Buy New
$15.61
Qty:1
  • List Price: $19.00
  • Save: $3.39 (18%)
FREE Shipping on orders over $35.
In Stock.
Ships from and sold by Amazon.com.
Gift-wrap available.
Sell yours for a Gift Card
We'll buy it for $2.00
Learn More
Trade in now
Have one to sell? Sell on Amazon
Flip to back Flip to front
Listen Playing... Paused   You're listening to a sample of the Audible audio edition.
Learn more
See this image

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

Buy New
Price: $15.61
29 New from $14.99 13 Used from $15.40
Rent from Amazon Price New from Used from
eTextbook
"Please retry"
$6.12
Paperback
"Please retry"
$15.61
$14.99 $15.40
Free Two-Day Shipping for College Students with Amazon Student Free%20Two-Day%20Shipping%20for%20College%20Students%20with%20Amazon%20Student


Frequently Bought Together

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)
Price for all three: $49.13

Buy the selected items together
If you buy a new print edition of this book (or purchased one in the past), you can buy the Kindle edition for only $2.99 (Save 80%). Print edition purchase must be sold by Amazon. Learn more.

Best Books of the Year
Best Books of 2014
Looking for something great to read? Browse our editors' picks for 2014's Best Books of the Year in fiction, nonfiction, mysteries, children's books, and much more.

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: #374,431 in Books (See Top 100 in Books)

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.


More About the Author

Discover books, learn about writers, read author blogs, and more.

Customer Reviews

5.0 out of 5 stars
5 star
1
4 star
0
3 star
0
2 star
0
1 star
0
See the customer review
Share your thoughts with other customers

Most Helpful Customer Reviews

9 of 9 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 ›
Comment Was this review helpful to you? Yes No Sending feedback...
Thank you for your feedback. If this review is inappropriate, please let us know.
Sorry, we failed to record your vote. Please try again