A Mathematical Primer for Social Statistics (Quantitative Applications in the Social Sciences)
| Author | : | |
| Rating | : | 4.61 (681 Votes) |
| Asin | : | 1412960800 |
| Format Type | : | paperback |
| Number of Pages | : | 184 Pages |
| Publish Date | : | 2016-05-26 |
| Language | : | English |
DESCRIPTION:
Timothy P. Foley said An Excellent Primer on Mathematical Concepts for Statistics. 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 s. "Five Stars" according to Administrator. An introduction that is easy to read and understand
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
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 AudienceThis 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 mathematic
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. 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 Met