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Statistics Quiz on Moving Averages

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Questions and Answers

In a 4-month Simple Moving Average calculation, will adding an additional month of data always increase the average value?

Yes, it stabilizes the average value significantly.

No, it will decrease the average value if data is low.

No, it may not affect the average value. (correct)

Yes, it always increases the average value.

Does Exponential Smoothing always give more weight to older data points?

No, it focuses more on the most recent data points. (correct)

Yes, it gives equal weight to all data points.

No, it treats all data uniformly without preference.

Yes, it exponentially decreases the weight of newer data.

What does capacity utilization represent?

The efficiency of workforce productivity.

The total output compared to expected demand.

The maximum output achievable without breakdowns.

The ratio of actual output to design capacity. (correct)

Is it possible for the average value in a Simple Moving Average to decrease when a new data point is added?

<p>Yes, if the new data point is significantly low.</p> Signup and view all the answers

Which statement about Exponential Smoothing is true?

<p>It gives decreasing weight to older points.</p> Signup and view all the answers

Study Notes

Simple Moving Average (SMA)

Adding an additional month of data in a 4-month SMA does not always increase the average; it depends on the relationship between the new month's data and the previous average.

Exponential Smoothing

Exponential smoothing does not give more weight to older data points; it assigns more weight to recent observations, enhancing the relevance of newer data in the forecast.

Capacity Utilization

Capacity utilization measures the efficiency of production by calculating the ratio of actual output to the maximum possible (design) capacity, indicating how effectively resources are being utilized.

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Description

Test your knowledge on Simple Moving Averages and Exponential Smoothing with this quiz. The questions cover fundamental concepts such as capacity utilization and data weighting in statistical analysis. Challenge yourself and see how well you understand these key statistical principles!