Provides a static picture of the location and spread of the Y variable (the process output) by showing the minimum and maximum values, first quartile (25% of points are less than this value), third quartile (75% of points are less than this value), median (or mean), and potential outliers. If you also include a categorical X variable, you can look at the location and spread of the Y at each level (for example, 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 contain any unusual data points (outliers)?
  • Does changing the level of an input variable (X) affect the location or the spread of the output Y?
When to Use Purpose
Mid-project The first rule in data analysis is to always plot your data before running any statistical tests. The boxplot is a logical choice for comparison tests where 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).


  1. Choose from one of two common data layouts that you can use with boxplots:
    1. Choose Boxplot with groups (stacked data) when you enter one column for the Y variable and one for the X (categorical) variable (optional). Note: You can have up to four categorical variables. Minitab draws a separate box for each combination of levels of the categorical variables; however, the boxes all appear in the same graph window. This display is handy for making comparisons across levels of X variables.
    2. Choose Boxplot with multiple Y's (unstacked data) when you enter the Y data into a separate column for each level of the X variable. Minitab draws a separate box for each Y. The boxes can be plotted either in separate graph windows or in the same graph window with a common scale.


  • The boxplot is very prone to misinterpretation when the sample size is small. When the sample size is less than 20, use a dotplot or individual value plot.
  • The boxplot provides a good visual comparison even when the number of levels of an X variable are high. If the number of levels of an X variable is greater than five, the boxplot provides a better visual comparison than the dotplot (assuming more than 20 points per category).
By using this site you agree to the use of cookies for analytics and personalized content.  Read our policy