MODEL REGRESI LINEAR PARTIAL LEAST SQUARE TERGENERALISASI PARTIAL LEAST SQUARE GENERALISED LINEAR REGRESSION

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