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Andi Megawarni
Universitas Terbuka
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KONSISTENSI KOEFISIEN DETERMINASI SEBAGAI UKURAN KESESUAIAN MODEL PADA REGRESI ROBUST THE CONSISTENCY OF COEFFICIENT OF DETERMINATION TO FITTING MODEL THROUGH ROBUST REGRESSION Harmi Sugiarti; Andi Megawarni
Jurnal Matematika Sains dan Teknologi Vol. 13 No. 2 (2012)
Publisher : LPPM Universitas Terbuka

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Abstract

In statistics, the coefficient of determination can be used to assess the suitability of a model with the data. If there are outliers in the data, the coefficient of determination obtained by the OLS method is not consistent. The purpose of this study was to compare the coefficient of determination of regression lines obtained by the OLS, the M and the LMS methods as a measure of the suitability model. The result showed that when the data contains no-outlier, the LMS method is as consistent as the OLS and the M methods concerning the coefficient of determinations. When the data contain outliers, the LMS method is more consistent than the OLS and the M methods. This result was based on real data with 9.1% outliers. Dalam statistik, koefisien determinasi dapat digunakan untuk menilai kesesuaian model dengan data. Jika ada outlier pada data, koefisien determinasi yang diperoleh dengan metode OLS tidak konsisten. Tujuan dari penelitian ini adalah untuk membandingkan koefisien determinasi dari garis regresi yang diperoleh melalui metode OLS, M dan metode LMS sebagai ukuran model kesesuaian. Hasil penelitian menunjukkan bahwa ketika data tidak mengandung-outlier, metode LMS adalah konsisten, serupa dengan metode OLS dan metode M terkait dengan koefisien determinasi. Ketika data mengandung outlier, metode LMS lebih konsisten daripada metode OLS dan metode M. Hasil ini berdasarkan ujicoba pada data nyata dengan outlier 9,1%.
TINGKAT EFISIENSI PENAKSIR M TERHADAP PENAKSIR LMS DALAM MENAKSIR KOEFISIEN GARIS REGRESI Harmi Sugiarti; Andi Megawarni
Jurnal Matematika Sains dan Teknologi Vol. 11 No. 2 (2010)
Publisher : LPPM Universitas Terbuka

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Abstract

The using of OLS method to estimate the regression coefficients in multiple linear regression model presupposed assumption that there is no outlier in the data. Alternatively, robust regression methods can be used. This paper aims to investigate the efficiency of M method and LMS method to estimate the regression coefficients. Besides application data, simulation data generated by MINITAB and SYSTAT package program were used. The investigation shows the LMS method is more efficient than the M method when there is outlier in the data. Otherwise, the M method is more efficient than the LMS method.
Tingkat Efisiensi Metode Regresi Robust Dalam Menaksir Koefisien Garis Regresi Jika Ragam Galat Tidak Homogen Harmi Sugiarti; Andi Megawarni
Jurnal Matematika Sains dan Teknologi Vol. 6 No. 1 (2005)
Publisher : LPPM Universitas Terbuka

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Abstract

This paper aims to compare the relative efficiency of weighted least square (WLS), ordinary least square (OLS) and robust regression method in regression coefficient estimation when the error term is not homogen. The assumption of homegeneous error variance underlying the ordinary least square (OLS) is very important to get the best linear unbiased estimation of the regression coefficients. The investigation compares the methods in calculating efficiency of booth simulation and experimental data. In conclusion, the WLS method is relatively more efficient than OLS and Robust Regression methods.
Penggunaan Metode Regresi Robust untuk Mencari Selang Kepercayaan Koefisien Garis Regresi jika Ragam Galat Tidak Homogen Harmi Sugiarti; Andi Megawarni
Jurnal Matematika Sains dan Teknologi Vol. 4 No. 2 (2003)
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33830/jmst.v4i2.909.2003

Abstract

The assumption of homogeneous error varianceunderlying the OLS method is very important to get the bestlinear unbiased estimation of the regression coefficients. Thispaper aims to compare the width of confidence interval which isresulted by the OLS, the WLS, and the robust regression methodswhen the error term is not homogen. Comparing the three methodsindicate that the width of confidence interval by the robustregression method is narrower than the OLS method but the WLSmethod is the narrowest.
Co-Authors Sugiarti, Harmi