A 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.
A 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.
John Fox received a BA from the City College of New York and a PhD from the University of Michigan, both in Sociology. He is Professor Emeritus of Sociology at McMaster University in Hamilton, Ontario, Canada, where he was previously the Senator William McMaster Professor of Social Statistics. Prior to coming to McMaster, he was Professor of Sociology, Professor of Mathematics and Statistics, and Coordinator of the Statistical Consulting Service at York University in Toronto. Professor Fox is the author of many articles and books on applied statistics, including \emph{Applied Regression Analysis and Generalized Linear Models, Third Edition} (Sage, 2016). He is an elected member of the R Foundation, an associate editor of the Journal of Statistical Software, a prior editor of R News and its successor the R Journal, and a prior editor of the Sage Quantitative Applications in the Social Sciences monograph series.
Inhaltsangabe
About the Author Series Editor Introduction Acknowledgments Preface 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 References Index
About the Author Series Editor Introduction Acknowledgments Preface 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 References Index
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