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Graphical exploratory data analysis

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Published by Springer-Verlag in New York .
Written in English


  • Statistics -- Graphic methods.

Book details:

Edition Notes

StatementS.H.C. du Toit, A.G.W. Steyn, R.H. Stumpf.
SeriesSpringer texts in statistics
ContributionsSteyn, A. G. W., Stumpf, R. H.
LC ClassificationsQA276.3 .D778 1986
The Physical Object
Paginationix, 314 p. :
Number of Pages314
ID Numbers
Open LibraryOL2711253M
ISBN 100387963138
LC Control Number86004009

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Get this from a library! Graphical Exploratory Data Analysis. [S H C Toit; A G W Steyn; R H Stumpf] -- Portraying data graphically certainly contributes toward a clearer and more penetrative understanding of data and also makes sophisticated statistical data analyses more marketable. This realization Additional Physical Format: Online version: Du Toit, S.H.C. Graphical exploratory data analysis. New York: Springer-Verlag, © (OCoLC)    15 Exploratory data analysis: graphical summaries ergaard [39], p). The term histogram appears to have been used first by KarlPearson([22]). Figure displays a histogramof the Old Faithful data. The picture immediately reveals the asymmetry of the dataset and the fact that the elements accumulate somewhere near and , which ~cs/Resources/ GRAPHICAL DATA ANALYSIS Howard Wainer) (RR) + Bureau of Social Science Research, Washington, D.C. , and Educational exploratory rather than confirmatory. The past 10 years have seen a vigorous development of methods, by the and so dating thestart of movement of informal data analysis by that book's publication is a bit

  Exploratory Data Analysis的话题 (全部 条) 什么是话题 无论是一部作品、一个人,还是一件事,都往往可以衍生出许多不同的话题。将这些话题细分出来,分别进行讨论,会有更多收获    graphical exploratory data analysis springer texts in statistics Posted By Edgar Rice BurroughsPublishing TEXT ID ea Online PDF Ebook Epub Library Graphical Exploratory Data Analysis Book Worldcat   This book covers the essential exploratory techniques for summarizing data with R. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data you   Exploratory data analysis techniques have been devised as an aid in this situation. Most of these techniques work in part by hiding certain aspects of the data while making other aspects more clear. Exploratory data analysis is generally cross-classi ed in two ways. First, each method is either non-graphical or graphical. And second, each ~hseltman//Book/

  data is of limited availability. Therefore, in addition to some contrived examples and some real examples, the majority of the examples in this book are based on simulation of data designed to match real experiments. I need to say a few things about the difficulties of learning about experi-mental design and ~hseltman//Book/   Exploratory Data Analysis - Detailed Table of Contents [1.] This chapter presents the assumptions, principles, and techniques necessary to gain insight into data via EDA-- exploratory data ://   variable or the data. The graphical presentation of data is very important for both the analysis of the variables and for the presentation of the findings that emerge from the data. As a result, a good deal exploratory data analysis involves graphing and plotting data, both single variables and multiple-variable data ://?article=&context=pspubs. The primary aim with exploratory analysis is to examine the data for distribution, outliers and anomalies to direct specific testing of your hypothesis. It also provides tools for hypothesis generation by visualizing and understanding the data usually through graphical representation [ 1 ].