A mathematical primer for social statistics

著者

    • Fox, John
    • Sage (Firm)

書誌事項

A mathematical primer for social statistics

John Fox, McMaster University

（Quantitative applications in the social sciences）

SAGE, 2020 , , © 2011

Second Edition

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注記

First edition published 2009

Includes bibliographical references

Summary: "A Mathematical Primer for Social Statistics, Second Edition is organized around bodies of mathematical knowledge central to learning and understanding advanced statistics: the basic "language" of linear algebra; differential and integral calculus; probability theory; common probability distributions; statistical estimation and inference. The volume concludes showing the application of mathematical concepts and operations to the familiar case, linear least-squares regression. The Second Edition gives much more attention to visualization. It also covers some new topics, such as an introduction to Markov-Chain Monte Carlo methods. Also included is a companion website with materials that will enable readers to use the R statistical computing environment to reproduce and expand on computations presented in the volume. The book will make an excellent text to accompany a math camp or a course designed to provide foundational mathematics needed to understand advanced statistics. It will also serve as a valu

収録内容

  • 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

内容説明・目次

内容説明

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. 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.

目次

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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