A Mathematical Primer for Social Statistics - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook A Mathematical Primer for Social Statistics write by John Fox. This book was released on 2021-01-11. A Mathematical Primer for Social Statistics available in PDF, EPUB and Kindle. A Mathematical Primer for Social Statistics, Second Edition presents mathematics central to learning and understanding statistical methods beyond the introductory level: the basic "language" of matrices and linear algebra and its visual representation, vector geometry; differential and integral calculus; probability theory; common probability distributions; statistical estimation and inference, including likelihood-based and Bayesian methods. The volume concludes by applying mathematical concepts and operations to a familiar case, linear least-squares regression. The Second Edition pays more attention to visualization, including the elliptical geometry of quadratic forms and its application to statistics. It also covers some new topics, such as an introduction to Markov-Chain Monte Carlo methods, which are important in modern Bayesian statistics. A companion website includes materials that enable readers to use the R statistical computing environment to reproduce and explore computations and visualizations presented in the text. The book is an excellent companion to a "math camp" or a course designed to provide foundational mathematics needed to understand relatively advanced statistical methods.
A Mathematical Primer for Social Statistics
A Mathematical Primer for Social Statistics - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook A Mathematical Primer for Social Statistics write by John Fox. This book was released on 2021. A Mathematical Primer for Social Statistics available in PDF, EPUB and Kindle. A Mathematical Primer for Social Statistics, Second Edition presents mathematics central to learning and understanding statistical methods beyond the introductory level: the basic "language" of matrices and linear algebra and its visual representation, vector geometry; differential and integral calculus; probability theory; common probability distributions; statistical estimation and inference, including likelihood-based and Bayesian methods. The volume concludes by applying mathematical concepts and operations to a familiar case, linear least-squares regression.
A Mathematical Primer for Social Statistics
A Mathematical Primer for Social Statistics - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook A Mathematical Primer for Social Statistics write by John Fox. This book was released on 2009. A Mathematical Primer for Social Statistics available in PDF, EPUB and Kindle. The ideal primer for students and researchers across the social sciences who wish to master the necessary maths in order to pursue studies involving advanced statistical methods
Mathematics for Social Scientists
Mathematics for Social Scientists - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Mathematics for Social Scientists write by Jonathan Kropko. This book was released on 2016. Mathematics for Social Scientists available in PDF, EPUB and Kindle.
Applied Regression Analysis and Generalized Linear Models
Applied Regression Analysis and Generalized Linear Models - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Applied Regression Analysis and Generalized Linear Models write by John Fox. This book was released on 2015-03-18. Applied Regression Analysis and Generalized Linear Models available in PDF, EPUB and Kindle. Combining a modern, data-analytic perspective with a focus on applications in the social sciences, the Third Edition of Applied Regression Analysis and Generalized Linear Models provides in-depth coverage of regression analysis, generalized linear models, and closely related methods, such as bootstrapping and missing data. Updated throughout, this Third Edition includes new chapters on mixed-effects models for hierarchical and longitudinal data. Although the text is largely accessible to readers with a modest background in statistics and mathematics, author John Fox also presents more advanced material in optional sections and chapters throughout the book. Accompanying website resources containing all answers to the end-of-chapter exercises. Answers to odd-numbered questions, as well as datasets and other student resources are available on the author′s website. NEW! Bonus chapter on Bayesian Estimation of Regression Models also available at the author′s website.
