## Applied Regression Including Computing and GraphicsA step-by-step guide to computing and graphics in regression analysis In this unique book, leading statisticians Dennis Cook and Sanford Weisberg expertly blend regression fundamentals and cutting-edge graphical techniques. They combine and up- date most of the material from their widely used earlier work, An Introduction to Regression Graphics, and Weisberg's Applied Linear Regression; incorporate the latest in statistical graphics, computing, and regression models; and wind up with a modern, fully integrated approach to one of the most important tools of data analysis. In 23 concise, easy-to-digest chapters, the authors present:? A wealth of simple 2D and 3D graphical techniques, helping visualize results through graphs * An improved version of the user-friendly Arc software, which lets readers promptly implement new ideas * Complete coverage of regression models, including logistic regression and generalized linear models * More than 300 figures, easily reproducible on the computer * Numerous examples and problems based on real data * A companion Web site featuring free software and advice, available at www.wiley.com/mathem atics Accessible, self-contained, and fully referenced, Applied Regression Including Computing and Graphics assumes only a first course in basic statistical methods and provides a bona fide user manual for the Arc software. It is an invaluable resource for anyone interested in learning how to analyze regression problems with confidence and depth. |

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### Contents

1 | |

PART II TOOLS | 95 |

PART III REGRESSION GRAPHICS | 409 |

PART IV LOGISTIC REGRESSION AND GENERALIZED LINEAR MODELS | 465 |

571 | |

Author Index | 579 |

583 | |

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1D structure 2D view 3D added-variable plot 3D plot Analysis of Variance bivariate normal distribution bluegill boxplots button Ceres plot chapter computed conditional distribution conﬁdence curve data ﬁle deﬁned deletions density depends deviance dialog discussed equation example ﬁnd ﬁrst ﬁt ﬁtted values ﬁtting graphical regression GREG predictors haystack data hypothesis intercept inverse kernel mean function Lake Mary least squares linear combination linear regression model linearly related predictors logistic regression methods model checking plots multiple linear regression Myopathy null hypothesis observations obtained Options p-value parameters points pop-up menu population problem quadratic quantile random variables Rem lin trend residual sum response plot scatterplot matrix Section select the item shown in Figure simple linear regression slice slidebar standard deviation standard error statistics submodel subpopulation sum of squares summary plot Table transactions data transformation Var(y variance function vector versus visual weight weighted least squares Xlisp-Stat