Statistical Analysis of Designed Experiments (Innbundet (stive permer))

This textbook presents the design and analysis of experiments that comprise the aspects of classical theory for continuous response, modern procedures for categorical response, and especially for correlated categorical response. For any design (independent and matched-pair response) the parametric and nonparametric tests depending on the data level are given. Complex designs, such as, for example, cross-over and repeated measures, are included at an introductory and advanced manner. The problem of missing data is also handled with care and procedures in approaching this problem are proposed. Thus, this is an important resource/reference book for undergraduates and university teachers, for statistical researchers in the pharmaceutical industry, or other industries, as well as for clinical research in medicine and dentistry. The third edition contains a new chapter on incomplete block designs that includes the balanced incomplete block design and partially balanced incomplete block design in addition to the general theory on incomplete block designs.The chapter "Multifactor Experiments" is extended and includes new topics on confounding, partial confounding and fractional replications that are explained with examples. The more theoretical proofs of Chapters 3, 4 and 6 are put into an appendix. Helge Toutenburg is Professor of Statistics at the University of Munich. He has written nineteen books on linear models, statistical methods in quality engineering, and the analysis of designed experiments. He works on applications of statistics to the fields of medicine and engineering. Shalabh is Associate Professor of Statistics at the Indian Institute of Technology Kanpur. He has co-authored a book on linear models and co-edited a book on the recent developments in the area of linear models. He works in the area of linear models, regression analysis and econometrics.

3rd Edition

Introduction.- Comparison of two samples.- The linear regression model.- Single-factor experiments with fixed and random effects.- More restrictive designs.- Incomplete block designs.- Multifactor experiments.- Models for categorical response variables.- Repeated measures model.- Cross-over design. - Statistical analysis of incomplete data.