Which of the following is a step in the process for analyzing seasonal variations in data?

The process of analyzing seasonal variations in data involves several steps, and one of the key steps is to compute average seasonal demand. This step is crucial because it allows for the identification of patterns that occur at specific times of the year. By averaging the demand for each season over multiple years, businesses can determine how much product is typically needed and when demand peaks.

This information can be critical for inventory management, ensuring that companies have the right amount of stock to meet the expected demand during different seasons without overstocking or running into shortages. Understanding average seasonal demand helps in creating more accurate forecasts and improving the overall efficiency of the supply chain.

In contrast, estimating next year's total revenue, averaging total demand across all years, and forecasting weather conditions are not directly steps involved in the specific analysis of seasonal variations. While they may relate to broader forecasting and planning processes, they do not specifically address the computation of seasonal demand patterns.