Andrew Gelman, Deborah Nolan · Oxford University Press eBooks · 2017
A statistical graph can offer an alternative compelling approach to teaching statistical thinking, but making good statistical graphs is hard to do. Each step in the process (e.g., change in scale, transform a variable, select colors, add a reference marker) engages students in better understanding data and models. However, this creative process is not easily encapsulated in a textbook. Since it is relatively easy to make a basic plot with statistical software, we can engage students in activities around making statistical graphs. This chapter provides guiding principles and lecture topics for teaching data visualization. The chapter contains exercises to deconstruct and reconstruct a plot, create a plot to reveal an important feature of the data, and turn a table of numbers into a plot.
Cited by 0
Martin Theus · Statistics and Computing · 2007
Cited by 2
Albert Vexler, Alan D. Hutson, Xiwei Chen · 2017
Cited by 0
W. John Braun, Duncan J. Murdoch · Cambridge University Press eBooks · 2016
Users of statistical computing need to produce graphs of their data and the results of their computations. In this chapter we start with a general overview of how this is done in R, and learn how to draw some basic plots. We then discuss some of the issues involved in choosing a style of plot to draw: it is not always an easy choice, and there are plenty of bad examples in the world to lead us astray. Finally, we will go into some detail about how to customize graphs in R.
Cited by 0
Frank E Harrell · 2010
The ability to construct clear and informative graphs is related to the ability to understand the data. There are many excellent texts on statistical graphics (many of which are listed at the end of this chapter). Some of the best are Cleveland’s 1994 book The Elements of Graphing Data and the books by Tufte. The suggestions for making good statistical graphics outlined here are heavily influenced by Cleveland’s 1994 book. 1.1 Graphical Perception • Goals in communicating information: reader perception of data values and of data patterns. Both accu-
Cited by 0
Carla Goad · 2020
Cited by 0
G. Einon · Trends in Neurosciences · 1986
Cited by 135
William S. Cleveland · Journal of the American Statistical Association · 1987
Abstract Three active areas of research in statistical graphics are methodology, computing, and graphical perception. The five articles in this special section provide a good representation of these areas. Dynamic graphical methods, which involve a tight weaving of methodological and computing issues, are a particularly exciting topic of research; dynamic graphics will soon have a major impact on how data are analyzed. Evaluation of graphical inventions is difficult. Certain visualization issues can be evaluated by studies in graphical perception. At the moment, however, the performance of most graphical methods can be studied only by rigorous field testing in which the methods are used to analyze a large number of data sets. A bibliography is provided to allow readers to begin a study of any of the major areas of statistical graphics.
Cited by 48
2012
For a Web download or e-book: Your use of this publication shall be governed by the terms established by the vendor at the time you acquire this publication. U.S. Government Restricted Rights Notice: Use, duplication, or disclosure of this software and related documentation by the U.S. government is subject to the Agreement with SAS Institute and the restrictions set forth in FAR 52.227-19, Commercial Computer Software-Restricted Rights (June 1987).
Cited by 0
2010
SAS ® 9.1 includes an experimental feature, ODS Statistical Graphics (ODS Graphics for short), which introduces more than 30 procedures in four SAS products. This new feature enables the procedures to create statistical graphs automatically as part of ODS output. With simple ODS Graphics statements, a procedure creates graphs that are commonly needed for data analysis. The default ODS Graphics provide informative graphs with important statistics printed alongside. Unfortunately, the default ODS Graphics are not suitable for the study reports, publications, or New Drug Application (NDA) submissions. A typical challenge is that the ODS Graphics are missing study specific titles and footnotes. To resolve this issue, the modification of default ODS Graphics is necessary. ODS Graphics appearances are governed by the ODS Graphical Template Language (GTL). The modification of ODS Graphics can be made by three sources: 1) from the data set that is used for the procedure execution, 2) from changing the style template, and 3) from modification of the graph template. This paper discusses several methods and tips to create customized ODS Graphics and how to modify system default templates or create stand along template. The discussion focuses in four topics: 1) A macro facility for users to easily supply titles and footnotes, 2) Overlaying two ODS Graphics, 3) Modification of the ODS Graphics elements, and 4) Creating stand alone template. Some sample code and output are included in the paper for illustration purposes.
