Statistical Models for Proportions and Probabilities

Beschreibung

Methods for making inferences from data about one or more probabilities and proportions are a fundamental part of a statistician’s toolbox and statistics courses. Unfortunately many of the quick, approximate methods currently taught have recently been found to be inappropriate. This monograph gives an up-to-date review of recent research on the topic and presents both exact methods and helpful approximations. Detailed theory is also presented for the different distributions involved, and can be used in a classroom setting. It will be useful for those teaching statistics at university level and for those involved in statistical consulting.
Methods for making inferences from data about one or more probabilities and proportions are a fundamental part of a statistician’s toolbox and statistics courses. Unfortunately many of the quick, approximate methods currently taught have recently been found to be inappropriate. This monograph gives an up-to-date review of recent research on the topic and presents both exact methods and helpful approximations. Detailed theory is also presented for the different distributions involved, and can be used in a classroom setting. It will be useful for those teaching statistics at university level and for those involved in statistical consulting.
An up-to-date review of recent research on the topic Detailed theory for the distributions involved is presented For undergraduate and graduate students of Statistics Includes supplementary material: sn.pub/extras

Autor*in

George A.F. Seber

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From the book reviews:

“This monograph is written by George Seber, a famous author of more than a dozen books and dozens of papers in statistics and matrix analysis. … It is amazing that the author could present a huge amount of valuable information in such a relatively small volume which can serve as a handbook on proportion and probability statistics for lecturers, undergraduate and graduate students, researchers, and statistical consultants.” (Stan Lipovetsky, Technometrics, Vol. 56 (2), May, 2014)

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