000			02191nam a22002297a 4500
999			_c4113 _d4113
005			20221013125025.0
008			221013b ||||| |||| 00| 0 eng d
020			_a9781071833209
082			_a519.5 _bFOX
100			_aFox, John _99316
245			_aA mathematical primer for social statistics
250			_a2nd
260			_bSage Publications, Inc. _aCalifornia _c2021
300			_axxiii, 229 p.
365			_aUSD _b30.00
490			_aQuantitative Applications in the Social Sciences
504			_aTable of content Matrices, Linear Algebra, and Vector Geometry: The Basics Matrix Decompositions and Quadratic Forms An Introduction to Calculus Elementary Probability Theory Common Probability Distributions An Introduction to Statistical Theory Putting the Math to Work: Linear Least-Squares Regression
520			_aA Mathematical Primer for Social Statistics, Second Edition presents mathematics central to learning and understanding statistical methods beyond the introductory level: the basic "language" of matrices and linear algebra and its visual representation, vector geometry; differential and integral calculus; probability theory; common probability distributions; statistical estimation and inference, including likelihood-based and Bayesian methods. The volume concludes by applying mathematical concepts and operations to a familiar case, linear least-squares regression. The Second Edition pays more attention to visualization, including the elliptical geometry of quadratic forms and its application to statistics. It also covers some new topics, such as an introduction to Markov-Chain Monte Carlo methods, which are important in modern Bayesian statistics. A companion website includes materials that enable readers to use the R statistical computing environment to reproduce and explore computations and visualizations presented in the text. The book is an excellent companion to a "math camp" or a course designed to provide foundational mathematics needed to understand relatively advanced statistical methods.
650			_aSocial sciences--Mathematics _99649
650			_aSocial sciences--Statistical methods _91897
942			_2ddc _cBK