## Applied linear regression modelsApplied 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. |

### From inside the book

51 pages matching **estimated standard deviations** in this book

Where's the rest of this book?

Results 1-3 of 51

### What people are saying - Write a review

We haven't found any reviews in the usual places.

### Contents

Some Basic Results in Probability and Statistics | 1 |

Populations 7 | 19 |

Inferences in Regression Analysis | 62 |

Copyright | |

17 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

appropriate Bonferroni box plot Calculate column conclusion confidence interval Cook's distance decision rule degrees of freedom denoted DFFITS effect error sum error terms error variances estimated regression coefficients estimated regression function estimated standard deviations expected values extra sums family confidence coefficient Figure fitted regression function fitted values gression Hence independent variables indicator variables interval estimate least squares estimates linear regression model mean response mean squared measure multicollinearity nonlinear regression normal error normal probability plot Note observations Obtain the residuals outlying P-value parameters percent confidence interval polynomial prediction interval Problem procedure quadratic reduced model Refer regres regression analysis regression line regression model 3.1 regression relation residual plot response function response surface sample scatter plot simple linear regression SSTO subset sum of squares Table test statistic tion transformation variance-covariance matrix vector weighted least squares Westwood Company example zero