Meta Analysis: A Guide to Calibrating and Combining Statistical Evidence
ISBN-13:
9780470028643
ISBN-10:
0470028645
Edition:
1
Author:
Elena Kulinskaya, Stephan Morgenthaler, Robert G. Staudte
Publication date:
2008
Publisher:
Wiley-Interscience
Format:
Paperback
282 pages
Category:
Applied
,
Mathematics
FREE US shipping
Book details
ISBN-13:
9780470028643
ISBN-10:
0470028645
Edition:
1
Author:
Elena Kulinskaya, Stephan Morgenthaler, Robert G. Staudte
Publication date:
2008
Publisher:
Wiley-Interscience
Format:
Paperback
282 pages
Category:
Applied
,
Mathematics
Summary
Meta Analysis: A Guide to Calibrating and Combining Statistical Evidence (ISBN-13: 9780470028643 and ISBN-10: 0470028645), written by authors
Elena Kulinskaya, Stephan Morgenthaler, Robert G. Staudte, was published by Wiley-Interscience in 2008.
With an overall rating of 4.5 stars, it's a notable title among other
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Description
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. 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.
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.
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