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Quantitative Applications in the Social Sciences Ser.: Mathematical Primer for Social Statistics by John Fox (2021, Trade Paperback)
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About this product
Product Identifiers
PublisherSAGE Publications, Incorporated
ISBN-101071833200
ISBN-139781071833209
eBay Product ID (ePID)21050086847
Product Key Features
Number of Pages256 Pages
LanguageEnglish
Publication NameMathematical Primer for Social Statistics
SubjectGeneral, Statistics
Publication Year2021
TypeTextbook
AuthorJohn Fox
Subject AreaSocial Science
SeriesQuantitative Applications in the Social Sciences Ser.
FormatTrade Paperback
Dimensions
Item Height0.6 in
Item Weight11 Oz
Item Length8.5 in
Item Width5.5 in
Additional Product Features
Edition Number2
Intended AudienceCollege Audience
LCCN2020-031287
Dewey Edition23
TitleLeadingA
IllustratedYes
Dewey Decimal519.5
Table Of ContentAbout the AuthorSeries Editor IntroductionAcknowledgmentsPrefaceMatrices, Linear Algebra, and Vector Geometry: The BasicsMatrix Decompositions and Quadratic FormsAn Introduction to CalculusElementary Probability TheoryCommon Probability DistributionsAn Introduction to Statistical TheoryPutting the Math to Work: Linear Least-Squares RegressionReferencesIndex
SynopsisA 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., 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.
LC Classification NumberH61.25.F69 2020
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