Summary

Graphically displays the average value of the output for multiple levels of 2-process inputs. The interactions plot displays the magnitude and direction of change in the output as you simultaneously change the levels of the 2-process inputs. You can also use it to plot standard deviations in a DOE to study the effects of 2-process inputs on process variation.

Answers the questions:
  • If I change two process inputs (factors) at the same time, is the effect on the process mean the same as it would be if I only changed one of the inputs?
  • If I change two process inputs (factors) at the same time, is the effect on the process variation the same as it would be if I only changed one of the inputs (only when plotting standard deviations in a DOE)?
  • What combination of settings of two key inputs results in the optimal process output?
When to Use Purpose
Mid-project Fixing two inputs at two or more different settings (levels) helps to determine which combinations of inputs have significant influence on the mean of the output.
Mid-project Fixing two inputs 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 Verify changes to inputs result in significant differences from the pre-project mean.
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.

Data

Continuous Y, two X's (numeric or categorical factors). If factors are numeric, they must be controlled at specific levels.

How-To

You can use an interaction 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 Stat > DOE > Factorial > Factorial Plots to generate the plot.
  • Without DOE, use Stat > ANOVA > Interactions Plot to generate the plot.

Enter the data as follows:

  1. Verify the measurement systems for the Y data and the inputs (factors) are adequate.
  2. Develop a data collection strategy (who should collect the data, as well as where and when; the preciseness of the data; how to record the data, and so on).
  3. Enter Y data in one column.
  4. Enter the 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 plots.

Guidelines

  • 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.
  • The interactions plot is often a key tool in identifying optimum process conditions when the results of a DOE show statistically significant interactions.
  • 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.
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