SAS A00-240 Exam - Topic 2 Question 114 Discussion

Actual exam question for SAS's A00-240 exam

Question #: 114
Topic #: 2

An analyst fits a logistic regression model to predict whether or not a client will default on a loan. One of the predictors in the model is agent, and each agent serves 15-20 clients each. The model fails to converge. The analyst prints the summarized data, showing the number of defaulted loans per agent. See the partial output below:

What is the most likely reason that the model fails to converge?

AThere is quasi-complete separation in the data.

BThere is collinearity among the predictors.

CThere are missing values in the data.

DThere are too many observations in the data.

Show Suggested Answer

Suggested Answer: A

by Brittni at Jul 11, 2025, 02:39 AM

Limited Time Offer

25%

Off

Get Premium A00-240 Questions as Interactive Web-Based Practice Test or PDF

Contribute your Thoughts:

Submit Cancel

Billye

4 months ago

Missing values could also mess things up, but not as likely here.

upvoted 0 times

...

Minna

4 months ago

Wait, how can there be too many observations? That seems odd.

upvoted 0 times

...

Annette

4 months ago

I thought collinearity could be a problem too, though.

upvoted 0 times

...

Timothy

4 months ago

Totally agree, that’s a classic reason for non-convergence!

upvoted 0 times

...

Alisha

4 months ago

I doubt that having too many observations is the problem. I think it's more about how the data is structured with the agents and defaults.

upvoted 0 times

...

Joana

4 months ago

Missing values could definitely cause problems, but I feel like the agent variable might be the main issue here.

upvoted 0 times

...

Dorian

5 months ago

Looks like quasi-complete separation is the issue here.

upvoted 0 times

...

Vallie

5 months ago

I'm not entirely sure, but I think collinearity could also be a problem if the predictors are too similar. We had a practice question on that.

upvoted 0 times

...

Flo

5 months ago

I remember we discussed quasi-complete separation in class, especially with binary outcomes. It seems like a likely reason for convergence issues here.

upvoted 0 times

...

Lindsay

5 months ago

Ah, I see what's going on here. With so many observations, the model might be struggling to converge. That's an interesting possibility I hadn't considered before. I'll make sure to keep that in mind.

upvoted 0 times

...

Virgie

6 months ago

Okay, let me think this through. Missing values in the data could definitely prevent the model from converging properly. I'll need to double-check the data to see if that might be the issue.

upvoted 0 times

...

Florencia

6 months ago

Hmm, I'm a bit confused here. Could it be an issue with collinearity between the predictors? I'll need to review my notes on that to see if that could be causing the convergence problem.

upvoted 0 times

...

Hillary

6 months ago

This looks like a tricky one. The data seems to show some agents with 0 defaulted loans and others with a lot, so there could be quasi-complete separation. I'll need to think carefully about that option.

upvoted 0 times

...

Jacquelyne

6 months ago

Too many observations? I'm not sure that's the most likely explanation here. The data looks fairly limited, so I don't think that's the issue. I'll focus on the other options.

upvoted 0 times

...

Moon

6 months ago

Missing values in the data could definitely cause problems with the model fitting. I'll double-check the data to see if there are any obvious gaps that could be the culprit.

upvoted 0 times

...

Niesha

7 months ago

I'm a bit confused here. Could the issue be collinearity between the predictors? I'll need to review my notes on that to see if that's a likely cause of the convergence failure.

upvoted 0 times

...

Maynard

7 months ago

Hmm, this looks like a tricky one. The data seems to show that some agents have either all defaulted or all non-defaulted clients, which could indicate quasi-complete separation. I'll need to think carefully about that option.

upvoted 0 times

...

Gertude

8 months ago

Wait, wait, wait... are we sure none of the agents are secretly superheroes in disguise? That would definitely cause some quasi-complete separation. Just a thought.

upvoted 0 times

...

Tresa

8 months ago

Too many observations? Nah, man, the more data the better, right? Unless they're all just a bunch of loan sharks or something. Then I can see why the model would have a hard time.

upvoted 0 times

...

Olive

9 months ago

Missing values, really? Isn't that just lazy data-collecting? Come on, analysts, get it together!

upvoted 0 times

Mabel

7 months ago

A) There is quasi-complete separation in the data.

upvoted 0 times

...

Almeta

9 months ago

I think missing values in the data might be causing the convergence problem.

upvoted 0 times

...

Audrie

9 months ago

I believe collinearity among predictors could also be a reason for the model failing to converge.

upvoted 0 times

...

Johnetta

9 months ago

Collinearity, huh? That could be it. If the agents are all doing pretty much the same thing, the model might not be able to figure out which one is really making the difference.

upvoted 0 times