A Mathematical Primer for Social Statistics
eBook - ePub

A Mathematical Primer for Social Statistics

  1. English
  2. ePUB (mobile friendly)
  3. Available on iOS & Android
eBook - ePub

A Mathematical Primer for Social Statistics

About this book

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.

 

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Yes, you can access A Mathematical Primer for Social Statistics by John Fox in PDF and/or ePUB format, as well as other popular books in Social Sciences & Social Science Research & Methodology. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Half Title
  3. Series
  4. Acknowledgements
  5. Publisher Note
  6. Title Page
  7. Copyright Page
  8. CONTENTS
  9. Series
  10. Acknowledgements
  11. Preface
  12. Contributors
  13. Chapter 1. Matrices, Linear Algebra, and Vector Geometry: The Basics
  14. Chapter 2. Matrix Decompositions and Quadratic Forms
  15. Chapter 3. An Introduction to Calculus
  16. Chapter 4. Elementary Probability Theory
  17. Chapter 5. Common Probability Distributions
  18. Chapter 6. An Introduction to Statistical Theory
  19. Chapter 7. Putting the Math To Work: Linear Least-Squares Regression
  20. References
  21. Index