Statistical Methods & Tests -- Library Resources (sampler):
Beyond Basic Statistics by
Features basic statistical concepts as a tool for thinking critically, wading through large quantities of information, and answering practical, everyday questions Written in an engaging and inviting manner, Beyond Basic Statistics: Tips, Tricks, and Techniques Every Data Analyst Should Know presents the more subjective side of statistics--the art of data analytics. Each chapter explores a different question using fun, common sense examples that illustrate the concepts, methods, and applications of statistical techniques. Without going into the specifics of theorems, propositions, or formulas, the book effectively demonstrates statistics as a useful problem-solving tool. In addition, the author demonstrates how statistics is a tool for thinking critically, wading through large volumes of information, and answering life's important questions. Beyond Basic Statistics: Tips, Tricks, and Techniques Every Data Analyst Should Know also features: Plentiful examples throughout aimed to strengthen readers' understanding of the statistical concepts and methods A step-by-step approach to elementary statistical topics such as sampling, hypothesis tests, outlier detection, normality tests, robust statistics, and multiple regression A case study in each chapter that illustrates the use of the presented techniques Highlights of well-known shortcomings that can lead to false conclusions An introduction to advanced techniques such as validation and bootstrapping Featuring examples that are engaging and non-application specific, the book appeals to a broad audience of students and professionals alike, specifically students of undergraduate statistics, managers, medical professionals, and anyone who has to make decisions based on raw data or compiled results.
Call Number: Science & Engineering Library Book Stacks QA276 .J37 2015ISBN: 9781118856116Publication Date: 2015
A Course in Mathematical Statistics and Large Sample Theory by
1 Introduction -- 2 Decision Theory -- 3 Introduction to General Methods of Estimation -- 4 Sufficient Statistics, Exponential Families, and Estimation -- 5 Testing Hypotheses -- 6 Consistency and Asymptotic Distributions and Statistics -- 7 Large Sample Theory of Estimation in Parametric Models -- 8 Tests in Parametric and Nonparametric Models -- 9 The Nonparametric Bootstrap -- 10 Nonparametric Curve Estimation -- 11 Edgeworth Expansions and the Bootstrap -- 12 Frechet Means and Nonparametric Inference on Non-Euclidean Geometric Spaces -- 13 Multiple Testing and the False Discovery Rate -- 14 Markov Chain Monte Carlo (MCMC) Simulation and Bayes Theory -- 15 Miscellaneous Topics -- Appendices -- Solutions of Selected Exercises in Part 1.
Call Number: Online access -- ebookISBN: 1493940325Publication Date: 2016
Linear Regression by
Part I: Python and Statistics -- Why Statistics? -- Python -- Data Input -- Display of Statistical Data -- Part II: Distributions and Hypothesis Tests -- Background -- Distributions of One Variable -- Hypothesis Tests -- Tests of Means of Numerical Data -- Tests on Categorical Data -- Analysis of Survival Times -- Part III: Statistical Modelling -- Linear Regression Models -- Multivariate Data Analysis -- Tests on Discrete Data -- Bayesian Statistics -- Solutions -- Glossary -- Index.
This textbook provides an introduction to the free software Python and its use for statistical data analysis. It covers common statistical tests for continuous, discrete and categorical data, as well as linear regression analysis and topics from survival analysis and Bayesian statistics. Working code and data for Python solutions for each test, together with easy-to-follow Python examples, can be reproduced by the reader and reinforce their immediate understanding of the topic. With recent advances in the Python ecosystem, Python has become a popular language for scientific computing, offering a powerful environment for statistical data analysis and an interesting alternative to R. The book is intended for master and PhD students, mainly from the life and medical sciences, with a basic knowledge of statistics. As it also provides some statistics background, the book can be used by anyone who wants to perform a statistical data analysis.
Call Number: Online access -- ebookISBN: 331955252XPublication Date: 2017
Logic of Statistical Inference by
One of Ian Hacking's earliest publications, this book showcases his early ideas on the central concepts and questions surrounding statistical reasoning. He explores the basic principles of statistical reasoning and tests them, both at a philosophical level and in terms of their practical consequences for statisticians. Presented in a fresh twenty-first-century series livery, and including a specially commissioned preface written by Jan-Willem Romeijn, illuminating its enduring importance and relevance to philosophical enquiry, Hacking's influential and original work has been revived for a new generation of readers.
Call Number: Science & Engineering Library, Book Stacks QA276 .H2 2016ISBN: 9781107144958Publication Date: 2016
Mathematical Statistics by
Volume I presents fundamental, classical statistical concepts at the doctorate level without using measure theory. It gives careful proofs of major results and explains how the theory sheds light on the properties of practical methods. Volume II covers a number of topics that are important in current measure theory and practice. It emphasizes nonparametric methods which can really only be implemented with modern computing power on large and complex data sets. In addition, the set includes a large number of problems with more difficult ones appearing with hints and partial solutions for the instructor.
Call Number: Science & Engineering Library. Book Stacks QA276 .B47 2015ISBN: 9781498740319Publication Date: 2015-2016
Statistics and Analysis of Scientific Data by
The revised second edition of this textbook provides the reader with a solid foundation in probability theory and statistics as applied to the physical sciences, engineering and related fields. It covers a broad range of numerical and analytical methods that are essential for the correct analysis of scientific data, including probability theory, distribution functions of statistics, fits to two-dimensional data and parameter estimation, Monte Carlo methods and Markov chains. Features new to this edition include: * a discussion of statistical techniques employed in business science, such as multiple regression analysis of multivariate datasets. * a new chapter on the various measures of the mean including logarithmic averages. * new chapters on systematic errors and intrinsic scatter, and on the fitting of data with bivariate errors. * a new case study and additional worked examples. * mathematical derivations and theoretical background material have been appropriately marked, to improve the readability of the text. * end-of-chapter summary boxes, for easy reference. As in the first edition, the main pedagogical method is a theory-then-application approach, where emphasis is placed first on a sound understanding of the underlying theory of a topic, which becomes the basis for an efficient and practical application of the material. The level is appropriate for undergraduates and beginning graduate students, and as a reference for the experienced researcher. Basic calculus is used in some of the derivations, and no previous background in probability and statistics is required. The book includes many numerical tables of data, as well as exercises and examples to aid the readers' understanding of the topic.
Call Number: Online access -- ebookISBN: 1493965727Publication Date: 2017
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