Provides a static picture of the location and spread of the Y-variable (the process output). Each dot represents the actual location of a data point in the sample (the points are offset symmetrically on the plot so you can see multiple points in the same location). If you also include a categorical X-variable, you can look at the location and spread of the Y at each level, or factor setting, of the X-variable.

Answers the questions:

- What is the general location of the Y data?
- How wide is the spread of the Y data?
- Does the sample have any unusual data points (outliers)?
- If you change the level of an input variable (X), is the location or spread of the output Y affected?

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

Mid-project | The first rule in data analysis is to always plot your data before running any statistical tests. The individual value plot is a logical choice for any comparison tests in which you are looking at what happens to the process output under various conditions, such as changes to a process input. |

Mid-project | Assess if an input (X) has an impact on the process mean or process variation and help eliminate noncritical X's from consideration. |

Mid-project | Identify levels (settings) of the process input that have the desired impact on the output mean or variation. |

Mid-project | Communicate the effects of process inputs on the process output to project stakeholders. |

Numeric Y, with optional discrete X (categories for comparison).

You can display individual value plots in one of three common layouts.

- For one sample of data, choose .
- For stacked data (one column for the Y-variable and one for the X-variable), choose .
- For unstacked data (separate columns for each value of the X-variable), choose .

- You can use the individual value plot with up to three nested X-variables. For example, you can plot sales by store, day of the week, and time of day.
- The individual value plot does not provide statistical evaluation of possible outliers. When you have sufficient data (a sample size greater than 20), use the boxplot.
- The individual value plot does not provide a good visual comparison when the number of levels of an X-variable are high. If the number of levels of an X-variable is greater than 10, the boxplot provides a better visual comparison than the individual value plot (assuming greater than 20 points per category).