APICS Exam CPIM-MPR Topic 4 Question 75 Discussion

Actual exam question for APICS's CPIM-MPR exam

Question #: 75
Topic #: 4

When quantitative data are being evaluated, a very small bias in a forecast can best be explained by which of the following statements?

APositive forecast errors approximately offset negative forecast errors.

BThe absolute values of all forecast errors were approximately equal.

CThe variability of forecast data was skewed.

DA very small alpha factor was used.

Show Suggested Answer

Suggested Answer: B

by Hillary at Oct 03, 2023, 11:16 PM

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Christiane

2 months ago

Well, at least it's not a trick question like 'when does a bear sit in the woods?' Haha, gotta love these statistics problems.

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Rolf

28 days ago

D) A very small alpha factor was used.

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Hubert

1 months ago

C) The variability of forecast data was skewed.

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Evangelina

1 months ago

B) The absolute values of all forecast errors were approximately equal.

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Rocco

2 months ago

A) Positive forecast errors approximately offset negative forecast errors.

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Rolf

2 months ago

Hmm, I'm going with C. The skewed variability of the forecast data must be the culprit. That's my best guess!

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Peggy

1 months ago

User 3: I'm sticking with C. The skewed variability is the reason for the bias.

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Yolando

1 months ago

User 2: I disagree, I believe it's D. A small alpha factor was used.

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Adelle

2 months ago

User 1: I think it's A. Positive forecast errors offset negative ones.

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Kaitlyn

3 months ago

B. is my pick. If all the forecast errors were about the same size, that could lead to a small overall bias, right?

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Gerry

1 months ago

B. is my pick. If all the forecast errors were about the same size, that could lead to a small overall bias, right?

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Lang

1 months ago

B) The absolute values of all forecast errors were approximately equal.

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Taryn

1 months ago

A) Positive forecast errors approximately offset negative forecast errors.

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Norah

2 months ago

C) The variability of forecast data was skewed.

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Madonna

2 months ago

B) The absolute values of all forecast errors were approximately equal.

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Derrick

2 months ago

A) Positive forecast errors approximately offset negative forecast errors.

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Hollis

3 months ago

D. using a small alpha factor makes the most sense to me. Doesn't that help reduce bias in the forecasting model?

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Kaycee

2 months ago

True, having all forecast errors with approximately equal absolute values can also explain a small bias.

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Mitzie

2 months ago

The variability of forecast data being skewed could also contribute to a small bias.

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Tamie

2 months ago

I think positive forecast errors offsetting negative forecast errors is also a good explanation.

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Fernanda

3 months ago

Yes, using a small alpha factor can help reduce bias in the forecast.

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Jennifer

3 months ago

I think the correct answer is A. Positive and negative forecast errors offsetting each other seems like the most logical explanation for a small bias.

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Arletta

2 months ago

I think it could also be because a very small alpha factor was used.

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Myrtie

2 months ago

I agree, positive and negative errors balancing out makes sense.

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Heike

4 months ago

But if positive forecast errors offset negative ones, it would explain the small bias.

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Leatha

4 months ago

I disagree, I believe it's C.

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Heike

4 months ago

I think the answer is A.

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