Use of The Principal Components in Case of The Problem multicollinearity (An Applied Study on the investment data in Sudan)

This study aims to apply the model Principal component Analysis to overcome the problem of multicollinearity which appears in many estimated models. The core of this method is replacing the independence variables that suffer from the multicollinearity problem with principal components to solve this problem. The problem of multicollinearity arises in a regression model when the independent variables are highly correlated to each other. Multicollinearity does not limit the possibility of obtaining a good model or affect inferences about expected responses the hypotheses the principal components regression method has the capability of resolving the multicollinearity that exists between explained variables , It is possible to conclude that throughout our paper; the problem of multi-collinearity has been solved by using a principal component technique in the case of the panel regression model, by using investment data in the Sudan.