Cited by 0
Lawrence H. Cox · 1981
Cited by 1
Susan VanderPlas · 2015
Cited by 0Open access
Eric R. Ziegel · Technometrics · 1993
Cited by 56
Matange, Sanjay, Heath, Dan · CERN Document Server (European Organization for Nuclear Research) · 2014
Cited by 0
Lawrence S. Hamilton · University of New Hampshire Scholars Repository (University of New Hampshire at Manchester) · 1986
The power and availability of desktop computers have increased dramatically in the last three years. Historically, sociological research has been tied closely to the evolution of mainframe computers. New developments in microcomputing are now also beginning to affect the discipline. Although objectively less powerful than mainframes, microcomputers offer a number of advantages besides size. One of these advantages is the ease, and low cost, of working interactively with analytical graphics. New graphics programs represent a significant advance over anything previously available for statistical analysis. This paper illustrates one such program, giving examples of its possible applications for sociological research.
Cited by 0
Theus, Martin · OPUS (Augsburg University) · 1996
Cited by 0
Wilhelm, Adalbert · OPUS (Augsburg University) · 1996
Cited by 0
2010
For a hard-copy book: No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, or otherwise, without the prior written permission of the publisher, SAS Institute Inc. For a Web download or e-book: Your use of this publication shall be governed by the terms established by the vendor at the time you acquire this publication. U.S. Government Restricted Rights Notice. Use, duplication, or disclosure of this software and related documentation by the U.S. government is subject to the Agreement with SAS Institute and the restrictions set forth in FAR 52.227-19 Commercial Computer Software-Restricted Rights (June 1987).
Cited by 0
Achim Zeileis, Kurt Hornik, Paul Murrell · 2016
Integral element in graphical displays. Easily available in statistical software. Omnipresent in (electronic) publications: technical reports, electronic journal articles, presentation slides. Problem: Little guidance about how to choose appropriate colors for a particular visualization task. Question: What are useful color palettes for coding qualitative and quantitative variables? Challenges Basic principles: Colors should be intuitive, avoid large areas of saturated colors. Purpose: Distinguish different elements of a statistical graphic depending on the levels of some variable. Control of perceptual properties: hue, brightness, colorfulness. Employ a color model or color space. RGB (Red-Green-Blue): Corresponds to generation of colors on computer, unintuitive for humans. HSV (Hue-Saturation-Value): Simple transformation of RGB, easily available. But: Maps poorly to perceptual properties, encourages use of highly saturated colors. HCL (Hue-Chroma-Luminance): Transformation of CIELUV space, mitigates problems above. Ideally, colors should work for: Screen, projector, (grayscale) printer, color-blind viewers,...
Cited by 0
Fernando Marmolejo‐Ramos · Revista Colombiana de Estadística · 2014
Traditionally, statistical graphics have been associated with the representation and exploration of data. For example, in meteorology, wind boxplots (also known as wind roses) are used to depict distributions of wind speed and directional data (e.g. see Figure see Figure As with statistical tests, statistical graphics are ubiquitous across many scientific fields. For example, just as ANOVA is used in different research areas, boxplots are also found in different research areas (e.g. boxplots used in neuroscience; Ghosh, Kakunoori, Augustinack, Nieto-Castanon, Kovelman, Gaab, Christodoulou, Triantafyllou, Gabrieli & Fischl, 2010, Figure However, statistical graphics go beyond the selection and representation of data, they are also used to represent statistical concepts and models (indeed, the colours used in visual displays do have an influence on the interpretation of statistical models; e.g. Statistical graphics can also be used to test assumptions, select estimators, and detect outliers.
Cited by 3Open access