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PRMIA Exam 8007 Topic 2 Question 81 Discussion

Actual exam question for PRMIA's 8007 exam
Question #: 81
Topic #: 2
[All 8007 Questions]

Which of the following can induce a 'multicollinearity' problem in a regression model?

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Suggested Answer: C

by Tasia at Sep 12, 2024, 10:17 PM

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Elmira
3 months ago
Ugh, multicollinearity is the bane of every regression analyst's existence. But C is the right call - those overly friendly predictors are the culprit.
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Terrilyn
2 months ago
Definitely, including both of them can lead to multicollinearity issues.
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Annette
2 months ago
Yeah, it's important to watch out for those sneaky predictors that are too similar.
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Alline
2 months ago
I agree, those highly correlated explanatory variables can really mess up the regression results.
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Abel
3 months ago
I'm going with C. High correlation between explanatory variables is the textbook definition of multicollinearity. The other options just don't fit the bill.
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Mickie
3 months ago
Haha, I bet the exam writer is trying to trick us with those other options. But C is the clear winner here - multicollinearity is all about those pesky predictor variables getting too cozy with each other.
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Chandra
2 months ago
Exactly, we have to watch out for those high positive correlations between two explanatory variables.
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Jonell
2 months ago
Yeah, it's when those explanatory variables start acting like twins or something.
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Melvin
3 months ago
I agree, C is definitely the culprit when it comes to multicollinearity.
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Trinidad
3 months ago
I don't think so, multicollinearity is specifically about the relationship between the explanatory variables, not about omitting variables.
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Shawnee
3 months ago
But what about option D) The omission of a relevant explanatory variable? Could that also induce multicollinearity?
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Kenny
3 months ago
Definitely C. Omitting a relevant variable (D) can cause other issues, but multicollinearity is all about the relationships between the predictors.
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Lai
2 months ago
So, we all agree that the correct answer is C) A high positive correlation between two explanatory variables.
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Marshall
2 months ago
You're right, C) A high positive correlation between two explanatory variables can definitely cause multicollinearity.
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Mireya
2 months ago
Actually, I believe it's C) A high positive correlation between two explanatory variables that can induce multicollinearity.
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Gilberto
2 months ago
I think it's B) A high positive correlation between the dependent variable and one of the explanatory variables.
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Trinidad
3 months ago
I think the answer is C) A high positive correlation between two explanatory variables.
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Melissa
3 months ago
I agree with Trinidad, multicollinearity occurs when two explanatory variables are highly correlated.
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Mary
4 months ago
I think C is the correct answer. High positive correlation between explanatory variables is a classic sign of multicollinearity.
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Cathrine
3 months ago
Yes, you're right. High positive correlation between explanatory variables can definitely lead to multicollinearity.
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Destiny
3 months ago
I think C is the correct answer.
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