{"id":10283,"date":"2021-03-17T13:01:57","date_gmt":"2021-03-17T04:01:57","guid":{"rendered":"http:\/\/macroscope.world.coocan.jp\/yukukawa\/?p=10283"},"modified":"2021-03-17T14:54:24","modified_gmt":"2021-03-17T05:54:24","slug":"william-cleveland-1985-the-elements-of-graphing-data-%e5%88%9d%e7%89%88%e3%81%ae%e6%9a%ab%e5%ae%9a%e3%83%a1%e3%83%a2","status":"publish","type":"post","link":"http:\/\/macroscope.world.coocan.jp\/yukukawa\/?p=10283","title":{"rendered":"William Cleveland (1985) The Elements of Graphing Data (\u521d\u7248\u306e\u66ab\u5b9a\u30e1\u30e2)"},"content":{"rendered":"<ul>\n<li>William S. <strong>Cleveland,<\/strong> 1985: T<em>he Elements of Graphing Data<\/em>. Monterey CA USA: Wadsworth Advanced Books and Software, 323 pp. ISBN 0-534-03729-1 (cloth), 0-534-03730-5 (paper). <a href=\"http:\/\/macroscope.world.coocan.jp\/ja\/reading\/cleveland\/index.html\">[\u8aad\u66f8\u30ce\u30fc\u30c8] <\/a><\/li>\n<\/ul>\n<p>\u6570\u91cf\u30c7\u30fc\u30bf\u3092\u30b0\u30e9\u30d5\u306b\u3057\u3066\u3057\u3081\u3059\u65b9\u6cd5\u306b\u3064\u3044\u3066\u306e\u6559\u79d1\u66f8\u7684\u306a\u672c\u3002\u76f4\u63a5\u306b\u306f\u79d1\u5b66\u8ad6\u6587\u306b\u306e\u305b\u308b\u56f3\u3092\u60f3\u5b9a\u3057\u3066\u3044\u308b\u304c\u3001\u305d\u308c\u306b\u304b\u304e\u3089\u305a\u6709\u52b9\u306a\u3053\u3068\u304c\u66f8\u3044\u3066\u3042\u308b\u3068\u601d\u3046\u3002\u672c\u306e\u5927\u304d\u3055\u306fA4\u5224\u3067\u3001\u30cf\u30fc\u30c9\u30ab\u30d0\u30fc\u306f\u6697\u3044\u8d64(\u3048\u3093\u3058\u8272\u3068\u3044\u3046\u3079\u304d\u304b)\u3001\u30da\u30fc\u30d1\u30fc\u30d0\u30c3\u30af\u306f\u7070\u8272\u307e\u3058\u308a\u306e\u8336\u8272(\u30ab\u30fc\u30ad\u8272\u3068\u3044\u3046\u3079\u304d\u304b)\u306e\u8868\u7d19\u3060\u3002\u8457\u8005\u306f Bell Laboratories \u306b\u3064\u3068\u3081\u3066\u3044\u3066\u3001Tukey \u306e\u5f71\u97ff\u3092\u5f37\u304f\u3046\u3051\u3066\u3044\u308b\u3002<\/p>\n<p>\u308f\u305f\u3057\u306f\u30011980\u5e74\u4ee3\u306e\u3046\u3061\u306b\u30da\u30fc\u30d1\u30fc\u30d0\u30c3\u30af\u3092\u8cb7\u3063\u3066\u3001\u81ea\u5206\u306e\u52c9\u5f37\u306b\u3082\u6559\u6750\u3065\u304f\u308a\u306b\u3082\u4f7f\u3063\u305f\u3002\u3053\u306e\u8aad\u66f8\u30d6\u30ed\u30b0\u3092\u306f\u3058\u3081\u308b\u307e\u3048\u306e2003\u5e74\u306b[\u8aad\u66f8\u30ce\u30fc\u30c8] (\u3053\u306e\u30da\u30fc\u30b8\u306e\u4e0a\u306e\u307b\u3046\u304b\u3089\u30ea\u30f3\u30af\u3057\u305f) \u3092\u66f8\u3044\u305f\u3002\u3069\u3093\u306a\u672c\u304b\u306e\u7d39\u4ecb\u306f\u3001\u3072\u3068\u307e\u305a\u3001\u305d\u3061\u3089\u3092\u898b\u3066\u3044\u305f\u3060\u304d\u305f\u3044\u3002<\/p>\n<p>\u305d\u306e\u5f8c\u3001\u672c\u3092\u306a\u304f\u3057\u3066\u3057\u307e\u3063\u305f\u30021994\u5e74\u306e\u65b0\u7248(\u66f8\u8a8c\u60c5\u5831\u306f[\u8aad\u66f8\u30ce\u30fc\u30c8]\u306b\u66f8\u3044\u305