# Individual Value Plot

## Summary

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.

• 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.

### Data

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

## How-To

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

1. For one sample of data, choose Graph > Individual Value Plot > One Y – Simple.
2. For stacked data (one column for the Y-variable and one for the X-variable), choose Graph > Individual Value Plot > One Y – With Groups.
3. For unstacked data (separate columns for each value of the X-variable), choose Graph > Individual Value Plot > Multiple Y's - Simple.

## Guidelines

• 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).
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