IASSC ICGB Exam - Topic 9 Question 36 Discussion

Actual exam question for IASSC's ICGB exam

Question #: 36
Topic #: 9

[All ICGB Questions]

Which statement(s) are correct for the Regression Analysis shown here? (Note: There are 2 correct answers).

AThis Regression is an example of a Multiple Linear Regression.

BThis Regression is an example of Cubic Regression.

C%Cu explains the majority of the process variance in heat flux.

DThickness explains over 80% of the process variance in heat flux.

EThe number of Residuals in this Regression Analysis is 26.

Show Suggested Answer

Suggested Answer: A

by Howard at Dec 07, 2022, 11:58 PM

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Kenny

4 months ago

Cubic Regression? That seems off to me.

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Terrilyn

4 months ago

%Cu is crucial for understanding heat flux variance.

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Valentin

5 months ago

Wait, are we sure about the number of Residuals being 26?

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Phillip

5 months ago

I think Thickness explaining over 80% is spot on!

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Cortney

5 months ago

This is definitely a Multiple Linear Regression.

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Marshall

5 months ago

I vaguely remember that the number of residuals is usually related to the sample size, but I can't remember if it's specifically 26 in this case.

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Brock

5 months ago

I practiced a similar question where we had to identify the type of regression, and I feel like this might be cubic regression, but I could be wrong.

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Stephania

5 months ago

I think option C sounds familiar; we talked about how certain variables can explain variance in heat flux, but I can't recall the exact percentages.

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Lizette

5 months ago

I remember discussing multiple linear regression in class, but I'm not sure if this one fits that definition.

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Rosamond

5 months ago

Ah, I think I've got it. The "L" is likely referring to the length or distance of the optical transmission. That makes the most sense given the options provided.

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Pamella

5 months ago

Okay, I've got this! The Safe Action button is Option C. I recognize that icon from using the Maya tools before. Feeling confident about this one.

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Franklyn

10 months ago

Wait, is this a trick question? Are they trying to see if we're paying attention to the number of residuals? That's just cruel!

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Raylene

9 months ago

I know, it does seem like a trick question with that detail about the number of residuals!

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Shelton

9 months ago

D) Thickness explains over 80% of the process variance in heat flux.

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Samira

10 months ago

A) This Regression is an example of a Multiple Linear Regression.

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Doretha

11 months ago

I'm with Yuonne on this one. The R-squared values for %Cu and Thickness seem to indicate they are the key drivers of heat flux. Time to brush up on my regression analysis!

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Hassie

8 months ago

Definitely, brushing up on regression analysis will help us interpret the results better.

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Moon

8 months ago

I think we should focus on understanding the relationship between %Cu and Thickness with heat flux.

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Latrice

8 months ago

Yes, those variables seem to be the key drivers of heat flux in this regression analysis.

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Hillary

9 months ago

I agree, the R-squared values for %Cu and Thickness are high.

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Josphine

9 months ago

Definitely, understanding Regression Analysis is crucial for interpreting these results.

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Cordelia

9 months ago

I think we should definitely brush up on our regression analysis skills.

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Vincent

9 months ago

Yes, it seems like %Cu and Thickness are the key drivers of heat flux in this analysis.

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Ashlee

10 months ago

I agree, the R-squared values for %Cu and Thickness are high.

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Kristel

11 months ago

Wow, this is a tough one! I'm going to have to think carefully about the relationships between the variables to nail this down.

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Glenna

10 months ago

User 2: I agree, A) and D) seem to be the correct statements.

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Weldon

10 months ago

User 1: I think A) and D) are correct.

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Rebecka

11 months ago

B is definitely not correct. This is a linear regression, not a cubic one. And E is wrong too - the number of residuals should be equal to the number of data points.

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