| Summary: | The core of the linear regression model is to find the values of the
coefficient estimator explanatory variables on the dependent variable so as to
provide the error value as small as possible. There are many methods that have
been studied including the popular classical method called OLS as well as
iterative methods such as WLS, robust can be used to determine estimator in the
regression model. However, when there is multicollinearity among the
explanatory variables, using these methods, the regression coefficient becomes
more unstable. Therefore, in this paper the stepwise regression method called
Partial least square is proposed. This method is a series of simple and multiple
regressions by creating new explanatory variables that is a linear combination of
the original explanatory variables. By taking the statistical test related to linear
regression, it is possible to choose independent significant variables used in the
Partial Least Square regression. The multicollinearity case study on the logistic
regression shows that the partial least square provides better estimation.
|
|---|