Determining the exact value of a trend line may not always be necessary. In some cases, an approximation is sufficient for gleaning the general behavior of the data. If the data set is linear, the trend line is simply a line running through each point. For all other data sets, there is a simple strategy for approximating the trend line: draw a line that is situated at a minimal distance from each point while trying to pass through as many as possible, so that the number of points falling above and below the line is roughly equal. Here is an illustration of this strategy:
Looking at data over a number of years and finding patterns, you can use this information to extrapolate future patterns. A trend means that the same series of events is happening over and over. For example, if there is a trend of constant sales each year with a decrease of sales in winter that is offset by an increase in the summer, a person might extrapolate this to predict that sales will continue to be low in the winter. A store manager might use this information to offer additional products in the winter to help hedge against a drop in sales that time of year. However, forecasting isn't done quickly by just looking at a graph. Forecasters may translate the patterns of a graph into a formula to better predict what will happen in the future. They also may use spreadsheet software, which typically comes with built-in trend forecasting tools.