%0 Generic
%A SHERLINA YULIANTI , sherlinay65@gmail.com
%C UNIVERSITAS LAMPUNG
%D 2023
%F eprints:69534
%I FAKULTAS PERTANIAN  
%T ANALISIS REGRESI RIDGE GENERALIZED LEAST SQUARE (RGLS)  UNTUK MENGATASI MULTIKOLINEARITAS DAN AUTOKORELASI
%U http://digilib.unila.ac.id/69534/
%X ABSTRACT  RIDGE GENERALIZED LEAST SQUARE (RGLS) REGRESSION  ANALYSIS TO OVERCOME MULTICOLLINEARITY AND  AUTOCORRELATION  By  Sherlina Yulianti  Multiple linear regression analysis is a method for analyzing the relationship  between the dependent variable and several independent variables. The  independent variables in multiple linear regression analysis have a possibility to  correlate with each other or called multicollinearity problems and linear  regression using time series data has a possibility to have error autocorrelation  problems because the data at this time has a relationship with data at the previous  time. Both of these conditions have an adverse effect on estimates and  predictions. The Ridge Generalized Least Square (RGLS) method is able to  overcome multicollinearity and autocorrelation problems simultaneously.  Therefore this study aims to study the performance of the RGLS method in  overcoming multicollinearity and autocorrelation problems through Monte Carlo  simulations with n = 50, 75, and 100 which have 6 independent variables with a  multicollinearity level of 0.99 and an autocorrelation level of 0.15. This study  gives the result that the RGLS method is able to overcome multicollinearity and  autocorrelation problems in the data and it can be concluded that the more the  number of samples used, the smaller the resulting MSE value and the greater the  resulting