APICS CPIM-MPR Exam - 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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Isadora

3 months ago

D is just about the alpha factor, not the bias.

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Jill

3 months ago

C seems plausible, but not sure.

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Hildred

4 months ago

Surprised that people think A is correct!

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Julio

4 months ago

I think B makes more sense.

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Katina

4 months ago

A is definitely the right choice!

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Delsie

4 months ago

I don't think option D is correct because a small alpha factor usually relates to smoothing rather than bias in forecasts.

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Shawnee

4 months ago

I'm a bit confused about option C; I feel like skewness in data could affect bias, but I can't recall the specifics.

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Trina

5 months ago

I remember practicing a question similar to this, and I think option B might be the right choice since it talks about the absolute values of errors.

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Dottie

5 months ago

I think option A sounds familiar because it relates to how errors can balance each other out, but I'm not entirely sure.

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Idella

5 months ago

Ah, I think I've got it. Based on my understanding, the correct answer is A. Positive and negative forecast errors offsetting each other would result in a very small overall bias.

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Sommer

5 months ago

I've seen questions like this before. My strategy is to focus on the key terms and try to eliminate the answer choices that don't seem to fit the description of a small bias in a forecast.

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Beula

5 months ago

I'm a bit confused by this question. The wording is a bit technical, so I'll need to make sure I fully understand the concepts before selecting an answer.

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Cathrine

5 months ago

Okay, let's see. I think the key here is understanding what a small bias means in the context of quantitative data evaluation. I'll need to consider each answer choice and how it relates to that.

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Anika

5 months ago

Hmm, this is a tricky one. I'll need to think through the different options carefully to determine which one best explains a very small bias in a forecast.

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Lemuel

5 months ago

Hmm, this one seems straightforward. I'll carefully read through the options and think about what information is typically included in an invoice header.

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Corrinne

5 months ago

It feels like identifying safety requirements is important, but I can't recall if that's specifically the auditee's job during the opening meeting.

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Kati

5 months ago

Hmm, I'm a bit unsure about the connection between compliance and reliability. I'll need to think this through carefully to make sure I understand the key points.

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Tequila

5 months ago

I remember a practice question about the stateful nature of Runtime Resources, and that makes sense because any maintenance impact could really affect the process.

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Christiane

9 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

8 months ago

D) A very small alpha factor was used.

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Hubert

8 months ago

C) The variability of forecast data was skewed.

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Evangelina

8 months ago

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

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Rocco

9 months ago

A) Positive forecast errors approximately offset negative forecast errors.

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Rolf

10 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

8 months ago

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

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Yolando

9 months ago

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

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Adelle

9 months ago

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

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Kaitlyn

10 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

8 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

8 months ago

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

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Taryn

8 months ago

A) Positive forecast errors approximately offset negative forecast errors.

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Norah

9 months ago

C) The variability of forecast data was skewed.

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Madonna

9 months ago

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

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Derrick

10 months ago

A) Positive forecast errors approximately offset negative forecast errors.

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Hollis

10 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

9 months ago

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

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Mitzie

9 months ago

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

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Tamie

10 months ago

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

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Fernanda

10 months ago

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

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Jennifer

10 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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