LEADER 04251nam 2200625Ia 450 001 9910829913403321 005 20180612234344.0 010 $a1-282-30757-6 010 $a9786612307577 010 $a0-470-31693-4 010 $a0-470-31777-9 035 $a(CKB)1000000000687569 035 $a(EBL)469296 035 $a(OCoLC)460049708 035 $a(SSID)ssj0000342261 035 $a(PQKBManifestationID)11255262 035 $a(PQKBTitleCode)TC0000342261 035 $a(PQKBWorkID)10285542 035 $a(PQKB)10299289 035 $a(MiAaPQ)EBC469296 035 $a(PPN)159334853 035 $a(EXLCZ)991000000000687569 100 $a19980310d1998 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aRegression graphics$b[electronic resource] $eideas for studying regressions through graphics /$fR. Dennis Cook 210 $aNew York $cWiley$dc1998 215 $a1 online resource (378 p.) 225 1 $aWiley series in probability and statistics Probability and statistics section 300 $aDescription based upon print version of record. 311 $a0-471-19365-8 320 $aIncludes bibliographical references (p. 329-337) and indexes. 327 $aRegression 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 327 $a2.3.2 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 327 $a4.4. 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 327 $a7.5.2 Conditions for S ylx1=S(n1) 330 $aAn exploration of regression graphics through computer graphics.Recent developments in computer technology have stimulated new and exciting uses for graphics in statistical analyses. Regression Graphics, one of the first graduate-level textbooks on the subject, demonstrates how statisticians, both theoretical and applied, can use these exciting innovations. After developing a relatively new regression context that requires few scope-limiting conditions, Regression Graphics guides readers through the process of analyzing regressions graphically and assessing and selecting models. This i 410 0$aWiley series in probability and statistics.$pProbability and statistics. 606 $aMultivariate analysis 606 $aRegression analysis$xGraphic methods 615 0$aMultivariate analysis. 615 0$aRegression analysis$xGraphic methods. 676 $a519.536 676 $a519.536028 700 $aCook$b R. Dennis$089150 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910829913403321 996 $aRegression graphics$9625215 997 $aUNINA