Most uses of moving/rolling averages are applied to time series data, say a series of daily sales. I’m an engineer (and MBA) who taught business maths in both British And US colleges and universities. Math-competent folk, feel free to leave endless comments about how I just mangled your favorite concept… That’s why rolling averages are so useful: Apply them at the right time and you can get an idea of emerging trends, even if those trends are happening because of sudden jumps in your data. Use a rolling average, though, and you start to see a pattern emerge, with peaks happening more and more often: It looks like my website got a case of the hiccups. That’s not terribly helpful as a trend detector. Using the data from above, you get a graph that looks like this: Why Use a Rolling Average?Ī rolling average can help you find trends that would otherwise be hard to detect. If you want to learn more complex rolling averages, read the Wikipedia page. That’s a simple rolling average.Īnd trust me, I stop at ‘simple’. See how that works? I’m just taking the average of the last 7 rows, all the way down the column. The column on the right is the rolling average: Here’s my definition of a simple rolling average: An average of the last n values in a data set, applied row-by-row, so that you get a series of averages. Maybe it made sense to you, but to me it’s total mathinese. My first reaction when I read a definition like that was, “Buh?”. What’s a Rolling Average?Ī simple rolling average (also called a moving average, if you wanted to know) is the unweighted mean of the last n values. If you work in marketing, you need to know it. But I’m writing this anyway, partly because this is analytics 101. So, when I click ‘publish’ for this post, I’m going to flinch a little, waiting for someone who’s actually math-competent to call me out as the fraud that I am. By the time I was 13 they’d both given up on any dreams of me being a math genius and instead hoped I’d finally count to 20 without removing my shoes.
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