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What is Variance inflation Factor?
In statistics, VIF measures that how much inflation takes place for the variance of related coefficient of estimated regression as when these variables or predictor variables are not linearly attached. It is completely associated with multicollinearity and it also indicates that in regression analysis how much multicollinearity exists?
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How to interpret the Variance inflation factor?
If the value of VIF is 1, it indicates that it is not correlated. Id the condition shows that the VIF is more than one, but less than 5, then it is moderately correlated. However, if the value shows that VIF is more than 5 to 10, then it is highly correlated. So, you just need to take the perfect decision of what is the regression value expects.
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What are the various terms that you must know?
Before you take an estimation of what is important and what are you doing, you must have the proper sense of the following terms. Moreover, always fix the terms that is used adequately. Here are they-
- Multicollinearity.
- Variance regression.
- Linear Model.
- Error term.
- Coefficient of determination.
- Predictor variable.
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How many steps are there?
Mainly there are three steps to find out the Variance inflation and these are –
- Step one id used for those that is known as error term.
- Step two where coefficient variables are very important.
- Step three is used to find out the multicollinearity, which returns higher rate.
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