LEADER 05516nam 2200661Ia 450 001 9910877092303321 005 20170810195502.0 010 $a1-282-30784-3 010 $a9786612307843 010 $a0-470-31686-1 010 $a0-470-31770-1 035 $a(CKB)1000000000687565 035 $a(EBL)469128 035 $a(OCoLC)625899956 035 $a(SSID)ssj0000339291 035 $a(PQKBManifestationID)11248057 035 $a(PQKBTitleCode)TC0000339291 035 $a(PQKBWorkID)10364580 035 $a(PQKB)10578534 035 $a(MiAaPQ)EBC469128 035 $a(PPN)159338271 035 $a(EXLCZ)991000000000687565 100 $a19940516d1994 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 13$aAn introduction to regression graphics$b[electronic resource] /$fR. Dennis Cook, Sanford Weisberg 210 $aNew York $cWiley$dc1994 215 $a1 online resource (282 p.) 225 1 $aWiley series in probability and mathematical statistics 300 $a"A Wiley-Interscience publication." 311 $a0-471-00839-7 320 $aIncludes bibliographical references (p. 241-244) and index. 327 $aAn Introduction to Regression Graphics; Contents; Preface; 1 Getting Started; 1.1 Doing the examples; 1.2 A Very Brief Introduction to Xlisp-Stat; 1.2.1 Entering Data; 1.2.2 Working with Lists; 1.2.3 Calculating the Slope and Intercept; 1.2.4 Drawing a Histogram; 1.2.5 Drawing a Scatterplot; 1.2.6 Saving and Printing Text; 1.2.7 Saving and Printing a Graph; 1.2.8 Quitting Xlisp-Stat; 1.3 An Introduction to the R-code; 1.4 Using Your Own Data; 1.5 Getting Help; 1.6 Complements; Exercises; 2 Simple Regression Plots; 2.1 Thinking about Scatterplots; 2.2 Simple Linear Regression 327 $a2.3 Assessing Linearity2.3.1 Superimposing the Fitted Line; 2.3.2 Residual Plots; 2.3.3 Average Smoothing; 2.3.4 Regression Smoothing; 2.4 Complements; Exercises; 3 Two-Dimensional Plots; 3.1 Aspect Ratio and Focusing; 3.2 Power Transformations; 3.3 Thinking about Power Transformations; 3.4 Showing Labels and Coordinates; 3.5 Linking Plots; 3.6 Marking and Coloring Points; 3.7 Brushing; 3.8 Name Lists; 3.9 complements; Exercises; 4 Scatterplot Matrices; 4.1 Using a Scatterplot Matrix; 4.2 Identifying Points; 4.3 Transforming Predictors to Linearity; 4.4 Partial Response Plots; 4.5 Complements 327 $aExercises5 Three-Dimensional Plots; 5.1 Viewing a Three-Dimensional Plot; 5.1.1 Rotation Control; 5.1.2 Recalling Views; 5.1.3 Rocking; 5.1.4 Show Axes; 5.1.5 Depth Cuing; 5.2 Scaling and Centering; 5.3 Two-Dimensional Plots fromThree-Dimensional Plots; 5.3.1 Saving h; 5.3.2 Rotation in Two Dimensions; 5.3.3 Extracting a Two-Dimensional Plot; 5.3.4 Summary; 5.4 Removing a Linear Trend in Three-Dimensional Plots; 5.5 Using Uncorrelated Predictors; 5.6 Complements; Exercises; 6 Visualizing Linear Regression with Two Predictors; 6.1 Linear Regression; 6.1.1 The Ideal Summary Plot 327 $a6.1.2 Viewing an Ideal Summary Plot When o2 = 06.2 Fitting by Eye; 6.2.1 Fitting by Eye When o2 = 0; 6.2.2 Fitting by Eye When o2 > 0; 6.2.3 Fitting by ols; 6.2.4 Checking a Candidate Summary Plot; 6.3 Correlated Predictors; 6.4 Distribution of the Predictors; 6.4.1 Nonlinear Predictors; 6.4.2 Linear Relationships Between Predictors; 6.4.3 Partial Variance Functions; 6.4.4 Scatterplot Matrices; 6.4.5 Multiple Regression; 6.5 Linear Predictors; 6.6 Complements; Exercises; 7 Visualizing Regression without Linearity; 7.1 General Three-Dimensional Response Plots; 7.1.1 Zero-Dimensional Structure 327 $a7.1.2 One-Dimensional Structure7.1.3 Two-Dimensional Structure; 7.2 Example: Australian Athletes Data; 7.3 Example: Ethanol Data; 7.4 Many Predictors; 7.4.1 The One-Dimensional Estimation Result; 7.4.2 An Example with a Nonlinear Response; 7.5 Example: Berkeley Guidance Study for Girls; 7.6 Example: Australian Athletes Again; 7.7 Complements; 7.7.1 Linearity; 7.7.2 ols Summary Plots; 7.7.3 References; Exercises; 8 Finding Dimension; 8.1 Finding Dimension Graphically; 8.1.1 The Inverse Regression Curve; 8.1.2 Inverse Partial Response Plots; 8.2 Sliced Inverse Regression 327 $a8.3 Example: Ethanol Data Revisited 330 $aCovers the use of dynamic and interactive computer graphics in linear regression analysis, focusing on analytical graphics. Features new techniques like plot rotation. The authors have composed their own regression code, using Xlisp-Stat language called R-code, which is a nearly complete system for linear regression analysis and can be utilized as the main computer program in a linear regression course. The accompanying disks, for both Macintosh and Windows computers, contain the R-code and Xlisp-Stat.An Instructor's Manual presenting detailed solutions to all the problems in the book 410 0$aWiley series in probability and mathematical statistics. 606 $aMultivariate analysis 606 $aRegression analysis$xGraphic methods$xData processing 615 0$aMultivariate analysis. 615 0$aRegression analysis$xGraphic methods$xData processing. 676 $a519.536028566 676 $a519.536078 700 $aCook$b R. Dennis$089150 701 $aWeisberg$b Sanford$f1947-$0104044 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910877092303321 996 $aAn introduction to regression graphics$94185235 997 $aUNINA