MAT RT ING RT IND 86 84 82 80 78 76 74 72 D at a

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RT BAJ RT TIK RT PJK RT SRP RT SEJ RT PKN RT AGM 90 85 80 75 D at a

Boxplot of NR AGM, NR PKN, NR SEJ, NR SRP, NR PJK, NR TIK, NR BAJ

NUS BIO NUS KIM NUS FIS NUS MAT NUS ING NUS IND 9.0 8.8 8.6 8.4 8.2 8.0 D at a

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Lampiran 4 Hasil Uji kelinieran

84 80 76 9 8 7 85 80 75 9 8 7 81 78 75 9 8 7 85 80 75 9 8 7 81 78 75 9 8 7 80.0 77.5 75.0 9 8 7 84 80 76 9.0 8.5 8.0 85 80 75 9.0 8.5 8.0 81 78 75 9.0 8.5 8.0 85 80 75 9.0 8.5 8.0 81 78 75 9.0 8.5 8.0 80.0 77.5 75.0 9.0 8.5 8.0 84 80 76 9 8 7 85 80 75 9 8 7 81 78 75 9 8 7 85 80 75 9 8 7 81 78 75 9 8 7 80.0 77.5 75.0 9 8 7

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 IND vs RT IND, UN IND vs RT ING, UN IND vs RT MAT, U

84 80 76 9 8 7 85 80 75 9 8 7 81 78 75 9 8 7 85 80 75 9 8 7 81 78 75 9 8 7 80.0 77.5 75.0 9 8 7 84 80 76 9.6 8.8 8.0 85 80 75 9.6 8.8 8.0 81 78 75 9.6 8.8 8.0 85 80 75 9.6 8.8 8.0 81 78 75 9.6 8.8 8.0 80.0 77.5 75.0 9.6 8.8 8.0 84 80 76 9 8 7 85 80 75 9 8 7 81 78 75 9 8 7 85 80 75 9 8 7 81 78 75 9 8 7 80.0 77.5 75.0 9 8 7

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 FIS vs RT IND, UN FIS vs RT ING, UN FIS vs RT MAT, U

85 80 75 9 8 7 85 80 75 9 8 7 80.0 77.5 75.0 9 8 7 85 80 75 9 8 7 88.5 87.0 85.5 9 8 7 84 81 78 9 8 7 85 80 75 9 8 7 85 80 75 9.0 8.5 8.0 85 80 75 9.0 8.5 8.0 80.0 77.5 75.0 9.0 8.5 8.0 85 80 75 9.0 8.5 8.0 88.5 87.0 85.5 9.0 8.5 8.0 84 81 78 9.0 8.5 8.0 85 80 75 9.0 8.5 8.0 85 80 75 9 8 7 85 80 75 9 8 7 80.0 77.5 75.0 9 8 7 85 80 75 9 8 7 88.5 87.0 85.5 9 8 7 84 81 78 9 8 7

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

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85 80 75 9 8 7 8.7 8.4 8.1 9 8 7 9.0 8.5 8.0 9 8 7 9.0 8.5 8.0 9 8 7 8.8 8.4 8.0 9 8 7 8.70 8.45 8.20 9 8 7 9.0 8.5 8.0 9 8 7 8.7 8.4 8.1 9.6 8.8 8.0 9.0 8.5 8.0 9.6 8.8 8.0 9.0 8.5 8.0 9.6 8.8 8.0 8.8 8.4 8.0 9.6 8.8 8.0 8.70 8.45 8.20 9.6 8.8 8.0 9.0 8.5 8.0 9.6 8.8 8.0 8.7 8.4 8.1 9 8 7 9.0 8.5 8.0 9 8 7 9.0 8.5 8.0 9 8 7 8.8 8.4 8.0 9 8 7 8.70 8.45 8.20 9 8 7 9.0 8.5 8.0 9 8 7

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 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

