Results in the Constitution appear in the center column; results from essays appear in the right column. Use a short, descriptive search phrase. Precise phrases like âreligious freedom,â âtaxation,â or âwomenâs suffrageâ will work better than longer phrases.
Multicollinearity refers to a situation in which two or more explanatory variables in a multiple regression model are highly linearly related. We have perfect multicollinearity if, for example as in the equation above, the correlation between two independent variables is equal to 1 or -1.In practice, we rarely face perfect multicollinearity in a data set.rom Wikipedia, the free encyclopedia. In statistics, multicollinearity (also collinearity) is a phenomenon in which two or more predictor variables in a multiple regression model are highly correlated, meaning that one can be linearly predicted from the others with a substantial degree of accuracy.
Solutions for Multicollinearity in Multiple Regression . What Can I Do About Multicollinearity? The best solution for dealing with multicollinearity is to understand the cause of multicollinearity and remove it. Multicollinearity occurs because two (or more) variables are related or they measure the same thing. If one of the variables in your model doesnât seem essential to your model, removing it may reduce multicollinearity. Examining the correlations between variables and taking into account the importance of the variables will help you make a decision about what variables to drop from the model.