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SAS A00-240 Exam - Topic 5 Question 115 Discussion

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?
C) Quasi-complete separation
A) Collinearity
B) Influential observations
D) Problems that arise due to missing values

SAS A00-240 Exam - Topic 5 Question 115 Discussion

Actual exam question for SAS's A00-240 exam
Question #: 115
Topic #: 5
[All A00-240 Questions]

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?

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Suggested Answer: C

by Maryann at Aug 02, 2025, 05:04 PM

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Angella
6 months ago
Missing values don't seem relevant here, right?
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Alesia
6 months ago
I thought it might be collinearity, but C makes more sense.
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Sarina
6 months ago
Wait, is it really that straightforward?
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Viola
6 months ago
Sounds like quasi-complete separation to me!
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Trinidad
6 months ago
Definitely C, can't have a probability of 1 without issues.
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Ronny
7 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.
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Temeka
7 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.
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Jame
7 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?
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Peggy
7 months ago
I remember discussing quasi-complete separation in class, where one category perfectly predicts the outcome. That sounds like what’s happening here.
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Carolynn
7 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.
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Fidelia
8 months ago
I'm pretty confident this is a problem of quasi-complete separation. I'll make sure to explain that in my answer.
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Daniel
8 months 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.
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Kiera
8 months 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.
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Jolanda
8 months ago
This looks like a tricky one. I'll need to think carefully about the implications of the probability being 1 for Region A.
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Alita
9 months ago
I see your point, Sharen. It could be either of those two.
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Chauncey
9 months 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.
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Sharen
10 months ago
But could it also be quasi-complete separation?
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Barrett
10 months ago
I agree with Alita, it seems like collinearity is the issue here.
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Alita
10 months ago
I think the problem illustrated is collinearity.
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Naomi
10 months 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.
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Cecil
9 months ago
Quasi-complete separation can cause issues in logistic regression models.
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Weldon
9 months ago
Yes, that probability of 1 when Region = A is definitely a sign of quasi-complete separation.
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