Request Inspection Copy

If you are an Academic or Teacher and wish to consider this book as a prescribed textbook for your course, you may be eligible for a complimentary inspection copy. Please complete this form, including information about your position, campus and course, before adding to cart.

* Required Fields

To complete your Inspection Copy Request you will need to click the Checkout button in the right margin and complete the checkout formalities. You can include Inspection Copies and purchased items in the same shopping cart, see our Inspection Copy terms for further information.

Any Questions? Please email our text Support Team on


Email this to a friend

* ALL required Fields

Order Inspection Copy

An inspection copy has been added to your shopping cart

Mathematical Primer for Social Statistics

by John Fox SAGE Publications, Inc
Pub Date:
Pbk 184 pages
AU$47.99 NZ$48.69
Product Status: Available in Approx 14 days
add to your cart
Available as eBook
AU$38.95 | NZ$43.00

Other Available Formats:

A Mathematical Primer for Social Statistics: Beyond the introductory level, learning and effectively using statistical methods in the social sciences requires some knowledge of mathematics. It is, however, surprising how far one can go with a relatively modest mathematical background. The proposed monograph aims to provide that background, introducing the areas of mathematics that are most centrally important to applied social statistics: matrices, linear algebra, and vector geometry; basic differential and integral calculus, including multivariable and matrix calculus, and the application of calculus to optimization problems; and probability and estimation, including the basics of probability theory, discrete and continuous random variables, commonly encountered statistical distributions, principles of estimation, the method of maximum likelihood and the basics of Bayesian inference. It would be advantageous to cover all these topics in a single book.The expected readership includes advanced undergrads, graduate students, and researchers in the social sciences who wish to learn and use relatively advanced statistical methods, especially regression type of analysis, but whose mathematical preparation for this work is insufficient. There should be a potentially large market. The title should be able to stand alone or bundled together with our existing numbers in two ways: it could go together with many of the volumes on regression analysis, and it could also help fill the gap in foundation with our existing numbers on matrix algebra and calculus. I expect it will do better in the former role of bundling together with existing volumes on regression.

Series Editor Introduction
1 - Matrices, Linear Algebra, Vector Geometry
2 - An Introduction to Calculus
3 - Probability and Estimation
4 - Putting the Math to Work

John Fox is Professor of Sociology at McMaster University in Hamilton, Ontario, Canada. He was previously Professor of Sociology and of Mathematics and Statistics at York University in Toronto, where he also directed the Statistical Consulting Service at the Institute for Social Research. 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 and the annual meetings of the American Sociological Association. His recent and current work includes research on statistical methods (for example, work on three-dimensional statistical graphs) and on Canadian society (for example, a study of political polls in the 1995 Quebec sovereignty referendum). He is author of many articles, in such journals as Sociological Methodology, The Journal of Computational and Graphical Statistics, The Journal of the American Statistical Association, The Canadian Review of Sociology and Anthropology, and The Canadian Journal of Sociology. He has written several other books, including Applied Regression Analysis, Linear Models, and Related Methods (Sage, 1997), Nonparametric Simple Regression (Sage, 2000), and Multiple and Generalized Nonparametric Regression (Sage, 2000).