## Applied Regression AnalysisAn outstanding introduction to the fundamentals of regression analysis-updated and expanded The methods of regression analysis are the most widely used statistical tools for discovering the relationships among variables. This classic text, with its emphasis on clear, thorough presentation of concepts and applications, offers a complete, easily accessible introduction to the fundamentals of regression analysis. Assuming only a basic knowledge of elementary statistics, Applied Regression Analysis, Third Edition focuses on the fitting and checking of both linear and nonlinear regression models, using small and large data sets, with pocket calculators or computers. This Third Edition features separate chapters on multicollinearity, generalized linear models, mixture ingredients, geometry of regression, robust regression, and resampling procedures. Extensive support materials include sets of carefully designed exercises with full or partial solutions and a series of true/false questions with answers. All data sets used in both the text and the exercises can be found on the companion disk at the back of the book. For analysts, researchers, and students in university, industrial, and government courses on regression, this text is an excellent introduction to the subject and an efficient means of learning how to use a valuable analytical tool. It will also prove an invaluable reference resource for applied scientists and statisticians. |

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### Contents

7The AlgebraandGeometry of Pure | |

3 | |

Straight Line Case | |

The General Regression Situation | |

6Extra Sumsof Squares and Tests for Several | |

7Serial Correlation in the Residuals and the Durbin | |

RidgeRegression | |

18Generalized Linear Models GLIM | |

Mixture Ingredients as Predictor Variables | |

20The Geometry of Least Squares | |

More Geometry of Least Squares | |

Orthogonal Polynomials and Summary Data | |

Multiple Regression Applied to Analysis of Variance | |

AnIntroduction to Nonlinear Estimation | |

2 | |

Exercises for Chapter | |

More on Checking Fitted Models | |

Special Topics | |

Biasin Regression Estimates and Expected Values | |

4Expected Value of Extra Sum of Squares | |

Dummy Variables 14 1 DummyVariables to Separate | |

15Selecting the Best Regression Equation | |

Models Containing Functions of the Predictors | |

Robust Regression | |

Bibliography | |

TrueFalse Questions | |

Answers to Exercises | |

Tables | |

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### Common terms and phrases

alternative analysis of variance approximate calculations canbe coefficients columns confidence intervals contours corresponding defined degrees of freedom diagonal distribution dummy variable evaluate example EXERCISES FOR CHAPTER experimental extra sum Figure fitted equation fitted model fitted values Ftest function Fvalue geometric given Hald Data hypothesis indicated inthe inverse isnot isthe itis lack of fit least squares estimates linear model matrix mean square MINITAB nonlinear normal equations Note observations obtained ofsquares ofthe onthe orthogonal orthogonal polynomials parameters plot polynomial predicted predictor variables problem pure error quadratic regression equation relationship residual sum response ridge regression Section serial correlation shown significant solution SS(b statistic steam data straight line studentized residual sum of squares Suppose thatthe thedata themodel theresiduals tothe transformation true mean value usually variance table vector versus weighted least squares zero