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A Mathematical Primer for Social Statistics (Quantitative Applications in the Social Sciences) [Paperback]

John Fox
5.0 out of 5 stars  See all reviews (1 customer review)

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Book Description

July 29, 2008 1412960800 978-1412960809 1

John Fox’s A Mathematical Primer for Social Statistics covers many often ignored yet important topics in mathematics and mathematical statistics. This text provides readers with the foundation on which an understanding of applied statistics rests.

Key Features

· Covers matrices, linear algebra, and vector geometry

· Discusses basic differential and integral calculus

· Focuses on probability and statistical estimation

· Develops by way of illustration the seminal statistical method of linear least-squares regression

Intended Audience

This book is ideal for advanced undergraduates, graduate students, and researchers in the social sciences who need to understand and use relatively advanced statistical methods but whose mathematical preparation for this work is insufficient.

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A Mathematical Primer for Social Statistics (Quantitative Applications in the Social Sciences) + Basic Math for Social Scientists: Concepts (Quantitative Applications in the Social Sciences) + Basic Math for Social Scientists: Problems and Solutions (Quantitative Applications in the Social Sciences)
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Editorial Reviews

About the Author

John Fox is the Senator William McMaster Professor of Social Statistics in the Sociology Department of McMaster University in Hamilton, Ontario, Canada. Professor Fox earned a Ph.D. in sociology from the University of Michigan in 1972. He has delivered numerous lectures and workshops on statistical topics, at such places as the summer program of the Inter-University Consortium for Political and Social Research, the annual meetings of the American Sociological Association, and the Oxford Spring School in Quantitative Methods for Social Research. He has written many articles on statistics, sociology, and social psychology, and is the author of several books on statistics, including most recently Applied Regression Analysis and Generalized Linear Models, Second Edition (Sage, 2008) and A Mathematical Primer for Social Statistics (Sage, 2009), and (with Sanford Weisberg) An R Companion to Applied Regression, Second Edition (Sage, 2011). Professor Fox is an active contributor to the R Project for Statistical Computing and is a member of the R Foundation. His work on this book was partly supported by a grant from the Social Sciences and Humanities Research Council of Canada..

Product Details

  • Series: Quantitative Applications in the Social Sciences (Book 159)
  • Paperback: 184 pages
  • Publisher: SAGE Publications, Inc; 1 edition (July 29, 2008)
  • Language: English
  • ISBN-10: 1412960800
  • ISBN-13: 978-1412960809
  • Product Dimensions: 8.3 x 5.4 x 0.4 inches
  • Shipping Weight: 8 ounces (View shipping rates and policies)
  • Average Customer Review: 5.0 out of 5 stars  See all reviews (1 customer review)
  • Amazon Best Sellers Rank: #522,230 in Books (See Top 100 in Books)

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9 of 9 people found the following review helpful
Format:Paperback|Verified Purchase
This gem of a book provides a concise and quite readable overview of important mathematical concepts that are important for understanding both basic and advanced statistical methods, not only for sociologists, but any practitioner of applied statistics. John Fox has successfully pulled together and integrated into a 166 page primer mathematical concepts that often appear scattered in more advanced mathematics courses, including advanced calculus courses that many users of applied statistical methods might not have taken. The book starts with an introduction and overview of matrices, linear algebra, and vector geometry. The material in this chapter is absolutely essential to anyone who wishes to use multivariate methods such as principle components analysis and should be mastered even if the reader chooses not to finish the book. The following chapter provides an astonishingly practical review of differential and intgeral calculus concepts important to statistics; after a brief review of planes, functions, and limits of functions, Dr. Fox quickly reviews differentiation of functions,optimization problems involving derivatives, multivariate and matrix differential calculus, Taylor's theorem, and finally an overview of integral calculus whose concepts are important in understanding probability distributions. Chapter 3 provides an excellent introduction to mathematical statistics to include properties of various distributions and estimators, maximum-likelihood estimation, and an introduction to Bayesian inference. Chapter 4 entitled "Putting the Math to Work: Linear Least Squares Regression" closes out this primer and shows how the method of least squares and the Gauss-Markov Theorem is used to estimate regression parameters and associated statistics. Read more ›
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