{"product_id":"statistical-distributions-paperback-softback-9780470390634","title":"Statistical Distributions (Paperback \/ softback) 9780470390634","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eStatistical Distributions\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eCatherine Forbes (Author), Merran Evans (Author), Nicholas Hastings (Author), Brian Peacock (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780470390634, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback \/ softback, published 7 January 2011\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e230 pages, Drawings: 2 B\u0026amp;W, 0 Color; Graphs: 81 B\u0026amp;W, 0 Color\u003cbr\u003e23.6 x 15.8 x 1.3 cm, 0.349 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e\"Overall, an excellent book for readers interested in qualitative data analysis. Highly recommended. Upper-division undergraduates through professionals.\" (Choice, 1 October 2011)\u003cbr\u003e \u003cbr\u003e   \u003cp\u003e\"This new edition continues to illustrate the application of statistical methods to research across various disciplines, including medicine, engineering, business\/finance, and the social sciences. Thoroughly revised and updated, the authors have refreshed this book to reflect the changes and current trends in statistical distribution theory that have occured since the publication of the previous edition eight years ago . . . key facts and formulas for forty major probability distributions are presented, making the book an ideal introduction to the general theory of statistical distributions as well as a quick reference on its basic principles\". (MyCFO, 22 December 2010)\u003c\/p\u003e \u003cp\u003e\"This new edition continues to illustrate the application of statistical methods to research across various disciplines, including medicine, engineering, business\/finance, and the social sciences. Thoroughly revised and updated, the authors have refreshed this book to reflect the changes and current trends in statistical distribution theory that have occured since the publication of the previous edition eight years ago. The introductory chapters introduce the fundamental concepts of the distributions and the relationships between variables. For each distribution that follows, the key formulae, tables and diagrams are presented in a concise, user-friendly format. Key facts and formulas for forty major probability distributions are presented, making the book an ideal introduction to the general theory of statistical distributions as well as a quick reference on its basic principles\". (MyCFO, 22 December 2010)\u003c\/p\u003e\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cb\u003eA new edition of the trusted guide on commonly used statistical distributions\u003c\/b\u003e  \u003cp\u003eFully updated to reflect the latest developments on the topic, \u003ci\u003eStatistical Distributions\u003c\/i\u003e, Fourth Edition continues to serve as an authoritative guide on the application of statistical methods to research across various disciplines. The book provides a concise presentation of popular statistical distributions along with the necessary knowledge for their successful use in data modeling and analysis.\u003c\/p\u003e \u003cp\u003eFollowing a basic introduction, forty popular distributions are outlined in individual chapters that are complete with related facts and formulas. Reflecting the latest changes and trends in statistical distribution theory, the \u003ci\u003eFourth Edition\u003c\/i\u003e features:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eA new chapter on queuing formulas that discusses standard formulas that often arise from simple queuing systems\u003c\/li\u003e \u003cli\u003eMethods for extending independent modeling schemes to the dependent case, covering techniques for generating complex distributions from simple distributions\u003c\/li\u003e \u003cli\u003eNew coverage of conditional probability, including conditional expectations and joint and marginal distributions\u003c\/li\u003e \u003cli\u003eCommonly used tables associated with the normal (Gaussian), student-t, F and chi-square distributions\u003c\/li\u003e \u003cli\u003eAdditional reviewing methods for the estimation of unknown parameters, such as the method of percentiles, the method of moments, maximum likelihood inference, and Bayesian inference\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003cb\u003eStatistical Distributions\u003c\/b\u003e, Fourth Edition is an excellent supplement for upper-undergraduate and graduate level courses on the topic. It is also a valuable reference for researchers and practitioners in the fields of engineering, economics, operations research, and the social sciences who conduct statistical analyses.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e1 Introduction.  \u003cp\u003e\u003cb\u003e2 Terms and Symbols.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Probability, Random Variable, Variate and Number.