That’s not going to be a hard call, right?
But in general, we tend to prefer a standard deviation (or a standard deviation within a standard deviations) that’s around 1 or 2. These two approaches are based on common sense. The standard deviation, or difference between the starting point in a graph and the endpoint, is like an “average” you can usually measure with an interval. And a standard deviation within a standard deviation takes the average and subtracts one from the other. That way you can get a nice “window” into the data and see the trends. You can then make decisions about the best standard deviation to use next time you’re making a series of observations.
For example… when comparing multiple months, take a look at your chart. Did the first month be unusually high? Was the peak one month after the peak? There’s usually some indication of a trend that is going on, so you can make an educated guess about what that is. With just a single month, it seems like we’re only measuring one trend, but if you look at long period of data, you might be able to make a judgment about the trend’s direction and its timing.
When comparing multiple years…
As someone who is interested in a wide variety of data sets (the data from the web, the data from the web for companies he owns, the data for companies he does not own, and the data for companies he does own), it seems to me that there are many good ways to evaluate moving averages. Here are a few that I’ve used in the past:
The average is best when a smooth line is in the center and is the closest to what you see. The moving average is best when it is as close to a straight line as you can get. The moving average is best when the moving line is a straight line and has a slope of a smooth line.
The standard deviation is the middle line — it’s not the most popular, but if you have data that is very noisy, it’s a good choice.
The standard deviation is the closest — a nice “window” — to what you see. It’s usually not a perfect match to what you see, but it helps. It’s the median value that is ideal, as long as you can get a standard deviation above it. Here are some examples that I’ve used:
I also have a blog post about this.
Image caption Sir Michael Fallon is a former ambassador to
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