Statistical Concepts consists of the last 9 chapters of An Introduction to Statistical Concepts 3rd ed Designed for the second course in statistics it is one of the few texts that focuses just on intermediate statistics The book highlights how statistics work & what they mean to better prepare students to analyze their own data & interpret SPSS & research results As such it offers more coverage of non-parametric procedures used when standard assumptions are violated since these methods are more frequently encountered when working with real data Determining appropriate sample sizes is emphasized throughout Only crucial equations are included The new edition features New co-author Debbie L Hahs-Vaughn the 2007 recipient of the University of Central Florida's College of Education Excellence in Graduate Teaching Award A new chapter on logistic regression models for today's more complex methodologies Much more on computing confidence intervals & conducting power analyses using GPower All new SPSS version 19 screenshots to help navigate through the program & annotated output to assist in the interpretation of results Sections on how to write-up statistical results in APA format & new templates for writing research questions New learning tools including chapter-opening vignettes outlines a list of key concepts Stop & Think boxes & many more examples tables & figures More tables of assumptions & the effects of their violation including how to test them in SPSS 33 new conceptual computational & all new interpretative problems A website with Power Points answers to the even-numbered problems detailed solutions to the odd-numbered problems & test items for instructors & for students the chapter outlines key concepts & datasets Each chapter begins with an outline a list of key concepts & a research vignette related to the concepts Realistic examples from education & the behavioral sciences illustrate those concepts Each example examines the procedures & assumptions & provides tips for how to run SPSS & develop an APA style write-up Tables of assumptions & the effects of their violation are included along with how to test assumptions in SPSS Each chapter

Includes:: computational conceptual & interpretive problems Answers to the odd-numbered problems are provided The SPSS data sets that correspond to the book's examples & problems are available on the web The book covers basic & advanced analysis of variance models & topics not dealt with in other texts such as robust methods multiple comparison & non-parametric procedures & multiple & logistic regression models Intended for courses in intermediate statistics andor statistics II taught in education andor the behavioral sciences predominantly at the master's or doctoral level Knowledge of introductory statistics is assumed

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Discover New Methods for Dealing with High-Dimensional Data A sparse statistical model has only a small number of nonzero parameters or weights; therefore it is much easier to estimate & interpret than a dense model Statistical Learning with Sparsity The Lasso & Generalizations presents methods that exploit sparsity to help recover the underlying signal in a set of data Top experts in this rapidly evolving field the authors describe the lasso for linear regression & a simple coordinate descent algorithm for its computation They discuss the application of 1 penalties to generalized linear models & support vector machines cover generalized penalties such as the elastic net & group lasso & review numerical methods for optimization They also present statistical inference methods for fitted (lasso) models including the bootstrap Bayesian methods & recently developed approaches In addition the book examines matrix decomposition sparse multivariate analysis graphical models & compressed sensing It concludes with a survey of theoretical results for the lasso In this age of big data the number of features measured on a person or object can be large & might be larger than the number of observations This book shows how the sparsity assumption allows us to tackle these problems & extract useful & reproducible patterns from big datasets Data analysts computer scientists & theorists will appreciate this thorough & up-to-date treatment of sparse statistical modeling ...

In each generation scientists must redefine their fields abstracting simplifying & distilling the previous standard topics to make room for new advances & methods Sethna's book takes this step for statistical mechanics
- a field rooted in physics & chemistry whose ideas & methods are now central to information theory complexity & modern biology Aimed at advanced undergraduates & early graduate students in all of these fields Sethna limits his main presentation to the topics that future mathematicians & biologists as well as physicists & chemists will find fascinating & central to their work The amazing breadth of the field is reflected in the author's large supply of carefully crafted exercises each an introduction to a whole field of study everything from chaos through information theory to life at the end of the universe
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How do beginning students of statistics for geography learn to fully understand the key concepts & apply the principal techniques? This text now in its Fourth Edition provides exactly that resource Accessibly written & focussed on student learning it's a statistics 101 that

Includes:: definitions examples & exercise throughout Now fully integrated with online self-assessment exercises & video navigation it explains everything required to get full credits for any undergraduate statistics module Descriptive statistics probability inferential statistics hypothesis testing & sampling variance correlation regression analysis spatial patterns spatial data reduction using factor analysis & cluster analysis Exercises in the text are complemented with online exercise & prompts that test the understanding of concepts & techniques additional online exercises review understanding of the entire chapter relating concepts & techniques Completely revised & updated for accessibility including new material (on measures of distance statistical power sample size selection & basic probability) with related exercises & downloadable datasets It is the only text required for undergraduate modules in statistical analysis statistical methods & quantitative geography

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Designing algorithms to recommend items such as news articles & movies to users is a challenging task in numerous web applications The crux of the problem is to rank items based on users' responses to different items to optimize for multiple objectives Major technical challenges are high dimensional prediction with sparse data & constructing high dimensional sequential designs to collect data for user modeling & system design This comprehensive treatment of the statistical issues that arise in recommender systems

Includes:: detailed in-depth discussions of current state-of-the-art methods such as adaptive sequential designs (multi-armed bandit methods) bilinear random-effects models (matrix factorization) & scalable model fitting using modern computing paradigms like Map Reduce The authors draw upon their vast experience working with such large-scale systems at Yahoo! & Linked In & bridge the gap between theory & practice by illustrating complex concepts with examples from applications they are directly involved with

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This book provides a general introduction to the ideas & methods of statistical mechanics with the principal aim of meeting the needs of Master's students in chemical mechanical & materials science engineering Extensive introductory information is presented on many general physics topics in which students in engineering are inadequately trained ranging from the Hamiltonian formulation of classical mechanics to basic quantum mechanics electromagnetic fields in matter intermolecular forces & transport phenomena Since engineers should be able to apply physical concepts the book also focuses on the practical applications of statistical physics to material science & to cutting-edge technologies with brief but informative sections on for example interfacial properties disperse systems nucleation magnetic materials superfluidity & ultralow temperature technologies The book adopts a graded approach to learning the opening four basic-level chapters being followed by advanced "starred" sections in which special topics are discussed Its relatively informal style including the use of musical metaphors to guide the reader through the text will aid self-learning ...

Statistical Rethinking A Bayesian Course with Examples in R & Stan builds readers' knowledge of & confidence in statistical modeling Reflecting the need for even minor programming in today's model-based statistics the book pushes readers to perform step-by-step calculations that are usually automated This unique computational approach ensures that readers understand enough of the details to make reasonable choices & interpretations in their own modeling work The text presents generalized linear multilevel models from a Bayesian perspective relying on a simple logical interpretation of Bayesian probability & maximum entropy It covers from the basics of regression to multilevel models The author also discusses measurement error missing data & Gaussian process models for spatial & network autocorrelation By using complete R code examples throughout this book provides a practical foundation for performing statistical inference Designed for both Ph D students & seasoned professionals in the natural & social sciences it prepares them for more advanced or specialized statistical modeling Web Resource The book is accompanied by an R package (rethinking) that is available on the author's website & Git Hub The two core functions (map & map 2stan) of this package allow a variety of statistical models to be constructed from standard model formulas ...

Fully endorsed by OCR & revised to match the 2005

Specification this series has been carefully revised by experienced

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Fully endorsed by OCR & revised to match the 2005

Specification this series has been carefully revised by experienced

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The new edition of this bestseller continues to demystify the world of statistics for health care professionals Revised throughout the 11th Edition features new material in the areas of binary data to deal with relative risk absolute risk & the evidence-based criteria of numbers needed to treat 192 pages Campbell Michael J ...