## multicollinearity in Logistic Regression

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Frequent Contributor
Posts: 92

# multicollinearity in Logistic Regression

Dear Team,
I am working on a C-SAT data where there are 2 outcome : SAT(9-10) and DISSAT(1-8). I have approx. 22 predictor variables most of which are categorical and some have more than 10 categories. As with Linear regression we can VIF to test the multicollinearity in predcitor variables. While searching from SAS forum itself i realized we can use "influence" as a measure but that helps with outliers. Moreover from this post
https://communities.sas.com/t5/SAS-Statistical-Procedures/Outliers-and-Multicollinearity-for-Regress... there is a link explaining the diagnostics however i do not understand the outcome in detail. Is there any other approach.

Also can we use stepwise/forward/backward regression to remove non signifincant predictors at a given p value. Kindly advice.

Attached is the data for reference. I am using Base SAS.

Regards, Shivi

Accepted Solutions
Solution
‎08-20-2016 03:24 AM
Super User
Posts: 10,213

## Re: multicollinearity in Logistic Regression

```Sorry. I don't know what that  WOE & IV  mean.
There are PROC GLMSELECT can pick up the most valuable variables for many models.
But that is a big topic .

```

All Replies
Super User
Posts: 10,213

## Re: multicollinearity in Logistic Regression

```Unlike proc reg which using OLS, proc logistic is using MLE ,
therefore you can't check multicollinearity. But SAS will automatically remove a
variable when it is collinearity with other variables.

Yes. you can use stepwise/forward/backward to  remove non signifincant predictors.Like:
proc logistic;
model y=x1 x2....x40 /selection=stepwise;
run;

Check documentation to see more examples.
```
Frequent Contributor
Posts: 92

## Re: multicollinearity in Logistic Regression

Thanks for the help.

I am using WOE & IV to reduce the number of predictors in the model as these can assist with both nominal and continuous variables.

Regards.

Solution
‎08-20-2016 03:24 AM
Super User
Posts: 10,213

## Re: multicollinearity in Logistic Regression

```Sorry. I don't know what that  WOE & IV  mean.
There are PROC GLMSELECT can pick up the most valuable variables for many models.
But that is a big topic .

```
Frequent Contributor
Posts: 92

## Re: multicollinearity in Logistic Regression

Thank you for assistance again.

Seems like the more you explore SAS the more you realize how vast & robust it is.

As you have suggested i will start witih build stepwise, forward & backward models and will do a comparison as i am not educated on Proc GLM Select and probably may not time as of now. But i will for sure check it in the near future.

Thanks again.

☑ This topic is solved.