f)\u306f(\u7bb1\u3065\u3081\u3057\u3066\u3057\u307e\u3063\u305f\u304c)\u6301\u3063\u3066\u3044\u308b\u306f\u305a\u3060\u304c\u3001\u5185\u5bb9\u304c\u3060\u3044\u3076\u5909\u308f\u3063\u3066\u3057\u307e\u3063\u3066\u3044\u3066\u3001\u308f\u305f\u3057\u304c\u898b\u305f\u3044\u306e\u306f\u521d\u7248\u306a\u306e\u3060\u30022021\u5e743\u6708\u306b\u601d\u3044\u305f\u3063\u3066\u3001\u53e4\u672c\u3092\u53d6\u308a\u3088\u305b\u305f\u3002\u3053\u3093\u3069\u306f\u30cf\u30fc\u30c9\u30ab\u30d0\u30fc\u3060\u3002<\/p>\n<p>\u3068\u3069\u3044\u305f\u672c\u306b\u4e8c\u3064\u6298\u308a\u306e\u6570\u679a\u306e\u7d19\u304c\u306f\u3055\u307e\u3063\u3066\u3044\u305f\u3002\u3064\u304e\u306e\u8ad6\u6587\u306e\u30b3\u30d4\u30fc\u3060\u3063\u305f\u3002\u8457\u8005\u304c\u958b\u767a\u3057\u305f\u3001\u6563\u5e03\u56f3\u306e\u70b9\u7fa4\u306b\u304a\u304a\u307e\u304b\u306b\u3042\u3066\u306f\u307e\u308b\u66f2\u7dda\u3092\u3072\u304f LOWESS \u3068\u3044\u3046\u65b9\u6cd5\u306b\u3064\u3044\u3066\u66f8\u3044\u305f\u3082\u306e\u3067\u3001\u308f\u305f\u3057\u3082\u30b3\u30d4\u30fc\u3092\u53d6\u308a\u3088\u305b\u305f\u304a\u307c\u3048\u304c\u3042\u308b\u306e\u3060\u304c\u305d\u308c\u3082\u898b\u5931\u3063\u3066\u3044\u305f\u306e\u3067\u3001\u3042\u308a\u304c\u305f\u3044\u3002(R\u306b\u306f\u304f\u307f\u3053\u307e\u308c\u3066\u3044\u308b\u306f\u305a\u3060\u304c\u3001\u305d\u308c\u3092\u3064\u304b\u3046\u306b\u3057\u3066\u3082\u539f\u7406\u3092\u77e5\u3063\u3066\u304a\u304d\u305f\u3044\u306e\u3060\u3002)<\/p>\n<ul>\n<li>William S. <strong>Cleveland<\/strong>, 1979: Robust Locally Weighted Regression and Smoothing Scatterplots. <em>Journal of the American Statistical Association<\/em>, <strong>74<\/strong>: 829-836.<\/li>\n<\/ul>\n<p>\u6298\u308a\u304b\u3048\u3057\u306e\u4e0b\u306b\u76ee\u6b21\u3092\u3064\u3051\u308b\u3002(\u308f\u305f\u3057\u304c\u672c\u306e\u76ee\u6b21\u3092\u66f8\u304d\u306c\u304f\u306e\u306f2014\u5e74\u3054\u308d\u4ee5\u5f8c\u306e\u7fd2\u6163\u3067\u3001\u30a6\u30a7\u30d6\u30b5\u30a4\u30c8\u306b[\u8aad\u66f8\u30ce\u30fc\u30c8]\u3092\u51fa\u3057\u305f\u3053\u308d\u306f\u3057\u3066\u3044\u306a\u304b\u3063\u305f\u3002)<br \/>\n<!--more--><br \/>\n== \u76ee\u6b21 ==<br \/>\nAcknowledgements<br \/>\nContents<br \/>\nPreface<br \/>\nChapter 1. Introduction<br \/>\n &#8211; 1.1 The Contents of the Book<br \/>\n &#8211; &#8211; Chapter 2: Principles of Graph Construction<br \/>\n &#8211; &#8211; Chapter 3: Graphical Methods<br \/>\n &#8211; &#8211; Chapter 4: Graphical Perception<br \/>\n &#8211; 1.2 The Power of Graphical Data Display<br \/>\n &#8211; 1.3 The Challenge of Graphical Data Display<br \/>\n &#8211; &#8211; Aerosol Concentrations<br \/>\n &#8211; &#8211; Brain Masses and Body Masses of Animal Species<br \/>\n &#8211; 1.4 Sources and Goals<br \/>\n &#8211; &#8211; Principles of GraphConstruction<br \/>\n &#8211; &#8211; Graphical Methods<br \/>\n &#8211; &#8211; Graphical Perception<br \/>\nChapter 2. Principles of Graph Construction<br \/>\n &#8211; 2.1 Terminology<br \/>\n &#8211; 2.2 Clear Vision<br \/>\n &#8211; 2.3 Clear Understanding<br \/>\n &#8211; 2.4 Scales<br \/>\n &#8211; 2.5 General Strategy<br \/>\n &#8211; 2.6 A Listing of the Principles of Graph Construction<br \/>\nChapter 3. Graphical Methods<br \/>\n &#8211; 3.1 General Methods: Logarithms and Residuals<br \/>\n &#8211; &#8211; Logarithms<br \/>\n &#8211; &#8211; Log Base 2 and Log Base e<br \/>\n &#8211; &#8211; Graphing Percent Change<br \/>\n &#8211; &#8211; Residuals<br \/>\n &#8211; &#8211; The Tukey Sum-Difference Graph<br \/>\n &#8211; 3.2 One or More Categories of Measurements of One Quantitative Variable: Graphing Distributions<br \/>\n &#8211; &#8211; Point Graphs and Hisograms<br \/>\n &#8211; &#8211; Percentile Graphs<br \/>\n &#8211; &#8211; Box Graphs<br \/>\n &#8211; &#8211; Percentile Graphs with Summaries<br \/>\n &#8211; &#8211; Percentile Comparison Graphs<br \/>\n &#8211; 3.3 One Quantitative Variable With Labels: Dot Charts<br \/>\n &#8211; &#8211; Ordinary Dot Charts<br \/>\n &#8211; &#8211; Two-Way, Grouped, and Multi-Valued Dot Charts<br \/>\n &#8211; 3.4 Two Quantitative Variables<br \/>\n &#8211; &#8211; Overlap: Logarithms, Residuals, Moving, Sunflowers, Jittering, and Circles<br \/>\n &#8211; &#8211; Box Graphs for Summarizing Distributions of Repeat Measurements of a Dependent Variable<br \/>\n &#8211; &#8211; Strip Summaries Using Box Graphs<br \/>\n &#8211; &#8211; Smoothing: Lowess<br \/>\n &#8211; &#8211; Time Series: Connected, Symbol, Connected Symbol, and Vertical Line Graphs<br \/>\n &#8211; &#8211; Time Series: Seasonal Subseries Graphs<br \/>\n &#8211; &#8211; An Equally-Spaced Independent Variable with a Single-Valued Dependent Variable<br \/>\n &#8211; 3.5 Two or More Caterogies of Measurements of Two Quantiatative Variables: Superposition and Juxtaposition<br \/>\n &#8211; &#8211; Superposed Plotting Symbols<br \/>\n &#8211; &#8211; Superposed Curves in Black and White<br \/>\n &#8211; &#8211; Juxtaposition<br \/>\n &#8211; &#8211; Color<br \/>\n &#8211; 3.6 Three or More Quantitative Variables<br \/>\n &#8211; &#8211; Framed-Rectangle Graphs<br \/>\n &#8211; &#8211; Scatterplot Matrices<br \/>\n &#8211; &#8211; A View of the Future: High-Interaction Graphical Methods<br \/>\n &#8211; 3.7 Statistical Variation<br \/>\n &#8211; &#8211; Empirical Distribution of the Data<br \/>\n &#8211; &#8211; Sample-to-Sample Variation of a Statistic<br \/>\n &#8211; &#8211; One-Standard-Error Bars<br \/>\n &#8211; &#8211; Two-Tiered Error Bars<br \/>\nChapter 4. Graphical Perception<br \/>\n &#8211; 4.1 Cognitive Tasks and Perceptual Tasks<br \/>\n &#8211; 4.2 The Elements