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Clustered multiple comparison graphs

Next selectClustered multiple comparison graphs


Clustered multiple comparison graphs allow to visualize the influence of two categorical variables on another (continuous) variable.

The categorical variables may contain character or numeric codes. These codes are used to break-up the data into different subgroups.

The graph can be composed from different elements: Bars, Horizontal lines, Markers and or Connecting lines for mean or median, with choice of different error bars for mean (95% CI, 1 SEM, 1 SD, 2 SD, 3 SD, range) or median (95% CI, 25-75 percentiles, 10-90 percentiles, 5-95 percentiles, 2.5-97.5 percentiles, 1-99 percentiles, range), Box-and-whisker plot or Notched box-and-whisker plot, Violin plot, and/or Dot plot (display all data).

How to enter data

You need to enter data for one continuous variable (MEASUREMENT1 in the example) and 2 categorical variables (GENDER and TREATMENT in the example).

Clustered multiple comparison graphs

Required input

Clustered multiple comparison graphs

The following need to be entered in the dialog box:


Bars representing means with error bars representing 95% confidence intervals:

Clustered multiple comparison graphs

Violin plot:

Violin plot

This is an example of a graph with option "Dots" selected:

Clustered multiple comparison graphs


See also

Recommended book

Book cover

Exploratory Data Analysis
John W. Tukey

Buy from Amazon

The approach in this introductory book is that of informal study of the data. Methods range from plotting picture-drawing techniques to rather elaborate numerical summaries. Several of the methods are the original creations of the author, and all can be carried out either with pencil or aided by hand-held calculator.