When creating a quantitative forecast, data should be evaluated to detect for a repeating pattern of demand from year to year, or over some other time interval, with some periods of considerably higher demand than others. This is known as a?

When creating a quantitative forecast, data should be evaluated to detect for a repeating pattern of demand from year to year, or over some other time interval, with some periods of considerably higher demand than others. This is known as a?

• A. Trend Variation
• B. Seasonal Variation
• C. Random Variation
• D. Cyclical Variation

Answer: (B)

Seasonal variation describes a repeating pattern in demand that occurs within a fixed time interval, such as months or quarters, with certain periods consistently higher than others. When forecasting, recognizing this regular within-year pattern allows you to adjust predictions for expected peaks and troughs—like higher demand in certain seasons or during holidays—so the forecast reflects these predictable swings.

This differs from a trend, which is a long-term movement up or down in the overall level and doesn’t imply a repeating seasonal pattern. Random variation is irregular, unpredictable noise that doesn’t repeat in a regular way. Cyclical variation involves longer, less regular up-and-down movements that span multiple years and don’t have a fixed calendar period.

Thus, the described repeating, periodically higher demand aligns with seasonal variation.