Regression graphics [[electronic resource] ] : ideas for studying regressions through graphics / / R. Dennis Cook

• Record Nr.: UNINA9910829913403321
• Autore: Cook R. Dennis
• Pubbl/distr/stampa: New York, : Wiley, c1998
• ISBN: 1-282-30757-6; 9786612307577; 0-470-31693-4; 0-470-31777-9
• Descrizione fisica: 1 online resource (378 p.)
• Collana: Wiley series in probability and statistics Probability and statistics section
• Disciplina: 519.536; 519.536028
• Soggetti: Multivariate analysis; Regression analysis - Graphic methods
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references (p. 329-337) and indexes.
• Nota di contenuto: Regression Graphics Ideas for Studying Regressions through Graphics; Contents; Preface; 1. Introduction; 1.1. C.C & I,1; 1.1.1. Construction; 1.1.3. Inference; 1.1.2. Characterization; 1.2. Illustrations; 1.2.1. Residuals versus fitted values; 1.2.2. Residuals versus the predictors; 1.2.3. Residuals versus the response; 1.3. On things to come; 1.4. Notational conventions; Problems; 2. Introduction to 2D Scatterplots; 2.1. Response plots in simple regression; 2.2. New Zealand horse mussels; 2.3. Transforming y via inverse response plots; 2.3.1 Response transformations; 2.3.2 Response transformations: Mussel data2.4. Danish twins; 2.5. Scatterplot matrices; 2.5.1 Consrruction; 2.5.2 Example; 2.6. Regression graphics in the 1920s; 2.6.1. Ezekiel's successive approximations; 2.6.2. Bean's graphic method; 2.7. Discussion; Problems; 3. Constructing 3D Scatterplots; 3.1. Getting an impression of 3D; 3.2. Depth cuing; 3.3. Scaling; 3.4. Orthogonalization; Problems; 4. Interpreting 3D Scatterplots; 4.1. Haystacks; 4.2. Structural dimensionality; 4.2.1. One predictor; 4.2.2. Two predictors; 4.2.3 Many ; predictors; 4.3. One-dimensional structure; 4.4. Two-dimensional structure4.4.1. Removing linear trends; 4.4.2. Identifying semiparametric regression functions; 4.5. Assessing structural dimensionality; 4.5.1. A visual metaphor for structural dimension; 4.5.2. A first method for deciding d = 1 or 2; 4.5.3. Natural rubber; 4.6. Assessment methods; 4.6.1. Using independence; 4.6.2. Using uncorrelated 2D views; 4.6.3. Uncorrelated 2D views: Haystack data; 4.6.4. Intraslice residuals; 4.6.5. Intraslice orthogonalization; 4.6.6. Mussels again; 4.6.7. Discussion; Problems; 5. Binary Response Variables; 5.1. One predictor; 5.2. Two predictors; 7.5.2 Conditions for S ylx1=S(n1)
• Sommario/riassunto: An exploration of regression graphics through computer graphics.Recent developments in computer technology have stimulated new and exciting uses for graphics in statistical analyses. Regression Graphics, one of the first graduate-level textbooks on the subject, demonstrates how statisticians, both theoretical and applied, can use these exciting innovations. After developing a relatively new regression context that requires few scope-limiting conditions, Regression Graphics guides readers through the process of analyzing regressions graphically and assessing and selecting models. This i