Provides a method for visualizing the effects that one or more inputs (factors) have on the mean and variation of a process and on making subjective decisions about the process.

Answers the questions:
  • If I systematically change the level (setting) of one or more inputs, what happens to the mean of the process?
  • If I systematically change the level (setting) of one or more inputs, what happens to the variation of the process?
When to Use Purpose
Mid-project Helps to assess which inputs exert influence on either the mean or the variation of the process output.
Mid-project Good tool for communicating the effects of process inputs on the process output to project stakeholders.


Continuous Y, from one to four X-variables called factors (numeric or categorical). If factors are numeric, they must be controlled at specific levels.


  1. Verify the measurement systems for the Y data and the input X (or inputs) are adequate.
  2. 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).
  3. Enter the Y data in a single column.
  4. Enter factor levels into additional columns, one for each factor.
  5. All combinations of factor levels must have at least one data point.


  • For one factor, the multi-vari chart is the same as a main effects plot.
  • For two factors, the multi-vari chart is basically the same as an interaction plot, although it displays the data in a slightly different manner.
  • For three factors, the multi-vari chart is the best tool available for graphically exploring high-order interactions.
  • While the multi-vari chart will handle four factors, it not recommended because it becomes very difficult to interpret.
  • While the multi-vari chart does not show statistical significance of the effects, it is still a valuable tool for visually spotting differences that occur when you change one or more input variables.
  • You should run the same data through one of the other tools for analyzing data from designed experiments to ensure what you see is really worth investigating (in other words, it is statistically significant).
  • You typically use a multi-vari chart with data from a designed experiment with no restriction on the number of levels for each factor. Do not use it when you have collected response data and recorded the values of one or more uncontrolled inputs; instead, use regression for these cases.
  • 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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