Introduction to Applied Bayesian Statistics and Estimation for Social Scientists - Info and Reading Options
By Scott M. Lynch
"Introduction to Applied Bayesian Statistics and Estimation for Social Scientists" is published by Springer in July 27, 2007, the book is classified in Social Science genre, it has 364 pages and the language of the book is English.
“Introduction to Applied Bayesian Statistics and Estimation for Social Scientists” Metadata:
- Title: ➤ Introduction to Applied Bayesian Statistics and Estimation for Social Scientists
- Author: Scott M. Lynch
- Language: English
- Number of Pages: 364
- Is Family Friendly: Yes - No Mature Content
- Publisher: Springer
- Publish Date: July 27, 2007
- Genres: Social Science
“Introduction to Applied Bayesian Statistics and Estimation for Social Scientists” Subjects and Themes:
- Subjects: ➤ Statistical methods - Bayesian statistical decision theory - Methode van Bayes - Statistische analyse - Social sciences - Social sciences, statistical methods - Methodology
Edition Specifications:
- Format: Hardcover
- Weight: 1.4 pounds
- Dimensions: 9.3 x 6.1 x 0.9 inches
Edition Identifiers:
- Google Books ID: AQkizgEACAAJ
- The Open Library ID: OL10154701M - OL9336032W
- Online Computer Library Center (OCLC) ID: 124025392
- Library of Congress Control Number (LCCN): 2007929729
- ISBN-13: 9780387712642
- ISBN-10: 038771264X
- All ISBNs: 038771264X - 9780387712642
AI-generated Review of “Introduction to Applied Bayesian Statistics and Estimation for Social Scientists”:
Snippets and Summary:
This book outlines Bayesian statistical analysis in great detail, from the development of a model through the process of making statistical inference.
"Introduction to Applied Bayesian Statistics and Estimation for Social Scientists" Description:
Google Books:
This book outlines Bayesian statistical analysis in great detail, from the development of a model through the process of making statistical inference. The key feature of this book is that it covers models that are most commonly used in social science research - including the linear regression model, generalized linear models, hierarchical models, and multivariate regression models - and it thoroughly develops each real-data example in painstaking detail.
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