This example is dedicated to the study of sales data. Why would you want to study your sales data? Information is power and knowledge of your sales trends can help you plan your future expenses, your future resources, your future inventory levels, etc. As an example, let's study the sales of Company ABC.

Company ABC sells winter sports merchandise and has collected their quarterly sales data for the past 10 years. Looking at a plot of their quarterly sales (below), it is clear that their sales are increasing year over year, but there is a lot of up and down motion each quarter. We could use a straight line to forecast the upward sales trend, but this would not account for the quarterly seasonality of their business.

Using mathematical regression and time series techniques, we can develop a straight line that predicts the overall sales trend and subtract this line from the sales data. This leaves us just the quarterly movement. The results of an analysis of the seasonality are shown below. To read this graph, we look at both the vertical position of the bar and the height of the individual bars. The position of the bars shows that ABC company can expect its sales in the first and fourth quarters of any year to be higher than the sales for the second and third quarters. This is consistent with ABC's winter product focus. The height of the individual bars shows that over the years, there is more variation in the sales data for the first quarters and the third quarters and less variation in the second and fourth quarters.

Further analysis can provide an indication of the percentage of volatility in each of the quarters. Both the trended line (straight line) and the analysis of the seasonality data can be combined to provide an overall model to predict future sales.