turn on suggestions

Auto-suggest helps you quickly narrow down your search results by suggesting possible matches as you type.

Showing results for

Find a Community

- Home
- /
- Analytics
- /
- Stat Procs
- /
- PROC LOGISTIC, SELECTION=BACKWARD Variable Selecti...

Topic Options

- Subscribe to RSS Feed
- Mark Topic as New
- Mark Topic as Read
- Float this Topic for Current User
- Bookmark
- Subscribe
- Printer Friendly Page

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Highlight
- Email to a Friend
- Report Inappropriate Content

03-06-2016 02:50 AM

I've been looking at the sample "SAS Statistical Business Analysis" exam questions at http://support.sas.com/certify/creds/samples/

Question #6 is as follows:

Question 6

When selecting variables or effects using SELECTION=BACKWARD in the LOGISTIC procedure, the business analyst's model selection terminated at Step 3.

What happened between Step 1 and Step 2?**correct_answer = "D"**

What happened between Step 1 and Step 2?

- DF increased.
- AIC increased.
- Pr > Chisq increased.
- - 2 Log L increased.

My question is: Shouldn't the answer be "C"? My understanding is that variable selection is based on a variable's p-value. The backward selection process, by default, eliminates variables one-by-one which don't meet the 0.10 criterion to "stay" in the model.

Could someone please clarify if my understanding is wrong? If so, please explain why "D" is the correct answer.

Thanks.

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Highlight
- Email to a Friend
- Report Inappropriate Content

03-06-2016 04:14 AM

The p values of all variables in the model will change when one is removed. One or more of the remaining might become "significant".

The question specifically asks about the step before variable selection. As variables are removed, - 2 Log L will increase. So the suggested answer is correct.

Norman.

Norman.

SAS 9.4 (TS1M4) X64_7PRO WIN 6.1.7601 Service Pack 1 Workstation

SAS 9.4 (TS1M4) X64_7PRO WIN 6.1.7601 Service Pack 1 Workstation

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Highlight
- Email to a Friend
- Report Inappropriate Content

03-06-2016 08:37 PM - edited 03-06-2016 08:44 PM

Oh, Contra .

Pr > Chisq( H0: beta = 0 ) is smaller is better (which means model is more significant. i.e. model can explain more variance of Y )

- 2 Log L is bigger is better(which is likelihood of X. more is better model).

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Highlight
- Email to a Friend
- Report Inappropriate Content

03-07-2016 02:36 AM

Bigger is not better.

The more variables that are included, the better the model fit (and the lower the deviance). However, the idea is to construct a model with only as many variables as are required.

Removing variables will decrease the model fit, but this, in itself, is not a bad thing.

Norman.

Norman.

SAS 9.4 (TS1M4) X64_7PRO WIN 6.1.7601 Service Pack 1 Workstation

SAS 9.4 (TS1M4) X64_7PRO WIN 6.1.7601 Service Pack 1 Workstation