E jurnal Matematika

8-13

Authors: Ni Wayan Yuni Cahyani, I Gusti Ayu Made Srinadi, Made Susilawati

Ordinary least square (OLS) is a method that can be used to estimate the parameter in linear regression analysis. There are some assumption which should be satisfied on OLS, one of this assumption is homoscedasticity, that is the variance of error is constant. If variance of the error is unequal that so-called heteroscedasticity. The presence heteroscedasticity can cause estimation with OLS becomes inefficient. Therefore, heteroscedasticity shall be overcome. There are some method that can used to overcome heteroscedasticity, two among those are Box-Cox power transformation and median quantile regression. This research compared Box-Cox power transformation and median quantile regression to overcome heteroscedasticity. Applied Box-Cox power transformation on OLS result

20-25

Authors: Putu Ayu Maziyya, I Komang Gde Sukarsa, Ni Made Asih

In the regression analysis we need a method to estimate parameters to fulfill the BLUE characteristic. There are assumptions that must be fulfilled homoscedasticity one of which is a condition in which the assumption of error variance is constant (same), infraction from the assumptions homoskedasticity called heteroscedasticity. The Consequence of going heteroscedasticity can impact OLS estimators still fulfill the requirements of not biased, but the variant obtained becomes inefficient. So we need a method to solve these problems either by Weighted Least Square (WLS). The purpose of this study is to find out how to overcome heteroscedasticity in regression with WLS. Step of this research was do with the OLS analysis, then testing to see whether there heteroscedasticity problem with BPG method, the next step is to repair the beginning model by way of weighting the data an exact multiplier factor, then re-using the OLS procedure to the data that have been weighted, the last stage is back with a heteroscedasticity test BPG method, so we obtained the model fulfill the assumptions of homoskedasicity. Estimates indicate that the WLS method can resolve the heteroscedasticity, with exact weighting factors based on the distribution pattern of data seen. Keywords: heteroscedasticity, OLS, WLS