Scatterplot of UN MAT vs RT BAJ, UN FIS vs NUS IND, UN FIS vs NUS ING,

8.7 8.4 8.1 9 8 7 9.0 8.5 8.0 9 8 7 9.0 8.5 8.0 9 8 7 8.8 8.4 8.0 9 8 7 8.70 8.45 8.20 9 8 7 9.0 8.5 8.0 9 8 7 8.7 8.4 8.1 9.6 8.8 8.0 9.0 8.5 8.0 9.6 8.8 8.0 9.0 8.5 8.0 9.6 8.8 8.0 8.8 8.4 8.0 9.6 8.8 8.0 8.70 8.45 8.20 9.6 8.8 8.0 9.0 8.5 8.0 9.6 8.8 8.0 8.7 8.4 8.1 9 8 7 9.0 8.5 8.0 9 8 7 9.0 8.5 8.0 9 8 7 8.8 8.4 8.0 9 8 7 8.70 8.45 8.20 9 8 7 9.0 8.5 8.0 9 8 7

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

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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(8*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’ BETA1HAT KAPPA1 PVALSKEW 4.3067076 54.55163 0.5298419 BETA2HAT KAPPA2 PVALKURT 45.507623 -1.108805 0.2675145 Interpretasi:

Karena (pvalskewness = 0.5298419) > (α = 0.05) maka terima H

Karena (pvalkurtosis = 0.2675145) > (α = 0.05) maka terima H

0

Jadi nilai UN berdistribusi Normal (Khatree, 1999)

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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;

y ={

………Data………

/*Untuk menentukan banyaknya data dan dimensi dari vektornya. Peubah dfchi adalah derajat bebas untuk pendugaan Chi-kuadrat dari skewness (kemenjuluran) multivariate . */

dfchi = p*(p+1)*(p+2)/6 ;

’Output skewness dan kurtosis mardia utk RAPORT UN’

BETA1HAT KAPPA1 PVALSKEW 5.2985339 67.114763 0.1468797 BETA2HAT KAPPA2 PVALKURT 47.056468 -0.419757 0.6746632 Interpretasi:

0

Jadi nilai Raport UN berdistribusi Normal (Khatree, 1999)

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Lampiran 7 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;

y ={

………data………

dfchi = p*(p+1)*(p+2)/6 ;

’Output skewness dan kurtosis mardia utk RAPORT NON UN’

BETA1HAT KAPPA1 PVALSKEW 7.3226814 92.753965 0.240628 BETA2HAT KAPPA2 PVALKURT 63.479447 0.1861797 0.8523039 Interpretasi:

0

Jadi nilai Raport Non UN berdistribusi Normal (Khatree, 1999)

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Lampiran 8 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;

y ={

………data………

dfchi = p*(p+1)*(p+2)/6 ;

’Output skewness dan kurtosis mardia utk NS UN’

BETA1HAT KAPPA1 PVALSKEW 5.5837649 70.727689 0.0889466 BETA2HAT KAPPA2 PVALKURT 45.34858 -1.179559 0.2381755 Interpretasi:

Karena (pvalkurtosis = 0.2381755) > (α = 0.05) maka terima H 0

Jadi nilai NS UN berdistribusi Normal (Khatree, 1999)

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Lampiran 9 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 0.450947 0.078707 0.318374 2 0.474507 0.403965 0.089471 0.225157 3 0.295125 0.069146 0.105413 0.087099 4 0.246051 . 0.108479 0.060541 5 0.220313 . 0.109865 0.048538 6 0.059026 . 0.115068 0.003484 Eigenvalues of Inv(E)*H = CanRsq/(1-CanRsq)

Eigenvalue Difference Proportion Cumulative 1 0.4671 0.1765 0.4805 0.4805 2 0.2906 0.1952 0.2989 0.7795 3 0.0954 0.0310 0.0982 0.8776 4 0.0644 0.0134 0.0663 0.9439 5 0.0510 0.0475 0.0525 0.9964 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 0.42947438 1.67 36 283.8 0.0120 2 0.63007332 1.29 25 242.97 0.1695 3 0.81316257 0.89 16 202.27 0.5867 4 0.89074536 0.88 9 163.21 0.5419 5 0.94814736 0.92 4 136 0.4559 6 0.99651596 0.24 1 69 0.6249

