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SAS A00-240 Exam - Topic 8 Question 111 Discussion

Actual exam question for SAS's A00-240 exam
Question #: 111
Topic #: 8
[All A00-240 Questions]

The selection criterion used in the forward selection method in the REG procedure is:

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Suggested Answer: B

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Pedro
2 months ago
100% agree, SLE is the way to go!
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Aleisha
3 months ago
Mallows' Cp is used in other methods, not this one.
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Brinda
3 months ago
Wait, is it really not Adjusted R-Square?
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Elliott
3 months ago
I thought it was AIC, but I guess not.
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Selma
3 months ago
It's definitely SLE!
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Ben
4 months ago
AIC seems familiar, but I thought it was more related to model comparison rather than the selection process itself.
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Gracia
4 months ago
I feel like adjusted R-square is important for model selection, but I can't recall if it's specifically for forward selection.
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Harrison
4 months ago
I remember practicing with Mallows' Cp in a similar question, but I don't think that's right for forward selection.
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Joye
4 months ago
I think the forward selection method uses SLE, but I'm not completely sure. It feels like I might be mixing it up with backward elimination.
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Evangelina
4 months ago
I'm pretty confident the answer is C, Mallows' Cp. The forward selection method aims to minimize Mallows' Cp, which is a measure of model fit that balances goodness of fit and model complexity.
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Roslyn
5 months ago
Okay, let me see if I can work this out. The forward selection method starts with an empty model and adds variables one by one based on some selection criterion. I believe the criterion is to choose the variable that gives the largest increase in R-squared, so the answer is A, Adjusted R-Square.
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Keneth
5 months ago
Hmm, I'm a bit unsure about this one. I know the forward selection method is used to build a regression model, but I can't quite recall the specific selection criterion it uses. I'll have to think this through carefully.
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Colene
5 months ago
I'm pretty sure the answer is B. The forward selection method uses the SLE (Significance Level for Entry) as the selection criterion to determine which variables to add to the model.
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