04251nam 2200625Ia 450 991082991340332120180612234344.01-282-30757-697866123075770-470-31693-40-470-31777-9(CKB)1000000000687569(EBL)469296(OCoLC)460049708(SSID)ssj0000342261(PQKBManifestationID)11255262(PQKBTitleCode)TC0000342261(PQKBWorkID)10285542(PQKB)10299289(MiAaPQ)EBC469296(PPN)159334853(EXLCZ)99100000000068756919980310d1998 uy 0engur|n|---|||||txtccrRegression graphics[electronic resource] ideas for studying regressions through graphics /R. Dennis CookNew York Wileyc19981 online resource (378 p.)Wiley series in probability and statistics Probability and statistics sectionDescription based upon print version of record.0-471-19365-8 Includes bibliographical references (p. 329-337) and indexes.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 transformations2.3.2 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 structure4.4. 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 predictors7.5.2 Conditions for S ylx1=S(n1)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 iWiley series in probability and statistics.Probability and statistics.Multivariate analysisRegression analysisGraphic methodsMultivariate analysis.Regression analysisGraphic methods.519.536519.536028Cook R. Dennis89150MiAaPQMiAaPQMiAaPQBOOK9910829913403321Regression graphics625215UNINA