Vif stata logistic regression
- how to solve multicollinearity in stata
- how to fix multicollinearity in stata
- how to solve collinearity in stata
- how to calculate multicollinearity
Multicollinearity test stata command.
Detecting and Correcting Multicollinearity Problem in Regression Model
Multicollinearity means independent variables are highly correlated to each other.
How to interpret vif in stata
In regression analysis, it's an important assumption that regression model should not be faced with a problem of multicollinearity.
Why is multicollinearity a problem?
If the purpose of the study is to see how independent variables impact dependent variable, then multicollinearity is a big problem.
If two explanatory variables are highly correlated, it's hard to tell which has an effect on the dependent variable.
Lets say, Y is regressed against X1 and X2 and where X1 and X2 are highly correlated.
Then the effect of X1 on Y is hard to distinguish from the effect of X2 on Y because any increase in X1 tends to be associated with an increase in X2.
Another way to look at multicollinearity problem is : Individual t-test P values can be misleading.
It means a P value can be high which means variable is not important, even though the variable is important.
When multicollinearity is not a problem?
- what causes multicollinearity
- what is multicollinearity in statistics