Which of the following is true regarding the output of statistical forecasting?

The correct answer is that statistical forecasting indicates a level of uncertainty. This is true because statistical forecasting employs various mathematical models and algorithms to predict future demand based on historical data. The nature of these models inherently involves some level of uncertainty, as they are based on assumptions and patterns derived from past occurrences. Thus, while statistical methods can yield valuable insights and trends, they cannot guarantee absolute accuracy due to the complexity and variability of real-world factors affecting demand.

Statistical forecasting takes into account historical data variance, which is crucial for understanding potential fluctuations. It effectively communicates the probabilities associated with forecasts, allowing businesses to prepare for different scenarios based on the calculated confidence intervals.

In contrast to the other options, statistical forecasting does not promise guaranteed accuracy, cannot strictly be limited to only monthly forecasts, and does consider seasonal trends for more accurate predictions. Seasonal patterns are often accounted for in statistical models, enhancing the forecasting process by applying techniques that adjust for these variations throughout different periods of the year.