Data considerations for Trend Analysis

To ensure that your results are valid, consider the following guidelines when you collect data, perform the analysis, and interpret your results.

Record data in chronological order

Time series data are collected at regular intervals and are recorded in time order. You should record the data in the worksheet in the same order that you collect it. If the data are not in chronological order, you cannot assess time-related patterns in the data. However, you can still use Scatterplot to investigate the relationship between a pair of continuous variables.

Collect enough data to assess trends or patterns

Collect enough data so that you can fully assess trends or patterns in the data. For example, you need enough data to be sure that any pattern you observe is a long-term pattern and not just a short-term anomaly.

Collect data at appropriate time intervals

Choose the time interval based on the patterns that you want to detect. For example, to look for month-to-month patterns in a process, collect data at the same time each month. If you collect data each week, then the monthly pattern may be lost in the noise of the weekly data. If you collect data each quarter, the monthly pattern may be lost when it is averaged out in each quarter.

If you are looking only for general trends or shifts in the data over time, and not for patterns associated with a specific time interval, the length of the interval is less important.

Your data should have a trend with no seasonal component

If your data do not have a trend and do not have a seasonal component, use Moving Average or Single Exponential Smoothing. If your data have a seasonal component, with or without a trend, use Decomposition or Winters' Method.

The trend should follow a consistent shape without shifts or reversals

If your data have cyclical movements, shifts, or reversals in the trend, use Double Exponential Smoothing because it uses a dynamic trend component that works well with changes in the trend.