2005Encyclopedia of BiostatisticsRequires access

Graphical Displays

Michael A. Martin, A. H. Welsh

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Abstract

Abstract Graphical methods are fundamental tools of modern data analysis. Graphics are useful for communicating complex information quickly and easily, and are thus excellent tools for describing data and for providing direction in data analysis. They are essential tools for building statistical models from data, and for assessing model adequacy. This article surveys the use of statistical graphics in describing data and for statistical modeling, including models for relating variables, for spatiotemporal dependence, and for survival data. Graphics are also critical tools for visualizing and analyzing high‐dimensional data. The article illustrates the use of graphics in statistical modeling through numerous examples drawn from the life sciences.

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What this paper is about

Abstract Graphical methods are fundamental tools of modern data analysis. Graphics are useful for communicating complex information quickly and easily, and are thus excellent tools for describing data and for providing direction in data analysis. They are essential tools for building statistical models from data, and for assessing model adequacy. This article surveys the use of statistical graphics in describing data and for statistical modeling, including models for relating variables, for spatiotemporal dependence, and for survival data. Graphics are also critical tools for visualizing and analyzing high‐dimensional data. The article illustrates the use of graphics in statistical modeling through numerous examples drawn from the life sciences.

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Available abstract

Abstract Graphical methods are fundamental tools of modern data analysis. Graphics are useful for communicating complex information quickly and easily, and are thus excellent tools for describing data and for providing direction in data analysis. They are essential tools for building statistical models from data, and for assessing model adequacy. This article surveys the use of statistical graphics in describing data and for statistical modeling, including models for relating variables, for spatiotemporal dependence, and for survival data. Graphics are also critical tools for visualizing and analyzing high‐dimensional data. The article illustrates the use of graphics in statistical modeling through numerous examples drawn from the life sciences.

Key concepts: Statistical graphics, Graphics, Computer science, Statistical model, Computer graphics, Statistical analysis, Data mining, Graphical model

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