Author: Elena Kulinskaya
Edition: 1
Publisher: Wiley-Interscience
Binding: Paperback
ISBN: 0470028645
Meta Analysis: A Guide to Calibrating and Combining Statistical Evidence (Wiley Series in Probability and Statistics)
Meta Analysis: A Guide to Calibrating and Combining Statistical Evidence acts as a source of basic methods for scientists wanting to combine evidence from different experiments. Medical books Meta Analysis. The authors aim to promote a deeper understanding of the notion of statistical evidence.
The book is comprised of two parts – The Handbook, and The Theory. The Handbook is a guide for combining and interpreting experimental evidence to solve standard statistical problems. This section allows someone with a rudimentary knowledge in general statistics to apply the methods Medical books Meta-Study of Qualitative Health Research: A Practical Guide to Meta-Analysis and Meta-Synthesis. Categories: Meta-analysis, Medicine->Research->Evaluation. Contributors: Barbara L. Paterson - Author. Format: Hardcover
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Categories: Meta-analysis, Medicine->Research->Evaluation. Contributors: Barbara L. Paterson - Author. Format: Hardcover
Categories: Meta-analysis, Statistical hypothesis testing, Data Interpretation, Statistica. Contributors: Joachim Hartung - Author. Format: Hardcover
Categories: Meta-analysis, Social sciences->Statistical methods, Meta-analysis. Contributors: Mark W. Lipsey - Author. Format: Hardcover
Categories: Meta-analysis. Contributors: Anne Whitehead - Author. Format: Hardcover
Medical Book Meta Analysis
The authors aim to promote a deeper understanding of the notion of statistical evidence.
The book is comprised of two parts – The Handbook, and The Theory. The Handbook is a guide for combining and interpreting experimental evidence to solve standard statistical problems. This section allows someone with a rudimentary knowledge in general statistics to apply the methods. The Theory provides the motivation, theory and results of simulation experiments to justify the methodology.
This is a coherent introduction to the statistical concepts required to understand the authors’ thesis that evidence in a test statistic can often be calibrated when transformed to the right scale.
