Data considerations for Area Graph

For the graph to represent your data most effectively, consider the following guidelines.

Record data in chronological order

An area graph displays time series data, which should be collected at regular intervals and recorded in time order. Record the data in the worksheet in the same order that you collect it. If the data are not in chronological order, you cannot use an area graph to assess time-related patterns in the data.

Collect data at regular time intervals

Time series data assumes that the data are collected at regular intervals, such as once a day, or once a month. If you collect data at irregular intervals, then the time series data shown on the area graph may be misleading.

If you collect data at irregular intervals, consider using a scatterplot. For example, if you collect data on days 1, 2, 4, 8, and 16, then you can use a scatterplot to plot the measurement data on the y-axis and the number of days (1, 2, 4, 8, and 16) on the x-axis.

Collect data at appropriate time intervals

Collect the data at intervals that best reveal the patterns that you want to detect. For example, if you believe that there are differences in the data between months and that these differences are likely to repeat each year, you should collect monthly data to reveal the pattern over multiple years. If you collect data each week, the pattern between months may be lost in the "noise" of the weekly data. Similarly, 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.

The sample data should be selected randomly
In statistics, random samples are used to make generalizations, or inferences, about a population. If your data were not collected randomly, your results may not represent the population.
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.
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