New Year Sale 2026! Hurry Up, Grab the Special Discount - Save 25% - Ends In 00:00:00 Coupon code: SAVE25
Welcome to Pass4Success

- Free Preparation Discussions
Mail Us support@pass4success.com
Location US
Mob_nav_barsMENU

IASSC ICGB Exam - Topic 6 Question 92 Discussion

Actual exam question for IASSC's ICGB exam
Question #: 92
Topic #: 6
[All ICGB Questions]

Multiple Linear Regressions (MLR) is best used when which of these are applicable? (Note: There are 3 correct answers).

Show Suggested Answer Hide Answer
Suggested Answer: C, D, E

by Linwood at Nov 23, 2025, 07:45 PM

Limited Time Offer

25%

Off

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

Contribute your Thoughts:

0/2000 characters
Submit Cancel
Juliann
15 days ago
D seems a bit off to me, not sure about that one.
upvoted 0 times
...
Floyd
20 days ago
Wait, can MLR really handle non-linear relationships?
upvoted 0 times
...
Marylin
26 days ago
I think E is also a must-have for MLR.
upvoted 0 times
...
Albina
1 month ago
Haha, D? Really? That's like saying multiple linear regression is best used when you want to prevent a designed experiment. What a joke!
upvoted 0 times
...
Buck
1 month ago
A, B, and C seem like the obvious choices here. I'm not even sure what D is asking.
upvoted 0 times
...
Geraldo
1 month ago
C, D, and E for sure. Who would choose D though? That's just silly.
upvoted 0 times
...
Gretchen
2 months ago
Hmm, I think A, B, and C are the right ones. Multiple linear regression can handle non-linear relationships, right?
upvoted 0 times
...
Dante
2 months ago
C, D, and E are the correct answers. I'm pretty sure about that.
upvoted 0 times
...
Tatum
2 months ago
I feel like B could be a tricky one; I think uncertainty in the slope might not directly relate to when we use MLR, but I’m not completely confident.
upvoted 0 times
...
Sophia
2 months ago
I practiced a question similar to this, and I believe E is also correct because we need the X's to be independent for MLR to work properly.
upvoted 0 times
...
Ashley
2 months ago
I'm not entirely sure, but I think A might be wrong since MLR assumes linear relationships, right?
upvoted 0 times
...
Lynette
2 months ago
Okay, I think I've got it. MLR is best used when you have relationships between the output Y and multiple inputs X, which is C. It can also handle non-linear relationships between the X's and Y, which is A. And the independence of the X's, which is E, is another key assumption. I'm feeling good about those three choices.
upvoted 0 times
...
Refugia
3 months ago
I remember that MLR is mainly used for relationships involving multiple predictors, so I think C is definitely one of the correct answers.
upvoted 0 times
...
Marta
3 months ago
I feel pretty confident about this one. MLR is all about handling multiple predictors, so C is for sure correct. And the question specifies that there are 3 right answers, so I'm guessing B and E are the other two. I'll mark those down and move on.
upvoted 0 times
...
Leota
3 months ago
C is definitely one of the correct answers!
upvoted 0 times
...
Viola
3 months ago
D seems off to me. MLR can be used even if experiments are designed.
upvoted 0 times
...
Mary
3 months ago
Whoa, this is a lot to unpack. I'm a little lost on some of these options. What does "preventing the use of a Designed Experiment" even mean? And the independence of the X's - is that really a requirement for MLR? I'm going to have to review my notes on the assumptions before answering this one.
upvoted 0 times
...
Dahlia
4 months ago
Okay, let's see. I know MLR is used for multiple inputs, so C is definitely one of the correct answers. And the question mentions non-linear relationships, so A could be another one. But I'm not sure about the other options - I'll have to think through those a bit more.
upvoted 0 times
...
Haydee
4 months ago
Hmm, this one seems tricky. I think the key is to focus on the "best used when" part of the question. That's probably hinting at the specific conditions where MLR is the most appropriate approach.
upvoted 0 times
...

Save Cancel