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ASQ CSSGB Exam - Topic 9 Question 80 Discussion

If a process has Outliers which pair of charts is most preferable if subgroups will exist for the Continuous Data?
B) Xbar-R Charts
A) Individual---Moving Range
C) Xbar-S Charts
D) nP and P Charts

ASQ CSSGB Exam - Topic 9 Question 80 Discussion

Actual exam question for ASQ's CSSGB exam
Question #: 80
Topic #: 9
[All CSSGB Questions]

If a process has Outliers which pair of charts is most preferable if subgroups will exist for the Continuous Data?

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Suggested Answer: B

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Glen
13 hours ago
I prefer C. Xbar-S Charts give more detail on variability.
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Raina
6 days ago
I think B is best. Xbar-R Charts handle outliers well.
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Roxane
11 days ago
C) Xbar-S Charts can handle subgroups well, I’m with that!
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Graciela
16 days ago
Wait, are we sure about B? I thought A was more common.
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Izetta
2 months ago
A) Individual-Moving Range is useful for outliers too!
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Ashanti
2 months ago
I disagree, C) Xbar-S Charts might be better for variability.
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Whitley
2 months ago
B) Xbar-R Charts are great for subgroups!
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Yolande
3 months ago
Wait, why are we even considering D) nP and P Charts?
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Vallie
3 months ago
Not sure about that, I prefer B) Xbar-R Charts.
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Ernie
3 months ago
Definitely A) Individual-Moving Range for outliers!
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Estrella
3 months ago
I think C) Xbar-S Charts could work too.
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Brice
3 months ago
B) Xbar-R Charts are great for subgroups!
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Wendell
3 months ago
I recall that Xbar-S Charts are used for larger sample sizes, but I’m not sure how they compare to Xbar-R Charts when it comes to outliers.
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James
4 months ago
I’m a bit confused about the outliers part. I thought Individual-Moving Range charts could be useful, but I’m not confident if they’re the best for subgroups.
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Filiberto
4 months ago
I practiced a similar question about control charts, and I feel like B) Xbar-R Charts might be the right choice, but I could be mixing it up with C) Xbar-S Charts.
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Jeffrey
4 months ago
I think I remember that Xbar-R Charts are often used for continuous data, but I'm not entirely sure if they handle outliers well.
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