On the Data tab of the Single Exponential Smoothing dialog box, specify the data for the analysis, specify the time scale, and enter the weight for smoothing.

In Y variable, enter a column of numeric data that were collected at regular intervals and recorded in time order. If your data are in multiple columns (for example, you have data for each year in a separate column), you must stack the data into a single column.

In this worksheet, Sales contains the number of computers that are sold each month.

C1 |
---|

Sales |

195000 |

213330 |

208005 |

249000 |

237040 |

(Optional) Enter a column to label the x-axis with values, such as dates. If you don't enter a column, Minitab labels each time period with an integer starting at 1.

The weight adjusts the amount of smoothing by defining how each component reacts to current conditions. Usually, you want to smooth the data enough to reduce the noise (irregular fluctuations) so that the pattern is more apparent. However, don't smooth the data so much that you lose important details.

If you do not know a good weight, use the default value. Then, you can increase or decrease the weight after you examine the time series plot. Usually, a weight between 0 and 1 works well. Use smaller weights for noisy data so that the smoothed values don't fluctuate with the noise. To calculate naive forecasts, use a weight of 1. For more information on naive forecasting, go to Forecasting with time series analysis and click "What is naive forecasting?".

Lower weights give less weight to recent data, which produces a smoother line. Higher weights give more weight to recent data, which produces a less smooth line.