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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?
Suggested Answer: A
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32 comments · newest first
BI
Billye
10 months ago
Missing values could also mess things up, but not as likely here.
MI
Minna
10 months ago
Wait, how can there be too many observations? That seems odd.
AN
Annette
10 months ago
I thought collinearity could be a problem too, though.
TI
Timothy
10 months ago
Totally agree, that’s a classic reason for non-convergence!
AL
Alisha
10 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.
JO
Joana
10 months ago
Missing values could definitely cause problems, but I feel like the agent variable might be the main issue here.
DO
Dorian
11 months ago
Looks like quasi-complete separation is the issue here.
VA
Vallie
11 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.
FL
Flo
11 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.
LI
Lindsay
11 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.
VI
Virgie
12 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.
FL
Florencia
12 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.
HI
Hillary
1 year 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.
JA
Jacquelyne
1 year 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.
MO
Moon
1 year 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.
NI
Niesha
1 year 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.
MA
Maynard
1 year 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.
GE
Gertude
1 year 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.
TR
Tresa
1 year 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.
OL
Olive
1 year ago
Missing values, really? Isn't that just lazy data-collecting? Come on, analysts, get it together!
MA
Mabel
1 year ago
A) There is quasi-complete separation in the data.
AL
Almeta
1 year ago
I think missing values in the data might be causing the convergence problem.
AU
Audrie
1 year ago
I believe collinearity among predictors could also be a reason for the model failing to converge.
JO
Johnetta
1 year 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.
BO
Bobbye
1 year ago
Collinearity can definitely cause convergence issues in logistic regression models.
LY
Lynda
1 year ago
B) There is collinearity among the predictors.
LA
Lanie
1 year ago
A) There is quasi-complete separation in the data.
BE
Beatriz
1 year ago
Hmm, the data seems to have some strange patterns. Maybe there's some kind of separation going on here that's causing the model to freak out.
CR
Craig
1 year ago
B) There is collinearity among the predictors.
IE
Iesha
1 year ago
A) There is quasi-complete separation in the data.
BE
Bettina
1 year ago
I agree with Shelton, quasi-complete separation can cause convergence issues.
SH
Shelton
1 year ago
I think the model fails to converge because of quasi-complete separation.