Statistical Graphics for Exploring Data, Presenting Information, and Understanding Statistical Models

Graphical methods are being increasingly used for exploratory data analysis. Some of the many graphical tools that are useful in this setting are scatterplot matrices, nonparametric smoothers, and tree diagrams. Statistical graphics for presenting information have been used much longer, but most of the commonly used graphics used in papers, presentations, and the popular media, such as bar charts and pie charts, are either poor or misleading in communicating information to the reader. This short course begins with a series of graphical horror stories from the scientific and lay press. Then elements of graphical perception and good graph construction, many from the writings of Bill Cleveland, are covered. Practical suggestions for choosing the best chart or graph type, making good and clear graphics, and formatting are covered. Techniques for simultaneous presentation of multiple variables are described.

Complex outcome or risk adjustment models are not easily grasped by non-statisticians. Special graphics such as effect charts and nomograms can assist physicians and other consumers of statistical analysis in understanding statistical models and in using them for obtaining predictions for individual subjects. Examples of model presentation graphics will be given.

At the close of the short course some graphical marvels from the literature (especially from Edward Tufte and Howard Wainer) are presented.

-- FrankHarrell - 04 Feb 2004
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Topic revision: r1 - 03 Feb 2004, FrankHarrell

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