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Chapter 8, "Market Share Estimation: Data Mining for an Exceptional Case", illustrates the PCA application for the market shares of some products, with several appendices presenting the needed SAS codes. The book can be useful to practitioners and researchers in the data science and machine-learning data mining. Chapter 36, "Data Reuse: A Powerful Data Mining Effect of the GenIQ Model", continues with description of this non-parametric technique, and presents it by original data and by the transformed variables for a better fit of the dependent variable, with example on the profit model. [Extracted from the article]

i I "The Gaussian distribution can be considered as a special case of the binomial, when the number of tries is sufficiently large." i I "This clearly does not mean that the Poisson distribution has no variance - in that case, it would not be a random variable! It does not account for the discrete nature of these data in the linear regression chapter; or make sense of the three-page derivation of the Gaussian distribution from a Taylor expansion of the Binomial pmf obtained by differentiating in the integer argument. Graph A close friend pointed out this (currently free access) Springer book by physicist Massimiliano Bonamente for its use of the unusual Monte Carlo Markov chain rendering of MCMC. [Extracted from the article]

The article reviews an innovative Evidence-based medicine (EBM) course designed for third- and fourth-year medical students.

Like the earlier edition of this book, the aim and scope of this edition has remained the same and continues to differentiate between statistical data mining and machine-learning data mining. 978-0-367-57360-7 Third Edition Improvements Thirteen new chapters are included in this edition as follows: The first new chapter, Chapter 2, follows Chapter 1 (Introduction). [Extracted from the article]

According to the author, this article grows out of a strong conviction on three points: first, statistics is fundamentally and primarily concerned with analyzing real data, second, data analysis, including inference, is both intellectually challenging and intrinsically interesting, and third, until recently, most authors of introductory statistics textbooks have managed to do a superb job of concealing from their readers the truth of the first two points. Fortunately, the last decade has seen the arrival of a number of innovative introductory textbooks, so the author now find it much more reasonable than in the past to apply high standards in judging an elementary book, according to him. In preparing this article, the author had tried to present these standards systematically; he uses them as an organizing frame for comparing 11 new books with 5 favorites from the past 10 years, according to him. Taken together these textbooks offer the instructor choices as to (a) technical level and quality of exposition, (b) topics covered, and (c) quality of the exercises.

This article presents a list of contemporary textbooks on multivariate statistical analysis. Some of these textbooks are "An Introduction to Multivariate Statistical Analysis," 2nd ed., by T.W. Anderson. "The Theory of Linear Models and Multivariate Analysis," by S.P. Arnold. "Multivariate Statistics: A Vector Space Approach," by M.L. Eston. "Multivariate Statistical Inference," by N.C. Giri. "Methods for Statistical Data Analysis of Multivariate Observations," by R. Gananadesikan. "Multivariate analysis," 2nd ed., by M.G. Kendall. "Multivariate analysis," by A.M. Kshirsagar. "Multivariate Analysis," by K.V. Mardia, J.T. Kent, and J.M. Bibby. "Multivariate Statistical Methods," 2nd ed., by D.P. Morrison. "Aspects of Multivariate Statistical Theory," by R.J. Muirhead. "Applied Multivariate Analysis," 2nd ed., S.J. Press. "Linear Statistical Inference and Its Applications," 2nd ed., by C.R. Rao. "Analysis and Design of Certain Quantitative Multi-Response Experiments," by S.N. Rao and R. Gananadesikan and J.N. Srivastava. And "Multivariate Observations," by G. A. F. Seber.

The article reviews the book "Multiple Regression With Discrete Dependent Variables," by J. G. Orme and T. Combs-Orme.