Statistical Principles and Techniques in Scientific and Social Research

Paperback | July 7, 2007

byWojtek J. Krzanowski

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This graduate-level text provides a survey of the logic and reasoning underpinning statistical analysis, as well as giving a broad-brush overview of the various statistical techniques that play a major role in scientific and social investigations. Arranged in rough historical order, the textstarts with the ideas of probability that underpin statistical methods and progresses through the developments of the nineteenth and twentieth centuries to modern concerns and solutions.Assuming only a basic level of Mathematics and with numerous examples and illustrations, this text presents a valuable resource not only to the experienced researcher but also to the student, by complementing courses in a wide range of substantive areas and enabling the reader to rise above thedetails in order to see the overall structure of the subject.

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This graduate-level text provides a survey of the logic and reasoning underpinning statistical analysis, as well as giving a broad-brush overview of the various statistical techniques that play a major role in scientific and social investigations. Arranged in rough historical order, the textstarts with the ideas of probability that und...

Professor Krzanowski completed his PhD in Applied Statistics at Reading University in 1974. Between 1974 and 1990 he was successively Lecturer, Senior Lecturer and Reader in Applied Statistics at Reading University, and was Professor of Statistics at Exeter University from 1990 to 2005. Taking early retirement in 2005, he is now Emeri...

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Principles of Multivariate Analysis

Paperback|Jul 1 2000

$184.56 online$234.00list price(save 21%)
Format:PaperbackDimensions:256 pages, 9.21 × 6.14 × 0.55 inPublished:July 7, 2007Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0199213097

ISBN - 13:9780199213092

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Table of Contents

Preface1. Probability2. Populations, samples, and data summary3. Population models4. Statistical inference - the frequentist approach5. Statistical inference - Bayesian and other approaches6. Linear models and least squares7. Generalising the linear model8. Association between variables9. Investigating complex data sets10. Special topicsSources and further readingIndex