Graphically displays the average value of the output for multiple levels of a given single input. The main effects plot displays the magnitude and direction of change in the output as you change the value of the input. You can also use it to plot standard deviations in a DOE to study the effects of an input on process variation.

Answers the questions:

- If I change an input from one level to another, does the mean of the process stay the same or does it change?
- If I change an input from one level to another, does the variation of the process stay the same or does it change (only when plotting standard deviations in DOE)?
- What setting of the process input results in the optimal process output?

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

Mid-project | Fixing an input at two or more different settings (levels) helps to determine which inputs have significant influence on the mean of the output. |

Mid-project | Verify changes to inputs result in significant differences from the pre-project mean. |

Mid-project | Fixing an input at two or more different settings (levels) and recording the standard deviation of the output at each setting helps to determine which inputs have significant influence on the process variation. |

Mid-project | Used as a graphical aid when using ANOVA or with a DOE. |

Mid-project | Good tool for communicating the effects of process inputs on the process output to project stakeholders. |

Continuous Y, a single X at two or more levels.

You can use a main effects plot with experimental data with or without designed experiments (DOEs):

- With DOE, the Y data and factor data should already be in the worksheet. In this case, use to generate the plot.
- Without DOE, use to generate the plot.

You should enter the data as follows:

- Verify the measurement systems for the Y data and the inputs (factors) are adequate.
- Develop a data collection strategy (who should collect the data, as well as where and when; how many data values are needed; the preciseness of the data; how to record the data, and so on).
- Enter Y data in one column.
- Enter factor levels into additional columns, one for each factor.
- If you have additional columns for the levels of additional factors (X's), Minitab creates and tiles the multiple main effects plots.

- Any significant interaction takes precedence over the main effects of the two factors involved in the interaction. For example, if you have two factors (A and B) and the AB interaction is significant, you should evaluate the A and B settings using the interactions plot and not the main effects plot.
- If you have discrete numeric data from which you can obtain every equally spaced value and you have measured at least 10 possible values, you can evaluate these data as if they are continuous.