Quickly and easily allows the graphical evaluation of relationships between all pairs of variables in a larger group of variables. Each pairwise combination appears in a separate panel.

Answers the question:

- Do any relationships exist between any pairs of variables in a large set?

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

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

Mid-project | Evaluate two inputs to identify whether they duplicate information. For example, inputs of Degree Obtained and Years of School are likely to explain the same variation of the output, so one of them may be eliminated. This evaluation is used primarily in multiple regression with many variables. |

Two or more numeric variables (X's, Y's, or any combination of these).

- Collect your numeric data and enter them in Minitab, one column per variable.

- The matrix plot is a collection of scatterplots. If you try to plot too many variables at once, the size of the graphics make the interpretation difficult. In this case, you can use individual scatterplots.
- The matrix plot does not include any measures of correlation. Therefore, you should use the matrix plot to look at a larger group of variables all at once, and identify the pairs that appear to have stronger relationships. Then, investigate these pairs one at a time using the fitted line plot or the correlation tool.