Provides a graphical means for assessing and communicating the dynamic (time-based) behavior of a process input (or process output) and for evaluating the dynamic effects on the output as process inputs change.

Answers the questions:

- What is the dynamic behavior of a process variable (input or output)?
- Does the mean of the process output change at different levels of a process input?
- Does the variation of the process output change at different levels of a process input?
- Do the dynamic patterns of the process output change at different levels of a process input?

When to Use | Purpose |
---|---|

Start of project | Assist in project selection by identifying process outputs that exhibit shifts, changes in variation, or changes in time-based patterns in response to changes in process inputs. |

Mid-project | Graph the data before running any statistical tests. This is the first rule of data analysis. Time series plots are often used to investigate the effects of making controlled changes to a process input, with the data collected at specified time intervals. |

Mid-project | Assess whether an input (X) has a strong relationship with an output (Y) to help eliminate noncritical X's from consideration. |

End of project | Graphically compare the dynamic behavior of the pre-project process with the dynamic behavior of the post-improvement process. |

One numeric variable (continuous or discrete) with optional categorical variables.

- Verify the measurement system for the Y data is adequate.
- Establish a data collection strategy to determine the best time interval for collecting data.
- Enter data for each Y-variable into a single column.
- Place optional categorical variables in additional columns. These categorical variables identify process segments with specific input conditions so you can make comparisons across segments. The resulting graph sequentially plots the data for each level (or combination of levels) of the X-variable.

- The data for a time series plot must be collected at equally spaced intervals in time. If the time intervals are not equally spaced, the patterns you observe in the plot can be very misleading.
- You can use categorical (group) variables with time series plots to show the effects of different levels of a factor. For example, if you want to examine hourly yield per FTE of a forms processing operation (Y) to detect differences between shifts, you can use the shift as a group variable (factor) and evaluate changes in the mean, variation, or within-shift patterns between the three shifts.
- Minitab allows up to three categorical (group) variables.
- If you have one factor (categorical variable) and the sample size within each level of the factor is at least 20 observations, you may also use an I chart (or I-MR chart) to display the dynamic behavior of the process output. The I chart (or I-MR chart) includes a center line and control limits for each level, allowing you to directly compare means and variation between levels much more easily than in a time series plot.