Linear models are used to describe data in statistical analysis, and the analysis of variance is used to estimate and make inferences about the means of populations. Although extensive literature exists on the methods of analyzing data in the context of linear models, there is a lack of agreement on the appropriate analysis and some confusion over the application of existing methods. This book aims to present methods in a conceptually simple way so the reader may more easily understand the applications of the methods. It contains a balance of theory and applications and there is a solutions manual and data sets disk included.
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A novel approach to linear model analysis
Methods and Applications of Linear Models provides a clear and concise summary of the concepts and methodologies of linear models and illustrates the analysis with numerous exercises and real–world examples. Special features include:
This novel approach to linear model analysis offers a unified treatment of linear regression and the analysis of variance. The focus is on the appropriate interpretation of results. Carefully chosen examples illustrate the analyses and some of the common sources of confusion in the application of the methods. The treatment of mixed models includes material that has not previously appeared in the literature.
For upper–level undergraduate and graduate students of regression and the analysis of variance, this volume provides simple explanations of the basic methodologies. It is also a valuable professional reference for applied statisticians and researchers. Ronald R. Hocking is Professor Emeritus in the Department of Statistics at Texas A&M University. He received his PhD in mathematics and statistics and is a Fellow of the American Statistical Association.
...presents a thorough treatment of the concepts and methods of linear model analysis and illustrates them with numerical and conceptual examples... ( Quarterly of Applied Mathematics, Vol. LXII, No. 1, March 2004)
"...an essential desktop reference book...it should definitely be on your bookshelf." (Technometrics, Vol. 45, No. 4, November 2003)
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