Collinearity affects and it's analysis in data

One of the assumptions of the multiple linear regression model is that there is no exact linear relationship between any of the independent variables. If such a linear relationship does exist, it can be said that the independent variables are collinear or multicollinearity. When collinearity exists among the regressors, a variety of interrelated problems are created. Specially, in the model building process collinearity causes high variance for parameters if ordinary least squares estimator (OLSE) is used. The main objective of this research paper is to analyze and detect the collinearity in the data set and recommend some important dealing methods for collinearity problems. Two collinearity data sets are used to illustrate the methodologies proposed in this research paper. The first data set was generated using Monte Carlo Simulation method with the highest correlation between the regressors and this data set contains five regressors and a response variable. The second data set is also a real collinearity data set of Macroeconomic Impact of Foreign Direct Investment in Sri Lanka form 1978 to 2004 and it contains four regressor and one response variables.