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Multivariate Statistics and F Approximations S=6 M=-0.5 N=31

Statistic Value F Value Num DF Den DF Pr > F Wilks' Lambda 0.42947438 1.67 36 283.8 0.0120 Pillai's Trace 0.74319266 1.63 36 414 0.0145 Hotelling-Lawley Trace 0.97202552 1.69 36 174.19 0.0139

Roy's Greatest Root 0.46708010 5.37 6 69 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

Standardized Canonical Coefficients for the VAR Variables V1 V2 V3 V4 V5 V6 y1 0.3205 0.4487 0.6090 0.1643 0.4994 -0.4724 y2 0.4830 0.2912 -0.4303 0.3511 0.3618 0.7754 y3 0.4287 -0.2399 0.4091 0.4912 -0.5812 0.4089 y4 0.7064 0.2188 0.0771 0.5750 0.4276 -0.2260 y5 0.4029 0.2168 0.4012 0.2430 0.4270 -0.7383 y6 0.0581 0.8178 0.4867 0.1908 0.1314 -0.3096

Standardized Canonical Coefficients for the WITH Variables W1 W2 W3 W4 W5 W6 x1 0.0121 0.3771 0.5458 0.0444 0.1086 -1.0825 x2 0.9930 -0.2975 -0.3298 -0.0465 0.5739 0.1094 x3 -0.4840 -1.2080 0.3942 0.1319 0.3690 0.1824 x4 0.3386 0.1954 0.1947 1.1692 -0.6016 0.1114 x5 -0.6708 0.8542 -0.5016 0.0087 0.7100 0.3147 x6 0.1562 0.4071 0.6386 -0.9061 -0.1702 0.4650

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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 1 0.1710 0.1710 0.3184 0.0544 0.0544 2 0.1619 0.3329 0.2252 0.0365 0.0909 3 0.1594 0.4923 0.0871 0.0139 0.1048 4 0.2115 0.7038 0.0605 0.0128 0.1176 5 0.1782 0.8820 0.0485 0.0087 0.1262 6 0.1180 1.0000 0.0035 0.0004 0.1266

Standardized Variance of the WITH Variables Explained by Their Own The Opposite Canonical Variables Canonical

Variables Canonical

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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 0.0778 0.1107 0.1252 0.1253 0.1445 0.1446 y2 0.1004 0.1066 0.1178 0.1261 0.1423 0.1425 y3 0.0030 0.0176 0.0427 0.0622 0.0732 0.0735 y4 0.0799 0.0964 0.0965 0.1316 0.1358 0.1358 y5 0.0632 0.0633 0.0837 0.0916 0.0929 0.0943 y6 0.0022 0.1508 0.1627 0.1687 0.1688 0.1691

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 0.0137 0.0441 0.0453 0.0558 0.0570 x2 0.1418 0.1419 0.1447 0.1458 0.1702 0.1702 x3 0.0130 0.0335 0.0722 0.0767 0.0924 0.0925 x4 0.0121 0.0271 0.0535 0.0821 0.0826 0.0830 x5 0.0147 0.0619 0.0673 0.0730 0.0997 0.0998 x6 0.0106 0.0284 0.0802 0.0823 0.0837 0.0845 Syntax :

options ps=100 ls=76 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;

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Lampiran 10 Hasil Output Analisis Kanonik NUN dan NR non UN

Hasil Analisis Korelasi Kanonik Nilai UN DAN Raport Non UN The CANCORR Procedure

Adjusted Approximate Squared Canonical Canonical Standard Canonical Correlation Correlation Error Correlation 1 0.423601 0.230226 0.094750 0.179438 2 0.345732 0.224495 0.101668 0.119530 3 0.175313 . 0.111921 0.030735 4 0.151583 . 0.112817 0.022977 5 0.137377 . 0.113291 0.018873 6 0.104253 . 0.114215 0.010869 Eigenvalues of Inv(E)*H = CanRsq/(1-CanRsq)