\u003c\/p\u003e \u003cp\u003e2.2 Range, Quantile, Probability and Domain.\u003c\/p\u003e \u003cp\u003e2.3 Distribution Function and Survival Function.\u003c\/p\u003e \u003cp\u003e2.4 Inverse Distribution and Inverse Survival Function.\u003c\/p\u003e \u003cp\u003e2.5 Probability Density Function and Probability Function.\u003c\/p\u003e \u003cp\u003e2.6 Other Associated Functions and Quantities.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 General Variate Relationships.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction.\u003c\/p\u003e \u003cp\u003e3.2 Function of a Variate.\u003c\/p\u003e \u003cp\u003e3.3 One-to-One Transformations and Inverses.\u003c\/p\u003e \u003cp\u003e3.4 Variate Relationships Under One-to-One Transformation.\u003c\/p\u003e \u003cp\u003e3.5 Parameters, Variate, and Function Notation.\u003c\/p\u003e \u003cp\u003e3.6 Transformation of Location and Scale.\u003c\/p\u003e \u003cp\u003e3.7 Transformation from the Rectangular Variate.\u003c\/p\u003e \u003cp\u003e3.8 Many-to-One Transformations.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Multivariate Distributions.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Joint Distributions.\u003c\/p\u003e \u003cp\u003e4.2 Marginal Distributions.\u003c\/p\u003e \u003cp\u003e4.3 Independence.\u003c\/p\u003e \u003cp\u003e4.4 Conditional Distributions.\u003c\/p\u003e \u003cp\u003e4.5 Bayes' Theorem.\u003c\/p\u003e \u003cp\u003e4.6 Functions of a Multivariate.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Stochastic Modeling.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction.\u003c\/p\u003e \u003cp\u003e5.2 Independent Variates.\u003c\/p\u003e \u003cp\u003e5.3 Mixture Distributions.\u003c\/p\u003e \u003cp\u003e5.4 Skew-Symmetric Distributions.\u003c\/p\u003e \u003cp\u003e5.5 Conditional Skewness.\u003c\/p\u003e \u003cp\u003e5.6 Dependent Variates.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Parameter Inference.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction.\u003c\/p\u003e \u003cp\u003e6.2 Method of Percentiles Estimation.\u003c\/p\u003e \u003cp\u003e6.3 Method of Moments Estimation.\u003c\/p\u003e \u003cp\u003e6.4 Maximum Likelihood Inference.\u003c\/p\u003e \u003cp\u003e6.5 Bayesian Inference.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Bernoulli Distribution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Random Number Generation.\u003c\/p\u003e \u003cp\u003e7.2 Curtailed Bernoulli Trial Sequences.\u003c\/p\u003e \u003cp\u003e7.3 Urn Sampling Scheme.\u003c\/p\u003e \u003cp\u003e7.4 Note.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Beta Distribution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Notes on Beta and Gamma Functions.\u003c\/p\u003e \u003cp\u003e8.2 Variate Relationships.\u003c\/p\u003e \u003cp\u003e8.3 Parameter Estimation.\u003c\/p\u003e \u003cp\u003e8.4 Random Number Generation.\u003c\/p\u003e \u003cp\u003e8.5 Inverted Beta Distribution.\u003c\/p\u003e \u003cp\u003e8.6 Noncentral Beta Distribution.\u003c\/p\u003e \u003cp\u003e8.7 Beta Binomial Distribution.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Binomial Distribution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Variate Relationships.\u003c\/p\u003e \u003cp\u003e9.2 Parameter Estimation.\u003c\/p\u003e \u003cp\u003e9.3 Random Number Generation.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Cauchy Distribution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Note.\u003c\/p\u003e \u003cp\u003e10.2 Variate Relationships.\u003c\/p\u003e \u003cp\u003e10.3 Random Number Generation.\u003c\/p\u003e \u003cp\u003e10.4 Generalized Form.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Chi-Squared Distribution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Variate Relationships.\u003c\/p\u003e \u003cp\u003e11.2 Random Number Generation.\u003c\/p\u003e \u003cp\u003e11.3 Chi Distribution.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Chi-Squared (Noncentral) Distribution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Variate Relationships.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Dirichlet Distribution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Variate Relationships.\u003c\/p\u003e \u003cp\u003e13.2 Dirichlet Multinomial Distribution.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Empirical Distribution Function.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Estimation from Uncensored Data.\u003c\/p\u003e \u003cp\u003e14.2 Estimation from Censored Data.\u003c\/p\u003e \u003cp\u003e14.3 Parameter Estimation.\u003c\/p\u003e \u003cp\u003e14.4 Example.\u003c\/p\u003e \u003cp\u003e14.5 Graphical Method for the Modified Order-Numbers.\u003c\/p\u003e \u003cp\u003e14.6 Model Accuracy.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Erlang Distribution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Variate Relationships.\u003c\/p\u003e \u003cp\u003e15.2 Parameter Estimation.\u003c\/p\u003e \u003cp\u003e15.3 Random Number Generation.