Lampiran 1. Data IPM, Rata-rata Lama Sekolah, UMR, dan PDRB

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1 Lampiran 1. Data IPM, Rata-rata Lama Sekolah, UMR, dan PDRB No. Rata-rata Lama Sekolah (Tahun) Provinsi IPM (%) UMR (Rupiah) 1 Aceh 68,81 9, ,4 2 Sumatra Utara 68,87 9, ,8 3 Sumatra Barat 69,36 8, Riau 70,33 8, ,7 5 Jambi 68,24 8, ,9 6 Sumatra Selatan 66,75 8, ,4 7 Bengkulu 68,06 8, ,2 8 Lampung 66,42 7, Bangka Belitung 68,27 7, ,4 10 Riau 73,4 9, ,9 11 DKI Jakarta 78,39 10, ,4 12 Jawa Barat 68,8 8, ,3 13 Jawa Tengah 68,78 7, ,7 14 DI Yogyakarta 76,81 9, ,4 15 Jawa Timur 68,14 7, ,7 16 Banten 69,89 8, ,8 17 Bali 72,48 8, ,4 18 NTB 64,31 7, ,2 19 NTT 62,26 7, ,6 20 Kalimantan Barat 64,89 7, ,9 21 Kalimantan Tengah 67,77 8, ,9 22 Kalimantan Selatan 67,63 8, ,6 23 Kalimantan Timur 73,82 9, Sulawesi Utara 69,96 9, ,6 25 Sulawesi Tengah 66,43 8, ,3 26 Sulawesi Selatan 68,49 8, ,1 27 Sulawesi Tenggara 68,07 8, ,6 28 Gorontalo 65,17 7, ,2 29 Sulawesi Barat 62,24 7, ,4 30 Maluku 66,74 9, ,7 31 Maluku Utara 65,18 8, ,1 32 Papua Barat 61,28 9, ,3 33 Papua 56,75 6, ,2 Sumber: Badan Pusat Statistik PDRB (Milyar) 66

2 Lampiran 2. Syntax Software Program R Metode Kuadrat Terkecil Dataset<-readdelim( C:/Robust ) IPM=Dataset$IPM Rata2lamasekolah=Dataset$Rata2lamasekolah UMR=Dataset$UMR PDRB=Dataset$PDRB x<-cbind(rata2lamasekolah, UMR, PDRB) y<-cbind(ipm) mkt<-lm(y~x) summary(mkt) plot(mkt, caption= Residual vs Fitted Values ) sr<-rstandard(mkt) pencilan1 <-cbind ("sampel" = 1:33, "standardized residual" = sr, "pencilan (0=tidak,1=ya) " = sr < (-35) sr > 35) pencilan1 cd<-cooksdistance(mkt) pencilan2<-cbind( sampel =1:33, cook s distance =cd, pencilan(0=tidak, 1=ya) =cd>4/33) pencilan2 Estimasi-S dengan Pembobot Welsch library(robustbase) xdan1<-cbind(1,x) control=lmrobcontrol(psi="welsh") s<-lmrobs(x=xdan1,y=y,control) summary(s) s["coefficients"] s["scale"] s["kiter"] s["converged"] 67

3 s["residuals"] s["rweights"] Estimasi-S dengan Pembobot Tukey bisquare library(robustbase) xdan1<-cbind(1,x) control=lmrobcontrol(psi="tukey") s<-lmrobs(x=xdan1,y=y,control) summary(s) s["coefficients"] s["scale"] s["kiter"] s["converged"] s["residuals"] s["rweights"] 68

4 Lampiran 3. Output Estimasi Parameter, Nilai Standard Error dan Adjusted R-Square Metode Kuadrat Terkecil (dengan outlier) Output menggunakan software program R Output menggunakan software SPSS Coefficients a Model Unstandardized Coefficients Standardized Coefficients B Std Error Beta t Sig (Constant) 40,964 4,619 8,869,000 1 Rata-rata Lama Sekolah 3,422,589,721 5,809,000 UMR -1,887E-006,000 -,146-1,195,242 PDRB,003,001,300 2,605,014 a Dependent Variable: IPM Model Summary Model R R Square Adjusted R Square Std Error of the Estimate 1,793 a,628,590 2,

5 Metode Kuadrat Terkecil (tanpa outlier) Output menggunakan software program R Output menggunakan software SPSS Coefficients a Model Unstandardized Coefficients Standardized t Sig Coefficients B Std Error Beta (Constant) 45,122 3,160 14,281,000 1 Rata-rata Lama Sekolah 2,188,470,545 4,653,000 UMR 2,319E-006,000,225 1,971,060 PDRB,003,001,434 4,716,000 a Dependent Variable: IPM Model Summary Model R R Square Adjusted R Square Std Error of the Estimate 1,895 a,801,777 1,55138 a Predictors: (Constant), PDRB, UMR, Rata-rata Lama Sekolah 70

6 Lampiran 4. Output Estimasi-S dengan Pembobot Welsch Nilai Residual dan Residual Terboboti Output menggunakan software program R Koefisien Estimasi Parameter, Nilai Standard Error dan Adjusted R-Square Output menggunakan software SPSS Coefficients a,b Model Unstandardized Coefficients Standardized Coefficients B Std Error Beta t Sig (Constant) 45,179 1,696 26,634,000 1 Rata-rata Lama Sekolah 2,241,266,571 8,412,000 UMR 2,215E-006,000,248 3,870,001 PDRB,002,000,414 8,604,000 a Dependent Variable: IPM b Weighted Least Squares Regression - Weighted by Welsch Model Summary b,c Model R R Square Adjusted R Square Std Error of the Estimate 1,971 a,943,937,57534 a Predictors: (Constant), PDRB, UMR, Rata-rata Lama Sekolah b Dependent Variable: IPM c Weighted Least Squares Regression - Weighted by Welsch 71

7 Nilai Skala Robust dan Iterasi Output menggunakan software program R 72

8 Lampiran 5. Output Estimasi-S dengan Pembobot Tukey bisquare Nilai Residual dan Residual Terboboti Output menggunakan software program R Koefisien Estimasi Parameter, Nilai Standard Error dan Adjusted R-Square Output menggunakan software SPSS Coefficients a,b Model Unstandardized Coefficients Standardized Coefficients B Std Error Beta t Sig (Constant) 43,975 2,102 20,922,000 1 Rata-rata Lama Sekolah 2,445,327,617 7,479,000 UMR 1,914E-006,000,210 2,685,013 PDRB,002,000,385 6,439,000 a Dependent Variable: IPM b Weighted Least Squares Regression - Weighted by Tukey bisquare Model Summary b,c Model R R Square Adjusted R Square Std Error of the Estimate 1,964 a,929,920,74923 a Predictors: (Constant), PDRB, UMR, Rata-rata Lama Sekolah b Dependent Variable: IPM c Weighted Least Squares Regression - Weighted by Tukey bisquare 73

9 Nilai Skala Robust dan Iterasi Output menggunakan software program R 74

10 Lampiran 6. Daftar Tabel Tabel Durbin-Watson dengan Taraf Signifikansi α = 5% 75

11 Tabel-F dengan Taraf Signifikansi 76

12 Tabel-t dengan Taraf Signifikansi 77

13 Tabel Kolmogorov-Smirnov Ukuran sampel (n) Tingkat Signifikansi untuk ( ) ( ) Over 35 78