## Applied Regression AnalysisFitting a straight lise by squares; The matrix approach to linear regression; Thr examination of residuals; Two independent variables; More complicated models; Selecting the "best" regression equation; A specific problem; Multiple regression and mathematical model building; Multiple regression applied to analysis of variance problems; An introduction to nonlinear estimation; Percentage points of the distribution; Percentage points of the distribution. |

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### Contents

CHAPTER | 1 |

The Matrix Approach to Linear Regression | 44 |

The Examination of Residuals | 86 |

Copyright | |

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analysis of variance ANOVA Source df Biometrika calculations Chapter Coefficients and Confidence column confidence interval Confidence Limits Constant term contours correlation matrix Decoded B Limits degrees of freedom deviation of residuals discussed Due to regression dummy variables estimation space examined example F-value Figure fitted equation follows function given independent variables iterative lack of fit least squares estimate linear regression method multiple regression normal equations Observed Y Predicted obtained orthogonal polynomials Overall F Total parameters Partial Correlation Coefficients Partial F-test plot Predicted Y Residual prediction equation pure error regression analysis regression equation Residual Analysis response mean sample space Section Sequential F-test shown Source df SS Source of Variation Square of Partials SS MS F Standard deviation sum of squares Tableau Technometrics temperature term in prediction Total corrected transformations Variable entering variance table Variation df SS vector zero