Contents
What does variance inflation factor indicates?
Variance inflation factor (VIF) is a measure of the amount of multicollinearity in a set of multiple regression variables. This ratio is calculated for each independent variable. A high VIF indicates that the associated independent variable is highly collinear with the other variables in the model.
What is an inflation factor?
Inflation Factor — the loading factor providing for future increases in either the cost of losses or the size of exposure bases (e.g., payroll, sales) resulting from inflation. It may be applied to historical data of any kind to convert historical data into more current data when making projections.
What does VIF of 1 mean?
A VIF of 1 means that there is no correlation among the jth predictor and the remaining predictor variables, and hence the variance of bj is not inflated at all.
How VIF is calculated?
The Variance Inflation Factor (VIF) is a measure of colinearity among predictor variables within a multiple regression. It is calculated by taking the the ratio of the variance of all a given model’s betas divide by the variane of a single beta if it were fit alone.
How do you check for Multicollinearity in SAS?
New SAS User There are no such command in PROC LOGISTIC to check multicollinearity . 1) you can use CORRB option to check the correlation between two variables. 2) Change your binary variable Y into 0 1 (yes->1 , no->0) and use PROC REG + VIF/COLLIN .
What is the formula for the variance inflation factor?
A common R function used for testing regression assumptions and specifically multicolinearity is “VIF ()” and unlike many statistical concepts, its formula is straightforward: $$ V.I.F. = 1 / (1 – R^2). $$ The Variance Inflation Factor (VIF) is a measure of colinearity among predictor variables within a multiple regression.
How is the variance of a predictor inflated?
As you can see, three of the variance inflation factors — 8.42, 5.33, and 4.41 — are fairly large. The VIF for the predictor Weight, for example, tells us that the variance of the estimated coefficient of Weight is inflated by a factor of 8.42 because Weight is highly correlated with at least one of the other predictors in the model.
How is the variance of the weight coefficient inflated?
Therefore, the variance inflation factor for the estimated coefficient Weight is by definition: Again, this variance inflation factor tells us that the variance of the weight coefficient is inflated by a factor of 8.42 because Weight is highly correlated with at least one of the other predictors in the model.
When to use variance inflation factor in OLS?
It is a statistical concept that indicates the increase in the variance of a regression coefficient as a result of collinearity. Variance inflation factor (VIF) is used to detect the severity of multicollinearity in the ordinary least square (OLS) regression analysis.