A residual is the difference between the actual data values and the trendline (see Trendlines) that you think represents the nature of the data. We have covered residuals previously (see Anomalies) as a useful tool for visualizing changes in data that might not be obvious from the raw data. Another way to think about residuals is a more advanced form of the relative change translation we covered yesterday.
For example, consider these two metrics which are changing over time:
There are some blips and trends, but it’s unclear how the two metrics are moving in relation to each other because the scale is so different. Instead of using a relative change chart, let us try charting just the residuals for the two metrics. For a trendline, I’ll use a 7-day moving average (see Moving Average Trendlines).
The residuals of both metrics tell an interesting story:
Both metrics changed significantly on 2/12/17, as indicated by the significant dip in the middle! This change was almost entirely hidden by the raw data, but is clear when looking at the residuals.
Translating multiple metrics into their residuals help identify these kinds of changes because you’re comparing the metrics to themselves, so any deviation from their typical behavior will become clear. Note that it is almost impossible to understand the nature of the actual data from the residual, so you should accompany any chart of a residual with the actual data it is derived from.
There are dozens of other translations we could cover this week, but tomorrow we will end with a warning about what happens when you take metric translation too far.
Quote of the Day: “Luck is the residue of design.” ― John Milton