Ratio F Value Num DF Den DF Pr > F 1 0.66397618 0.65 42 298.95 0.9547 2 0.80917217 0.47 30 258 0.9928 3 0.91902349 0.28 20 216.53 0.9992 4 0.94816514 0.30 12 174.91 0.9894 5 0.97046389 0.34 6 134 0.9161 6 0.98913128 0.37 2 68 0.6897

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Multivariate Statistics and F Approximations S=6 M=0 N=30.5

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

Standardized Variance of the VAR Variables Explained by Their Own The Opposite Canonical Variables Canonical Variables

Canonical

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Standardized Variance of the WITH Variables Explained by Their Own The Opposite Canonical Variables canonical Variables Canonical

Number Proportion Proportion R-Square Proportion Proportion 1 0.1876 0.1876 0.1794 0.0337 0.0337 2 0.1459 0.3335 0.1195 0.0174 0.0511 3 0.1116 0.4451 0.0307 0.0034 0.0545 4 0.1524 0.5976 0.0230 0.0035 0.0580 5 0.1479 0.7455 0.0189 0.0028 0.0608 6 0.0781 0.8236 0.0109 0.0008 0.0617 Syntax :

options ps=100 ls=76 nonumber nodate; title' '; data UN_DAN_RAPORT_NON_UN; input y1 y2 y3 y4 y5 y6 x1 x2 x3 x4 x5 x6 x7; 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-x7;

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Lampiran 11 Hasil Output Analisis Kanonik NUN dan NUS UN

Hasil Analisis Korelasi Kanonik Nilai UN DAN NS UN The CANCORR Procedure

Adjusted Approximate Squared Canonical Canonical Standard Canonical Correlation Correlation Error Correlation 1 0.553405 0.337582 0.080106 0.306258 2 0.524422 . 0.083714 0.275019 3 0.467924 . 0.090188 0.218952 4 0.259526 0.195181 0.107693 0.067354 5 0.096604 . 0.114392 0.009332 6 0.017567 . 0.115434 0.000309 Eigenvalues of Inv(E)*H = CanRsq/(1-CanRsq)

Ratio F Value Num DF Den DF Pr > F 1 0.36283850 2.05 36 283.8 0.0007 2 0.52301617 1.85 25 242.97 0.0099 3 0.72142035 1.43 16 202.27 0.1321 4 0.92365740 0.60 9 163.21 0.7945 5 0.99036191 0.17 4 136 0.9558 6 0.99969142 0.02 1 69 0.8844

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Multivariate Statistics and F Approximations S=6 M=-0.5 N=31

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 -0.4672 0.3457 -0.3231 0.7487 y2 0.1069 0.5164 0.5196 0.6895 0.3980 -0.4204 y3 0.3750 -0.1845 0.1621 -0.3998 0.8233 0.3412 y4 -0.3830 0.7180 0.2733 0.5776 0.0630 0.2067 y5 0.2830 0.4275 0.2017 0.7362 0.4580 -0.3186 y6 0.7275 0.4250 0.3906 0.0102 0.3929 -0.2039

Standardized Canonical Coefficients for the WITH Variables W1 W2 W3 W4 W5 W6 x1 0.5713 0.7181 0.1354 0.4081 0.3616 -0.1693 x2 0.2418 0.2433 0.8444 0.0322 0.5232 -0.2849 x3 0.5280 0.3959 0.5479 0.5171 0.1884 -0.6233 x4 0.5941 -0.3865 -0.1184 0.1063 -0.5245 0.8281 x5 -0.3259 0.6873 0.1505 -0.5814 0.1443 0.4786 x6 -0.4982 -0.1048 0.2643 0.5470 0.8933 0.2853