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A Mathematical Primer for Social Statistics (Quantitative Applications in the Social Sciences) Paperback – July 29, 2008

ISBN-13: 978-1412960809 ISBN-10: 1412960800 Edition: 1st
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Editorial Reviews

About the Author

John Fox is professor of sociology at McMaster University in Hamilton, Ontario, Canada. Fox earned a PhD in sociology from the University of Michigan in 1972, and prior to arriving at McMaster, he taught at the University of Alberta and at York University in Toronto, where he was cross-appointed in the sociology and mathematics and statistics departments and directed the university's statistical consulting service. He has delivered numerous lectures and workshops on statistical topics in North and South America, Europe, and Asia, at such places as the summer program of the Inter-University Consortium for Political and Social Research, the Oxford University Spring School in Quantitative Methods for Social Research, and the annual meetings of the American Sociological Association. Much of his recent work has been on formulating methods for visualizing complex statistical models and on developing software in the R statistical computing environment. He is the author and co-author of many articles, in such journals as Sociological Methodology, Sociological Methods and Research, The Journal of the American Statistical Association, The Journal of Statistical Software, The Journal of Computational and Graphical Statistics, Statistical Science, Social Psychology Quarterly, The Canadian Review of Sociology and Anthropology, and The Canadian Journal of Sociology. He has written a number of other books, including Regression Diagnostics (SAGE, 1991), Nonparametric Simple Regression (SAGE, 2000), Multiple and General-ized Nonparametric Regression (SAGE, 2000), A Mathematical Primer for Social Statistics (SAGE, 2008), and, with Sanford Weisberg, An R Companion to Applied Regression, Second Edition (SAGE, 2010). Fox also edits the SAGE Quantitative Applications in the Social Sciences (QASS) monograph series.

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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: 3.5 out of 5 stars  See all reviews (2 customer reviews)
  • Amazon Best Sellers Rank: #565,980 in Books (See Top 100 in Books)

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10 of 10 people found the following review helpful By Timothy P. Foley on July 31, 2010
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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Format: Paperback Verified Purchase
This is a preliminary review and will be updated when I've finished the book.

So far, I've read Chapter 1 on matrices, vectors, etc.. There are two huge problems with this book so far.

1) This is DEFINITELY not written for people who aren't very well familiar with mathematical terminology. This is about as concise as you can get (a great benefit for some!), and so many like myself will be lost when he quickly describes a proof and moves on. There are examples here and there but very few are applied and don't always clarify what is going on.
2) Very seldom does the author indicate why a certain topic is relevant to social science statistics, and he never goes into depth. He occasionally says, "[x] statistical process uses what we just described," then moves on.

The thoroughness is great. And maybe I'm the wrong audience (took up to calculus 2 at a college level, but never used it in an applied scenario), but this definitely is not helping so far.
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A Mathematical Primer for Social Statistics (Quantitative Applications in the Social Sciences)
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