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      A Mathematical Primer for Social Statistics

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      SKU 9781071833209 Categories ,
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      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...

      £33.99

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      Description

      Product ID:9781071833209
      Product Form:Paperback / softback
      Country of Manufacture:US
      Series:Quantitative Applications in the Social Sciences
      Title:A Mathematical Primer for Social Statistics
      Authors:Author: John Fox
      Page Count:256
      Subjects:Research methods: general, Research methods: general, Social research and statistics, Political science and theory, Social research & statistics, Political science & theory
      Description:Select Guide Rating
      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. 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.

       


      Imprint Name:SAGE Publications Inc
      Publisher Name:SAGE Publications Inc
      Country of Publication:GB
      Publishing Date:2021-03-02

      Additional information

      Weight324 g
      Dimensions138 × 214 × 20 mm