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Error Distribution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Note.\u003c\/p\u003e \u003cp\u003e16.2 Variate Relationships.\u003c\/p\u003e \u003cp\u003e17 Exponential Distribution.\u003c\/p\u003e \u003cp\u003e17.1 Note.\u003c\/p\u003e \u003cp\u003e17.2 Variate Relationships.\u003c\/p\u003e \u003cp\u003e17.3 Parameter Estimation.\u003c\/p\u003e \u003cp\u003e17.4 Random Number Generation.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Exponential Family.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e18.1 Members of the Exponential Family.\u003c\/p\u003e \u003cp\u003e18.2 Univariate One-Parameter Exponential Family.\u003c\/p\u003e \u003cp\u003e18.3 Estimation.\u003c\/p\u003e \u003cp\u003e18.4 Generalized Exponential Distributions.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Extreme Value (Gumbel) Distribution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e19.1 Note.\u003c\/p\u003e \u003cp\u003e19.2 Variate Relationships.\u003c\/p\u003e \u003cp\u003e19.3 Parameter Estimation.\u003c\/p\u003e \u003cp\u003e19.4 Random Number Generation.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 F (Variance Ratio) or Fisher{ Snedecor Distribution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e20.1 Variate Relationships.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 F (Noncentral) Distribution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e21.1 Variate Relationships.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22 Gamma Distribution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e22.1 Variate Relationships.\u003c\/p\u003e \u003cp\u003e22.2 Parameter Estimation.\u003c\/p\u003e \u003cp\u003e22.3 Random Number Generation.\u003c\/p\u003e \u003cp\u003e22.4 Inverted Gamma Distribution.\u003c\/p\u003e \u003cp\u003e22.5 Normal Gamma Distribution.\u003c\/p\u003e \u003cp\u003e22.6 Generalized Gamma Distribution.\u003c\/p\u003e \u003cp\u003e22.6.1 Variate Relationships.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e23 Geometric Distribution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e23.1 Notes.\u003c\/p\u003e \u003cp\u003e23.2 Variate Relationships.\u003c\/p\u003e \u003cp\u003e23.3 Random Number Generation.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e24 Hypergeometric Distribution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e24.1 Note.\u003c\/p\u003e \u003cp\u003e24.2 Variate Relationships.\u003c\/p\u003e \u003cp\u003e24.3 Parameter Estimation.\u003c\/p\u003e \u003cp\u003e24.4 Random Number Generation.\u003c\/p\u003e \u003cp\u003e24.5 Negative Hypergeometric Distribution.\u003c\/p\u003e \u003cp\u003e24.6 Generalized Hypergeometric (Series) Distribution.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e25 Inverse Gaussian (Wald) Distribution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e25.1 Variate Relationships.\u003c\/p\u003e \u003cp\u003e25.2 Parameter Estimation.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e26 Laplace Distribution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e26.1 Variate Relationships.\u003c\/p\u003e \u003cp\u003e26.2 Parameter Estimation.\u003c\/p\u003e \u003cp\u003e26.3 Random Number Generation.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e27 Logarithmic Series Distribution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e27.1 Variate Relationships.\u003c\/p\u003e \u003cp\u003e27.2 Parameter Estimation.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e28 Logistic Distribution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e28.1 Notes.\u003c\/p\u003e \u003cp\u003e28.2 Variate Relationships.\u003c\/p\u003e \u003cp\u003e28.3 Parameter Estimation.\u003c\/p\u003e \u003cp\u003e28.4 Random Number Generation.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e29 Lognormal Distribution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e29.1 Variate Relationships.\u003c\/p\u003e \u003cp\u003e29.2 Parameter Estimation.\u003c\/p\u003e \u003cp\u003e29.3 Random Number Generation.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e30 Multinomial Distribution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e30.1 Variate Relationships.\u003c\/p\u003e \u003cp\u003e30.2 Parameter Estimation.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e31 Multivariate Normal (Multinormal) Distribution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e31.1 Variate Relationships.\u003c\/p\u003e \u003cp\u003e31.2 Parameter Estimation.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e32 Negative Binomial Distribution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e32.1 Note.\u003c\/p\u003e \u003cp\u003e32.2 Variate Relationships.\u003c\/p\u003e \u003cp\u003e32.3 Parameter Estimation.\u003c\/p\u003e \u003cp\u003e32.4 Random Number Generation.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e33 Normal (Gaussian) Distribution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e33.1 Variate Relationships.\u003c\/p\u003e \u003cp\u003e33.2 Parameter Estimation.