## Applied Regression Modeling
Fully revised to reflect the latest methodologies and emerging applications, The author utilizes a bounty of real-life examples, case studies, illustrations, and graphics to introduce readers to the world of regression analysis using various software packages, including R, SPSS, Minitab, SAS, JMP, and S-PLUS. In a clear and careful writing style, the book introduces modeling extensions that illustrate more advanced regression techniques, including logistic regression, Poisson regression, discrete choice models, multilevel models, and Bayesian modeling. In addition, the - Transformations, indicator variables, and interaction
- Testing model assumptions
- Nonconstant variance
- Autocorrelation
- Variable selection methods
- Model building and graphical interpretation
Throughout the book, datasets and examples have been updated and additional problems are included at the end of each chapter, allowing readers to test their comprehension of the presented material. In addition, a related website features the book's datasets, presentation slides, detailed statistical software instructions, and learning resources including additional problems and instructional videos. With an intuitive approach that is not heavy on mathematical detail, |

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### Contents

Problems | |

Simple linear regression | |

Multiple linear regression | |

Regression model building I | |

Regression model building II | |

Case studies | |

Extensions | |

Computer software help | |

Notation and formulas | |

Answers for selected problems | |

References | |