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An analyst investigates Region (A, B, or C) as an input variable in a logistic regression model.
The analyst discovers that the probability of purchasing a certain item when Region = A is 1.
What problem does this illustrate?
Suggested Answer: C
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22 comments · newest first
AN
Angella
10 months ago
Missing values don't seem relevant here, right?
AL
Alesia
10 months ago
I thought it might be collinearity, but C makes more sense.
SA
Sarina
10 months ago
Wait, is it really that straightforward?
VI
Viola
11 months ago
Sounds like quasi-complete separation to me!
TR
Trinidad
11 months ago
Definitely C, can't have a probability of 1 without issues.
RO
Ronny
11 months ago
I’m a bit confused about the options, but I feel like influential observations are more about outliers affecting the model rather than this situation.
TE
Temeka
11 months ago
This reminds me of a practice question we did about logistic regression. I think the answer is quasi-complete separation since Region A has a probability of 1.
JA
Jame
12 months ago
I’m not entirely sure, but I think collinearity might be related to this. It’s when two variables are highly correlated, right?
PE
Peggy
12 months ago
I remember discussing quasi-complete separation in class, where one category perfectly predicts the outcome. That sounds like what’s happening here.
CA
Carolynn
12 months ago
This is a tricky one, but I think the key is recognizing the issue of quasi-complete separation. I'll need to be careful in my explanation.
FI
Fidelia
1 year ago
I'm pretty confident this is a problem of quasi-complete separation. I'll make sure to explain that in my answer.
DA
Daniel
1 year ago
Okay, I've got this. The probability of 1 for Region A indicates quasi-complete separation, which can cause issues with model estimation and interpretation.
KI
Kiera
1 year ago
Hmm, I'm a bit confused by this question. I'll need to review my notes on logistic regression and the potential issues that can arise.
JO
Jolanda
1 year ago
This looks like a tricky one. I'll need to think carefully about the implications of the probability being 1 for Region A.
AL
Alita
1 year ago
I see your point, Sharen. It could be either of those two.
CH
Chauncey
1 year ago
Hmm, I'm thinking this is more of an influential observations issue. That 100% probability is really skewing the model, if you ask me.
SH
Sharen
1 year ago
But could it also be quasi-complete separation?
BA
Barrett
1 year ago
I agree with Alita, it seems like collinearity is the issue here.
AL
Alita
1 year ago
I think the problem illustrated is collinearity.
NA
Naomi
1 year ago
Aha! Looks like we've got a case of quasi-complete separation here. That probability of 1 is a real red flag, if you ask me.
CE
Cecil
1 year ago
Quasi-complete separation can cause issues in logistic regression models.
WE
Weldon
1 year ago
Yes, that probability of 1 when Region = A is definitely a sign of quasi-complete separation.