Introduction to applied Bayesian statistics and estimation for social scientists / Scott M. Lynch.
By: Lynch, Scott M. (Scott Michael)
Material type: 
TextLanguage: English Series: Statistics for social and behavioral sciencesPublisher: New York : Springer, c2007Description: xxviii, 357 p. : ill. ; 24 cmISBN: 9780387712642 (hardcover : alk. paper)Subject(s): Social sciences -- Statistical methods | Bayesian statistical decision theoryDDC classification: 519.5 LYI LOC classification: HA29 | .L973 2007Online resources: Table of contents | OCLC | E-book Fulltext | Item type | Current location | Collection | Call number | Copy number | Status | Date due | Barcode | Item holds |
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EWU Library E-book | Non-fiction | 519.5 LYI 2007 (Browse shelf) | Not For Loan | ||||
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EWU Library Reserve Section | Non-fiction | 519.5 LYI 2007 (Browse shelf) | C-1 | Not For Loan | 25614 | ||
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EWU Library Circulation Section | Non-fiction | 519.5 LYI 2007 (Browse shelf) | C-2 | Available | 26049 |
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| 519.5 LIS 2012 Statistical techniques in business & economics / | 519.5 LIS 2012 Statistical techniques in business & economics / | 519.5 LIS 2018 Statistical techniques in business & economics / | 519.5 LYI 2007 Introduction to applied Bayesian statistics and estimation for social scientists / | 519.5 MAR 2006 Robust statistics : | 519.5 MAS 1996 Statistical techniques in business and economics / | 519.5 MAT 2008 Mathematical statistics with applications / |
Includes bibliographical references (p. [345]-351) and index.
1. Introduction --
2. Probability theory and classical statistics --
3. Basics of Bayesian statistics --
4. Modern model estimation part 1 : Gibbs sampling --
5. Modern model estimation part 2 : Metropolis-Hastings sampling --
6. Evaluating Markov chain Monte Carlo algorithms and model fit --
7. The linear regression model --
8. Generalized linear models --
9. Introduction to hierarchical models --
10. Introduction to multivariate regression models --
11. Conclusion --
A. Background mathematics --
B. The central limit theorem, confidence intervals, and hypothesis tests.
Lynch covers the complete process of Bayesian statistical analysis in great detail from the development of a model through the process of making statistical inference. The key feature of the book is that it covers models that are most commonly used on social science research.
Applied Statistics
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