PRINCIPAL COMPONENT LOGISTIC REGRESSION (PCLR) UNTUK MULTIKOLINEARITAS DALAM REGRESI LOGISTIK BINER; PRINCIPAL COMPONENT LOGISTIC REGRESSION (PCLR) FOR MULTICOLLINEARITY IN BINARY LOGISTIC REGRESSION

Afifi, Nayla Azmi, Zulaela

2016 | Skripsi | FMIPA

Logistic regression is used to predict binary response variabel by a set of independent variables (predictors). The parameter estimation can be not too accurate, also the interpretation of odd ratio may be erroneous when there is multicollinearity among the predictors. In order to improve the estimation of the parameters under multicollinearity, we need to reduce the predictor dimension using optimum principal components of the covariates. Principal components are determined using covariance matrix of the centered data. The number of optimum principal components are selected based on several criterias, i.e. Kaiser?s Stopping Rule, Scree Test, and Percent of Cumulative Variance. Finally, the principal component logistic regression model performance is compared with the logistic regression model with removed multicollinear predictors. By looking at the Bayesian Information Criterion (BIC), PCLR considered better than logistic regression model with removed multicollinear predictors because it has a smaller BIC. In contrast to logistic regression model with removed multicollinear predictors, PCLR can maintain the predictors so there is no loss of information needed.

Kata Kunci : logistic regression; multicollinearity; principal component