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| Author | : John Fox |
| Publisher | : SAGE |
| Total Pages | : 185 |
| Release | : 2009 |
| Genre | : Social Science |
| ISBN | : 1412960800 |
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
| Author | : John Fox |
| Publisher | : SAGE Publications |
| Total Pages | : 199 |
| Release | : 2021-01-11 |
| Genre | : Social Science |
| ISBN | : 1071833243 |
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.
| Author | : Jonathan Kropko |
| Publisher | : |
| Total Pages | : 392 |
| Release | : 2016 |
| Genre | : Social sciences |
| ISBN | : 9781506304199 |
| Author | : John Fox |
| Publisher | : SAGE Publications |
| Total Pages | : 612 |
| Release | : 2015-03-18 |
| Genre | : Social Science |
| ISBN | : 1483321312 |
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.
| Author | : John Levi Martin |
| Publisher | : University of Chicago Press |
| Total Pages | : 377 |
| Release | : 2018-08-21 |
| Genre | : Social Science |
| ISBN | : 022656777X |
Simply put, Thinking Through Statistics is a primer on how to maintain rigorous data standards in social science work, and one that makes a strong case for revising the way that we try to use statistics to support our theories. But don’t let that daunt you. With clever examples and witty takeaways, John Levi Martin proves himself to be a most affable tour guide through these scholarly waters. Martin argues that the task of social statistics isn't to estimate parameters, but to reject false theory. He illustrates common pitfalls that can keep researchers from doing just that using a combination of visualizations, re-analyses, and simulations. Thinking Through Statistics gives social science practitioners accessible insight into troves of wisdom that would normally have to be earned through arduous trial and error, and it does so with a lighthearted approach that ensures this field guide is anything but stodgy.
| Author | : Jeff Gill |
| Publisher | : Cambridge University Press |
| Total Pages | : 449 |
| Release | : 2006-04-24 |
| Genre | : Mathematics |
| ISBN | : 0521834260 |
"More than ever before, modern social scientists require a basic level of mathematical literacy, yet many students receive only limited mathematical training prior to beginning their research careers. This textbook addresses this dilemma by offering a comprehensive, unified introduction to the essential mathematics of social science. Throughout the book the presentation builds from first principles and eschews unnecessary complexity. Most importantly, the discussion is thoroughly and consistently anchored in real social science applications, with more than 80 research-based illustrations woven into the text and featured in end-of-chapter exercises. Students and researchers alike will find this first-of-its-kind volume to be an invaluable resource."--BOOK JACKET.
| Author | : M. D. Edge |
| Publisher | : |
| Total Pages | : 318 |
| Release | : 2019 |
| Genre | : Mathematics |
| ISBN | : 0198827628 |
Focuses on detailed instruction in a single statistical technique, simple linear regression (SLR), with the goal of gaining tools, understanding, and intuition that can be applied to other contexts.
| Author | : Horace C. Levinson |
| Publisher | : Courier Corporation |
| Total Pages | : 388 |
| Release | : 2001-01-01 |
| Genre | : Mathematics |
| ISBN | : 9780486419978 |
In simple, non-technical language, this volume explores the fundamentals governing chance and applies them to sports, government, and business. Topics includenbsp;the theory of probability in relation to superstitions, betting odds, warfare,nbsp;social problems, stocks, and other areas. "Clear and lively ...nbsp;remarkably accurate." —Scientific Monthly.
| Author | : Thomas M. Carsey |
| Publisher | : SAGE Publications |
| Total Pages | : 304 |
| Release | : 2013-08-05 |
| Genre | : Social Science |
| ISBN | : 1483324923 |
Taking the topics of a quantitative methodology course and illustrating them through Monte Carlo simulation, this book examines abstract principles, such as bias, efficiency, and measures of uncertainty in an intuitive, visual way. Instead of thinking in the abstract about what would happen to a particular estimator "in repeated samples," the book uses simulation to actually create those repeated samples and summarize the results. The book includes basic examples appropriate for readers learning the material for the first time, as well as more advanced examples that a researcher might use to evaluate an estimator he or she was using in an actual research project. The book also covers a wide range of topics related to Monte Carlo simulation, such as resampling methods, simulations of substantive theory, simulation of quantities of interest (QI) from model results, and cross-validation. Complete R code from all examples is provided so readers can replicate every analysis presented using R.
