Moving averages are averages calculated from artificial subgroups of consecutive observations. In control charting, you can create a moving average chart for time-weighted data. In time series analysis, Minitab uses moving average to smooth data and reduce random fluctuations in a time series.
For example, an office products supply company monitors inventory levels each day. They want to use moving averages of length 2 to track inventory levels to smooth the data. They collect data collected for 8 days for one of their products.
Day | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
---|---|---|---|---|---|---|---|---|
Inventory Level | 4310 | 4400 | 4000 | 3952 | 4011 | 4000 | 4110 | 4220 |
Moving Average | 4310 | 4355 | 4200 | 3976 | 3981.5 | 4005.5 | 4055 | 4165 |
The first moving average is 4310, which is the value of the first observation. (In time series analysis, the first number in the moving average series is not calculated; it is a missing value.) The next moving average is the average of the first two observations, (4310 + 4400) / 2 = 4355. The third moving average is the average of observation 2 and 3, (4400 + 4000) / 2 = 4200, and so on. If you want to use a moving average of length 3, three values are averaged instead of two.