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IASSC Exam ICGB 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).

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Suggested Answer: A

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

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Franklyn
3 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
2 months ago
I know, it does seem like a trick question with that detail about the number of residuals!
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Shelton
2 months ago
D) Thickness explains over 80% of the process variance in heat flux.
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Samira
3 months ago
A) This Regression is an example of a Multiple Linear Regression.
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Doretha
4 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
1 months ago
Definitely, brushing up on regression analysis will help us interpret the results better.
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Moon
1 months ago
I think we should focus on understanding the relationship between %Cu and Thickness with heat flux.
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Latrice
1 months ago
Yes, those variables seem to be the key drivers of heat flux in this regression analysis.
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Hillary
1 months ago
I agree, the R-squared values for %Cu and Thickness are high.
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Josphine
2 months ago
Definitely, understanding Regression Analysis is crucial for interpreting these results.
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Cordelia
2 months ago
I think we should definitely brush up on our regression analysis skills.
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Vincent
2 months ago
Yes, it seems like %Cu and Thickness are the key drivers of heat flux in this analysis.
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Ashlee
3 months ago
I agree, the R-squared values for %Cu and Thickness are high.
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Kristel
4 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
3 months ago
User 2: I agree, A) and D) seem to be the correct statements.
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Weldon
3 months ago
User 1: I think A) and D) are correct.
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Rebecka
4 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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Remedios
4 months ago
I'm not sure about C and E, they don't seem relevant to the Regression Analysis.
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Yuonne
4 months ago
C and D seem like the correct answers here. The regression plot shows that both %Cu and Thickness are significant predictors of heat flux.
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Elmer
2 months ago
Thickness explains over 80% of the process variance in heat flux.
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Stephanie
2 months ago
The Regression is an example of Multiple Linear Regression, not Cubic Regression.
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Irene
2 months ago
Yes, %Cu and Thickness explain the majority of the process variance in heat flux.
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Teddy
2 months ago
I agree, both %Cu and Thickness are significant predictors of heat flux.
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Lashawnda
4 months ago
I agree with Elvis, A and D seem to make sense.
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Elvis
4 months ago
I think A and D are correct.
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