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![]( "Buchcover ISBN 9781119180173")
„The book can be used for statistics and engineeringcourses on regression at the upper-undergraduate and graduatelevels. It also serves as a resource for professionals in thefields of engineering, life and biological sciences, and the socialsciences.“ (Zentralblatt MATH, 1 October2013)
Introduction to Linear Regression Analysis
von Douglas C. Montgomery, Elizabeth A. Peck und G. Geoffrey ViningPraise for the Fourth Edition
„As with previous editions, the authors have produced a leadingtextbook on regression.“
--Journal of the American Statistical Association
A comprehensive and up-to-date introduction to thefundamentals of regression analysis
Introduction to Linear Regression Analysis, Fifth Editioncontinues to present both the conventional and less common uses oflinear regression in today's cutting-edge scientificresearch. The authors blend both theory and application to equipreaders with an understanding of the basic principles needed toapply regression model-building techniques in various fields ofstudy, including engineering, management, and the healthsciences.
Following a general introduction to regression modeling, including typical applications, a host of technical tools areoutlined such as basic inference procedures, introductory aspectsof model adequacy checking, and polynomial regression models andtheir variations. The book then discusses how transformations andweighted least squares can be used to resolve problems of modelinadequacy and also how to deal with influential observations. TheFifth Edition features numerous newly added topics, including:
* A chapter on regression analysis of time series data thatpresents the Durbin-Watson test and other techniques for detectingautocorrelation as well as parameter estimation in time seriesregression models
* Regression models with random effects in addition to adiscussion on subsampling and the importance of the mixedmodel
* Tests on individual regression coefficients and subsets ofcoefficients
* Examples of current uses of simple linear regression models andthe use of multiple regression models for understanding patientsatisfaction data.
In addition to Minitab, SAS, and S-PLUS, the authors haveincorporated JMP and the freely available R software to illustratethe discussed techniques and procedures in this new edition. Numerous exercises have been added throughout, allowing readers totest their understanding of the material.
Introduction to Linear Regression Analysis, Fifth Editionis an excellent book for statistics and engineering courses onregression at the upper-undergraduate and graduate levels. The bookalso serves as a valuable, robust resource for professionals in thefields of engineering, life and biological sciences, and the socialsciences.
„As with previous editions, the authors have produced a leadingtextbook on regression.“
--Journal of the American Statistical Association
A comprehensive and up-to-date introduction to thefundamentals of regression analysis
Introduction to Linear Regression Analysis, Fifth Editioncontinues to present both the conventional and less common uses oflinear regression in today's cutting-edge scientificresearch. The authors blend both theory and application to equipreaders with an understanding of the basic principles needed toapply regression model-building techniques in various fields ofstudy, including engineering, management, and the healthsciences.
Following a general introduction to regression modeling, including typical applications, a host of technical tools areoutlined such as basic inference procedures, introductory aspectsof model adequacy checking, and polynomial regression models andtheir variations. The book then discusses how transformations andweighted least squares can be used to resolve problems of modelinadequacy and also how to deal with influential observations. TheFifth Edition features numerous newly added topics, including:
* A chapter on regression analysis of time series data thatpresents the Durbin-Watson test and other techniques for detectingautocorrelation as well as parameter estimation in time seriesregression models
* Regression models with random effects in addition to adiscussion on subsampling and the importance of the mixedmodel
* Tests on individual regression coefficients and subsets ofcoefficients
* Examples of current uses of simple linear regression models andthe use of multiple regression models for understanding patientsatisfaction data.
In addition to Minitab, SAS, and S-PLUS, the authors haveincorporated JMP and the freely available R software to illustratethe discussed techniques and procedures in this new edition. Numerous exercises have been added throughout, allowing readers totest their understanding of the material.
Introduction to Linear Regression Analysis, Fifth Editionis an excellent book for statistics and engineering courses onregression at the upper-undergraduate and graduate levels. The bookalso serves as a valuable, robust resource for professionals in thefields of engineering, life and biological sciences, and the socialsciences.
