Judul: Perbandingan Tingkat Kepentingan Peubah dalam Regresi Linear Berganda antara Model Konvensional dan Metode Pratt, Sigit D.S., Rahmatullah
Abstrak: The objective of this research is to compare various methods in determining variable importance in multiple linear regression. In multiple linear regression analysis, it is of interest to rank order and scale predictors in terms of their importance to dependent variable. In one conventional method, variables are ranked based on their standardized coefficients obtained from regression model, which is regarded as their relative importance. In other method, partial correlation between the predictors and dependent variable is used as a measure of variable importance. Pratt in 1987 proposed different method to determine variable importance within several assumptions that the variables have to met. Specifically, this method gives an estimation to the importance measure. This research reviews three methods, namely the standardized coefficient, partial correlation, and Pratt method. Two sets of data with specific variable importance parameters were generated. One set was generated under multicollinearity condition, while the other was without it. Three kind of regression models were constructed from the data with each contained two, five and eight variables, respectively. Each model was then simulated in 50 turns, in each of which importance measures were obtained using the three methods. The measures' averages were then compared to the importance parameters to determine their performance. The result shows that under no multicollinearity condition Pratt method gives better estimate to variable importance parameter in regression model with 5 and 8 variables, compared to conventional methods. Meanwhile, under multicollinearity condition the three methods performance are about the same. Pratt method also consistently produces smaller variance than the other two, indicating that this method is more stable in measuring the variable importance.
Keyword: Pratt method, multicollinearity data generation, simulation, multiple linear regression.

Judul: Kajian Perbandingan Metode Least Median of Squares dan Maximum Likelihood Type pada Regresi Kekar
Abstrak: Keberadaan pencilan dapat memengaruhi pendugaan parameter dalam analisis regresi yang menggunakan metode ordinary least squares (OLS). Metode regresi kekar dapat digunakan sebagai pendekatan alternatif dari OLS. Penelitian ini dilakukan untuk mengkaji perbandingan dari dua metode regresi kekar, yaitu least median of squares (LMS) dan maximum likelihood type (M) melalui data simulasi. Data simulasi diperoleh dengan membangkitkan bilangan acak berdasarkan model regresi linear berganda dua peubah penjelas. Kombinasi yang digunakan adalah jenis pencilan, proporsi pencilan, dan ukuran contoh. Berdasarkan nilai bias mutlak dan kuadrat tengah galat (KTG) diperoleh bahwa metode M lebih tepat diterapkan ketika data mengandung vertical outlier atau good leverage point, lalu diikuti oleh metode LMS. Sementara itu, metode LMS dapat menduga parameter regresi pada data yang mengandung bad leverage point. Analisis selanjutnya dilakukan pada data pertanian mengenai pengaruh pupuk organik dan konsumsi beras per kapita terhadap produksi padi sawah di 34 provinsi tahun 2017. Hasil yang diperoleh menunjukkan bahwa metode LMS menghasilkan nilai bias mutlak dan KTG terkecil., The presence of outliers can be affect to estimate the parameters of the linear regression using ordinary least squares (OLS). The robust regression methods can be used as an alternative to OLS. This study aims to compare the performance of two methods, namely least median of squares (LMS) and maximum likelihood type (M) using simulation study. The simulation data obtained by generating random number based on multiple linear regression with two explanatory variables. With combination of the sample size, outliers type, and the number of outliers. Based on the criteria: absoulut bias and mean square error (MSE), the M-estimator gave better result than the LMS-estimator when the data contains vertical outliers or good leverage point. Meanwhile, the LMS-estimator gave better result at data containing bad leverage points. The next step is analysed data application for agriculture on the affect of organic fertilizer and rice consumption per capita against the use of rice production in 34 provinces in 2017 using the best method based on data characteristic. The results from LMS-estimator produce the smallest absolut bias and MSE.
Keyword: LMS, M, OLS, Outlier, Robust Regression

Judul: Embriogenesis ikan buta (Astyanax fasciatus)
Abstrak: Ikan buta Astyanax fasciatus adalah spesies ikan unik dari famili Characidae yang menempati perairan dalam gua di Wilayah La Cueva Chica-San Luis Potasi-Mexico. Ikan ini memiliki mata yang tereduksi (Blind Cavefish) dan warna kulit merah muda, pucat serta mengkilap.
Keyword: