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While this chapter focuses on Python, Denis ([6]) uses the opportunity to introduce z-scores, standard deviation, skewness, kurtosis, and different measures of central tendency, demonstrating both the Python code and many of the mathematical equations used to compute these values. I Applied Univariate, Bivariate, and Multivariate Statistics Using Python i by Denis ([6]) is a recent textbook aimed at providing an introduction to statistics along with tutorials on how to apply those methods with Python software. The author has previously written two similar introductory textbooks (Denis, [3], [4]) for statistics, utilizing SPSS and R respectively, along with a more detailed introduction (Denis, [5]) to the discipline that uses both R and SPSS. [Extracted from the article]

As Winter emphasises throughout the book, statistical modelling instead encourages us to think more deeply about what the data actually look like, which mathematical functions would be useful to fit the data and what meanings we can extract from the data. For example, in my own statistics training during my graduate study, I was told that we should include as many factors as possible in ANOVA to soak up variance, but Winter has informative discussion on why this is bad practice, and argues that model construction should be a subjective, theory-driven process (see especially §16.5). Winter repeatedly argues that it is important that the data and the code are published together with a paper, in order to promote open and reproducible research practice in linguistics. Finally, another aspect which makes this book special as a textbook is that Winter challenges some common practices in the field. [Extracted from the article]

There is extensive historical commentary, severe testing is a strong theme, and null hypothesis significance testing gets a remake. The statistics wars continue on! [ABSTRACT FROM AUTHOR]

Statistics for Making Decisions,: by Nicholas T. Longford. Nicholas T. Longford reminds readers of their role and responsibility as facilitators of decisions, and presents a fully fledged statistical approach that can do justice to the task. In addition to a discussion of sample size determination as a function of costs and expected knowledge gain, the chapter also thematizes the importance of avoiding a decision impasse. [Extracted from the article]

This book has been written "for people that have to apply statistical methods in their research but whose main interest is not in theorems and proofs" (from the preface). From there on all statistical procedures are exemplified via R commands starting with point estimation in Chapter 2, one and two sample testing problems in Chapter 3 and confidence estimation in Chapter 4. Chapter 11 on Generalised Linear Models and Chapter 12 on Spatial Statistics have no counterpart in Rasch and Schott ([1]), which testifies to the rather recent addition of geostatistics to the range of methods decent statisticians are supposed to know about. [Extracted from the article]

This is the first edition of Bartolucci, Bacci, and Gnaldi's Statistical Analysis of Questionnaires book and consists of six chapters, which focuses mostly on Item Response Theory (IRT). The sixth and final chapter of the book provides a discussion on some extensions of the traditional IRT model to IRT models with covariates, models for multilevel data, multidimensional IRT models, and IRT models in the structural equation modeling (SEM) framework. The two examples provided in R, using the I MultiLCIRT i package (Bartolucci et al., [1]), are the estimation of a graded response model with covariates using the Russian Longitudinal data and a multilevel and multidimensional (i.e., three dimension) dichotomous IRT model for the Italian education dataset. [Extracted from the article]

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.

There is a peculiar situation in statistics. Statisticians consider statistics as one integral subject, and, at the same time, artificially divide statistics into two conflicting parts: descriptive and inferential. Therefore, statisticians constantly fail in applied statistical research, especially in economics. Therefore, statisticians often lie with statistics and are often deceived by statistics. This situation is considered as a crisis in statistics. The article proposes a theory of coordination between statistical methods and statistical information, and to restore the continuity between notions in the theory of probability and in statistics. The theory of probability is the most abstract mathematical science, with unlimited scope of application. However, the essential question of the theory of probability has not been solved. The application of statistics in economics can be considered as an art where imagination predominates. Statistics is a methodological science and is a dialectical juxtaposition of quality (object, data) and quantity (method). Coordination between data and method is based on the principal rule that a population must be homogeneous.

Highlights from the article: This book is one of the recent most additions in the literature on statistical analysis of fMRI data, with a distinguishing feature that it specifically deals with the analysis of noise in MRI data. Chapter 4 is on fundamentals of noise analysis and Chapter 5 on noise filtering. With only a reasonable background knowledge required, it can be used as a basic guide for someone willing to learn the basic modeling and analysis of noise in MRI data.

If you would like to review a book, and thereby to retain it for your collection, please contact the Book Reviews Editor, whose details can be found by clicking on "book review list" in the information on the Royal Statistical Society's Web site: Sætnan, A. R., Schneider, I. and Green, N. (eds) I The Politics of Big Data: Big Data, Big Brother? Trivedi, K. S. and Bobbio, A. I Reliability and Availability Engineering: Modeling, Analysis, and Applications i , p. 1648. [Extracted from the article]

Reviews the book "Mathematical Statistics," by B.L. Van der Waerden.

Reviews the book "Logic of Statistical Inference," by Ian Hacking.

Reviews the book "The Fundamental Principles of Mathematical Statistics," by Hugh H. Wolfenden.

Reviews the book "Statistical Methods for the Reliability of Repairable Systems," by Steven E. Rigdon and Asit P. Basl.

Reviews the book "Mathematical Statistics and Data Analysis," by John A. Rice.

Reviews the book "Asymptotic Theory of Statistical Inference," by B.L.S. Prakasa Rao.

Reviews the book "Statistical Inference Based on Ranks," by Thomas P. Hettmansperger.

Reviews the book "Introduction to Probability and Mathematical Statistics," by Lee J. Bain and Max Engelhardt.

Reviews the book "Paradoxes in Probability Theory and Mathematical Statistics," by Gabor J. Székely.

Reviews the book "Empirical Processes With Applications to Statistics," by Galen R. Shorack and Jon A. Wellner.

Reviews the book "The Statistics of Natural Selection," by Bryan F.J. Manly.

Reviews the book "Statistics With Applications to the Biological and Health Sciences," 2nd ed., by Richard D. Remington and M. Anthony Schork.

Reviews the book "Functional Relations, Random Coefficients, and Nonlinear Regression With Application to Kinetic Data," by Soren Johansen.

Reviews the book "Multivariate Statistics: A Vector Space Approach," by Morris L. Eaton.

Reviews the book "Multivariate Statistical Analysis," edited by R.P. Gupta.

Reviews two volumes of the book "Statistics and Experimental Design in Engineering and the Physical Sciences," 2nd. ed., by Norman L. Johnson and Fred C. Leone.

Reviews the book "Mathematical Statistics: Basic Ideas and Selected Topics," by Peter J. Bickel and Kjell A. Doksum.

Reviews the book "Statistical Prediction Analysis," by J. Aitchinson and L.R. Dunsmore.

Reviews the book "An Introduction to Probability Theory and Mathematical Statistics," V.K. Rohatgi.

Reviews the book "Probability and Statistics," by Morris Degroot.

Reviews the book "An Introduction to Multivariate Techniques for Social and Behavioral Sciences," by Spencer Bennett and David Bowers.

Reviews the book "Selected Tables in Mathematical Statistics," vol. 3, by H.L. Harter and D.B. Owen.

Reviews the book "Selected Tables in Mathematical Statistics," vol. 2, edited by H.L. Harter and D.B. Owen.

Reviews the book "Comparative Statistical Inference," by Vie Burnett.

Reviews the book 'Statistics,' by D. Freedman, R. Pisani and R. Purves.

Reviews the book "Statistical Models and Their Experimental Application," by Per Ottestad.

Reviews the book "Nonparametric Statistical Inference," by Jean Dickinson Gibbons.

Reviews the book "Selected Tables in Mathematical Statistics," edited by H. Leon Harter and D.B. Owen.

Reviews the book "Practical Nonparametric Statistics," by W.J. Conover.

Reviews the book "Statistical Inference I and II," by Jerome C. R. Li.

Reviews the book "Die sozialen Auswirkungen der Konjunkturschwankungen," by J. Soudek.