Table of contents

Contents

Preface

  1. Introduction
    1. C C & I
    2. Illustrations
    3. On things to come
    4. Notational conventions
  2. Introduction to 2D Scatterplots
    1. Response plots in simple regression
    2. New Zealand horse mussels
    3. Transforming y via inverse response plots
    4. Danish twins
    5. Scatterplot matrices
    6. Regression graphics in the 1920's
    7. Discussion
  3. Constructing 3D Scatterplots
    1. Getting an impression of 3D
    2. Depth cuing
    3. Scaling
    4. Orthogonalization
  4. Interpreting 3D Scatterplots
    1. Haystacks
    2. Structural dimensionality
    3. One-dimensional structure
    4. Two-dimensional structure
    5. Assessing structural dimensionality
    6. Assessment methods
  5. Binary Response Variables
    1. One predictor
    2. Two predictors
    3. Illustrations
    4. Three predictors
    5. Visualizing a logistic model
  6. Dimension-Reduction Subspaces
    1. Overview
    2. Dimension-reduction subspaces
    3. Central subspaces
    4. Guaranteeing S_{y|x} constraining
    5. Importance of central subspaces
    6. h-Level response plots
  7. Graphical Regression
    1. Introduction to graphical regression
    2. Capturing S_{y|x1}
    3. Forcing S_{y|x1} S(n1)
    4. Improving resolution
    5. Forcing S_{y|x1}= S(n1)
    6. Visual fitting with h-level response plots
  8. Getting Numerical Help
    1. Fitting with linear kernels
    2. Quadratic kernels
    3. The predictor distribution
    4. Reweighting for elliptical contours
    5. Graphical Regression Studies
    6. Naphthalene data
    7. Wheat protein
    8. Reaction yield
    9. Discussion
  9. Inverse Regression Graphics
    1. Inverse regression function
    2. Inverse variance function
  10. Sliced Inverse Regression
    1. Inverse regression subspace
    2. SIR
    3. Asymptotic distribution of gamma_d
    4. SIR: Mussel data
    5. Minneapolis schools
    6. Discussion
  11. Principal Hessian Directions
    1. Incorporating residuals
    2. Connecting S_{e|z} and S_{ezz} when
    3. Estimation and testing
    4. pHd: Reaction yield
    5. phd: Mussel data
    6. phd: Haystacks
    7. Discussion
  12. Studying Predictor Effects
    1. Introduction to net-effect plots
    2. Distributional indices
    3. Global net-effect plots
  13. Predictor Transformations
    1. CERES plots
    2. CERES plots when E(x1|x2) is...
    3. CERES plots in practice
    4. Big Mac data
    5. Added-variable plots
    6. Environmental contamination
  14. Graphics for Model Assessment
    1. Residual plots
    2. Assessing model adequacy

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