of the Paradigm<br \/>\n &#8211; &#8211; Elementary Graphical-Perception Tasks<br \/>\n &#8211; &#8211; Distance<br \/>\n &#8211; &#8211; Detection<br \/>\n &#8211; 4.3 Theory and Experimentation<br \/>\n &#8211; &#8211; Weber&#8217;s Law<br \/>\n &#8211; &#8211; Stevens&#8217; Law<br \/>\n &#8211; &#8211; Angle Judgments<br \/>\n &#8211; &#8211; The Angle Contamination of Slope Judgments<br \/>\n &#8211; &#8211; Experiments in Graphical Perception<br \/>\n &#8211; &#8211; Summary and Discussion<br \/>\n &#8211; 4.4 Application of the Paradigm to Data Display<br \/>\n &#8211; &#8211; Slope Judgments: Graphing Rate of Change<br \/>\n &#8211; &#8211; Length Judgments: Divided Bar Charts<br \/>\n &#8211; &#8211; Angle Judgments: Pie Charts<br \/>\n &#8211; &#8211; Cognition<br \/>\n &#8211; &#8211; Distance and Detection<br \/>\n &#8211; &#8211; Detection: Superposed Curves<br \/>\n &#8211; &#8211; Area<br \/>\n &#8211; &#8211; Density and Length: Statistical Maps<br \/>\n &#8211; &#8211; Residuals<br \/>\n &#8211; &#8211; Dot Charts and Bar Charts<br \/>\n &#8211; &#8211; Summation<br \/>\nReferences<br \/>\nGraph Index<br \/>\nText Index<\/p>\n","protected":false},"excerpt":{"rendered":"<p>William S. Cleveland, 1985: The Elements of Graphing Data. Monterey CA USA: Wadsworth Advanced Books and Softw [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[],"class_list":["post-10283","post","type-post","status-publish","format-standard","hentry","category-dokusyo-memo-"],"_links":{"self":[{"href":"http:\/\/macroscope.world.coocan.jp\/yukukawa\/index.php?rest_route=\/wp\/v2\/posts\/10283","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/macroscope.world.coocan.jp\/yukukawa\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/macroscope.world.coocan.jp\/yukukawa\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/macroscope.world.coocan.jp\/yukukawa\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"http:\/\/macroscope.world.coocan.jp\/yukukawa\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=10283"}],"version-history":[{"count":8,"href":"http:\/\/macroscope.world.coocan.jp\/yukukawa\/index.php?rest_route=\/wp\/v2\/posts\/10283\/revisions"}],"predecessor-version":[{"id":10295,"href":"http:\/\/macroscope.world.coocan.jp\/yukukawa\/index.php?rest_route=\/wp\/v2\/posts\/10283\/revisions\/10295"}],"wp:attachment":[{"href":"http:\/\/macroscope.world.coocan.jp\/yukukawa\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=10283"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/macroscope.world.coocan.jp\/yukukawa\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=10283"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/macroscope.world.coocan.jp\/yukukawa\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=10283"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}