\u003c\/p\u003e \u003cp\u003e33.3 Random Number Generation.\u003c\/p\u003e \u003cp\u003e33.4 Truncated Normal Distribution.\u003c\/p\u003e \u003cp\u003e33.5 Variate Relationships.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e34 Pareto Distribution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e34.1 Note.\u003c\/p\u003e \u003cp\u003e34.2 Variate Relationships.\u003c\/p\u003e \u003cp\u003e34.3 Parameter Estimation.\u003c\/p\u003e \u003cp\u003e34.4 Random Number Generation.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e35 Poisson Distribution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e35.1 Note.\u003c\/p\u003e \u003cp\u003e35.2 Variate Relationships.\u003c\/p\u003e \u003cp\u003e35.3 Parameter Estimation.\u003c\/p\u003e \u003cp\u003e35.4 Random Number Generation.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e36 Power Function Distribution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e36.1 Variate Relationships.\u003c\/p\u003e \u003cp\u003e36.2 Parameter Estimation.\u003c\/p\u003e \u003cp\u003e36.3 Random Number Generation.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e37 Power Series (Discrete) Distribution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e37.1 Note.\u003c\/p\u003e \u003cp\u003e37.2 Variate Relationships.\u003c\/p\u003e \u003cp\u003e37.3 Parameter Estimation.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e38 Queuing Formulas.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e38.1 Characteristics of Queuing Systems.\u003c\/p\u003e \u003cp\u003e38.2 Definitions, Notation and Terminology.\u003c\/p\u003e \u003cp\u003e38.3 General Formulas.\u003c\/p\u003e \u003cp\u003e38.4 Some Standard Queuing Systems.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e39 Rayleigh Distribution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e39.1 Variate Relationships.\u003c\/p\u003e \u003cp\u003e39.2 Parameter Estimation.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e40 Rectangular (Uniform) Continuous Distribution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e40.1Variate Relationships.\u003c\/p\u003e \u003cp\u003e40.2 Parameter Estimation.\u003c\/p\u003e \u003cp\u003e40.3 Random Number Generation.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e41 Rectangular (Uniform) Discrete Distribution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e41.1 General Form.\u003c\/p\u003e \u003cp\u003e41.2 Parameter Estimation.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e42 Student's t Distribution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e42.1 Variate Relationships.\u003c\/p\u003e \u003cp\u003e42.2 Random Number Generation.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e43 Student's t (Noncentral) Distribution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e43.1 Variate Relationships.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e44 Triangular Distribution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e44.1 Variate Relationships.\u003c\/p\u003e \u003cp\u003e44.2 Random Number Generation.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e45 von Mises Distribution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e45.1 Note.\u003c\/p\u003e \u003cp\u003e45.2 Variate Relationships.\u003c\/p\u003e \u003cp\u003e45.3 Parameter Estimation.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e46 Weibull Distribution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e46.1 Note.\u003c\/p\u003e \u003cp\u003e46.2 Variate Relationships.\u003c\/p\u003e \u003cp\u003e46.3 Parameter Estimation.\u003c\/p\u003e \u003cp\u003e46.4 Random Number Generation.\u003c\/p\u003e \u003cp\u003e46.5 Three-Parameter Weibull Distribution.\u003c\/p\u003e \u003cp\u003e46.6Three-Parameter Weibull Random Number Generation.\u003c\/p\u003e \u003cp\u003e46.7 Bi-Weibull Distribution.\u003c\/p\u003e \u003cp\u003e46.8 Five-Parameter Bi-Weibull Distribution.\u003c\/p\u003e \u003cp\u003eBi-Weibull Random Number Generation.\u003c\/p\u003e \u003cp\u003eBi-Weibull Graphs.\u003c\/p\u003e \u003cp\u003e46.9 Weibull Family.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e47 Wishart (Central) Distribution.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e47.1 Note.\u003c\/p\u003e \u003cp\u003e47.2 Variate Relationships.\u003c\/p\u003e \u003cp\u003e48 Statistical Tables.\u003c\/p\u003e \u003cp\u003eBibliography.\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Mathematics [\u003ca title=\"See our other books on Mathematics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Mathematics%20%5BPB%5D%22\"\u003ePB\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley","offers":[{"title":"Brand New","offer_id":52173782843672,"sku":"9780470390634","price":61.98,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9780470390634.jpg?v=1781171883","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/statistical-distributions-paperback-softback-9780470390634","provider":"Freshly Printed Books","version":"1.0","type":"link"}