## Applied Linear Regression Models, Volume 1Applied Linear Regression Models was listed in the newsletter of the Decision Sciences Institute as a classic in its field and a text that should be on every member's shelf. The third edition continues this tradition. It is a successful blend of theory and application. The authors have taken an applied approach, and emphasize understanding concepts; this text demonstrates their approach trough worked-out examples. Sufficient theory is provided so that applications of regression analysis can be carried out with understanding. John Neter is past president of the Decision Science Institute, and Michael Kutner is a top statistician in the health and life sciences area. Applied Linear Regression Models should be sold into the one-term course that focuses on regression models and applications. This is likely to be required for undergraduate and graduate students majoring in allied health, business, economics, and life sciences. |

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### Contents

Inferences in Regression Analysis | 44 |

Diagnostics and Remedial Measures | 95 |

Simultaneous Inferences and Other Topics | 152 |

Copyright | |

13 other sections not shown

### Other editions - View all

Applied Linear Regression Models Michael H. Kutner,Chris J. Nachtsheim,John Neter No preview available - 2003 |

### Common terms and phrases

95 percent confidence appropriate approximate autocorrelation Bonferroni calculations column conclusion confidence interval confidence limits Cook's distance decision rule degrees of freedom denoted error sum error terms error variance estimated regression coefficients estimated regression function estimated standard deviations explanatory variables extra sum family confidence coefficient Figure fitted model fitted regression function fitted values follows Hence least squares estimates likelihood function linear regression function linear regression model logistic regression logistic regression model maximum likelihood estimates mean response mean square measure multicollinearity nonlinear regression normal probability plot normally distributed Note observations ordinary least squares outlying P-value percent confidence interval prediction interval probability distribution Problem procedure random variables Refer regression analysis regression line regression model 2.1 regression relation residual plot response variable simple linear regression SSTO standard deviation subset sum of squares Table test statistic Toluca Company example variance-covariance matrix vector weighted least squares