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Megawarni, Andi
LPPM Universitas Terbuka
Agriculture, Biological Sciences & Forestry Mathematics Other

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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 Sugiarti, Harmi; Megawarni, Andi
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 Metode Regresi Robust dalam Menaksir Koefisien Garis Regresi Jika Ragam Galat Tidak Homogen Sugiarti, Harmi; Megawarni, Andi
Jurnal Matematika Sains dan Teknologi Vol 6 No 1 (2005)
Publisher : LPPM Universitas Terbuka

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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.
Co-Authors Sugiarti, Harmi