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Elements Of Statistical Reasoning
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Book Synopsis Elements of Statistical Reasoning by : Edward W. Minium
Download or read book Elements of Statistical Reasoning written by Edward W. Minium and published by Wiley. This book was released on 1998-11-19 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text stresses conceptual development and the logic of statistics for use in the Behavioral Sciences. It is designed for those who need to evaluate statistical findings. Coverage includes estimation procedures, independent samples t-test, Steven's scales of measurements, Pearson correlation coefficient. Updated to include Turkey's HSD test on factorial analysis of variance and Spearman's rho on assumption-free tests and all chapter have been edited to enhance clarity and flow, to simplify numbers in the worked problems, and to align all examples with the behavioral sciences.
Book Synopsis All of Statistics by : Larry Wasserman
Download or read book All of Statistics written by Larry Wasserman and published by Springer Science & Business Media. This book was released on 2013-12-11 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.
Book Synopsis Statistical Reasoning in Sports by : Josh Tabor
Download or read book Statistical Reasoning in Sports written by Josh Tabor and published by Macmillan. This book was released on 2011-12-23 with total page 709 pages. Available in PDF, EPUB and Kindle. Book excerpt: Offering a unique and powerful way to introduce the principles of statistical reasoning, Statistical Reasoning in Sports features engaging examples and a student-friendly approach. Starting from the very first chapter, students are able to ask questions, collect and analyze data, and draw conclusions using randomization tests. Is it harder to shoot free throws with distractions? We explore this question by designing an experiment, collecting the data, and using a hands-on simulation to analyze results. Completely covering the Common Core Standards for Probability and Statistics, Statistical Reasoning in Sports is an accessible and fun way to learn about statistics!
Book Synopsis Elements of Statistical Reasoning by : E M Minium
Download or read book Elements of Statistical Reasoning written by E M Minium and published by John Wiley & Sons. This book was released on 1994-09-01 with total page 496 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis The Challenge of Developing Statistical Literacy, Reasoning and Thinking by : Dani Ben-Zvi
Download or read book The Challenge of Developing Statistical Literacy, Reasoning and Thinking written by Dani Ben-Zvi and published by Springer Science & Business Media. This book was released on 2006-02-23 with total page 423 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unique in that it collects, presents, and synthesizes cutting edge research on different aspects of statistical reasoning and applies this research to the teaching of statistics to students at all educational levels, this volume will prove of great value to mathematics and statistics education researchers, statistics educators, statisticians, cognitive psychologists, mathematics teachers, mathematics and statistics curriculum developers, and quantitative literacy experts in education and government.
Book Synopsis Statistical Reasoning in the Behavioral Sciences by : Bruce M. King
Download or read book Statistical Reasoning in the Behavioral Sciences written by Bruce M. King and published by John Wiley & Sons. This book was released on 2018-04-24 with total page 976 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cited by more than 300 scholars, Statistical Reasoning in the Behavioral Sciences continues to provide streamlined resources and easy-to-understand information on statistics in the behavioral sciences and related fields, including psychology, education, human resources management, and sociology. Students and professionals in the behavioral sciences will develop an understanding of statistical logic and procedures, the properties of statistical devices, and the importance of the assumptions underlying statistical tools. This revised and updated edition continues to follow the recommendations of the APA Task Force on Statistical Inference and greatly expands the information on testing hypotheses about single means. The Seventh Edition moves from a focus on the use of computers in statistics to a more precise look at statistical software. The “Point of Controversy” feature embedded throughout the text provides current discussions of exciting and hotly debated topics in the field. Readers will appreciate how the comprehensive graphs, tables, cartoons and photographs lend vibrancy to all of the material covered in the text.
Book Synopsis Statistical Reasoning for Everyday Life by : Jeff Bennett
Download or read book Statistical Reasoning for Everyday Life written by Jeff Bennett and published by Pearson. This book was released on 2017-01-09 with total page 449 pages. Available in PDF, EPUB and Kindle. Book excerpt: For courses in Statistical Literacy A qualitative approach teaches students how to reason using statistics Understanding the core ideas behind statistics is crucial to everyday success in the modern world. Statistical Reasoning for Everyday Life is designed to teach these core ideas through real-life examples so that students are able to understand the statistics needed in their college courses, reason with statistical information in their careers, and to evaluate and make everyday decisions using statistics. The authors approach each concept qualitatively, using computation techniques only to enhance understanding and build on ideas step-by-step, working up to real examples and complex case studies. The Fifth Edition has been revised to update many exercises, examples, and case studies to engage today’s students with the latest data and relevant topics. Also available with MyLab Statistics MyLab™ Statistics is an online homework, tutorial, and assessment program designed to work with this text to engage students and improve results. Within its structured environment, students practice what they learn, test their understanding, and pursue a personalized study plan that helps them absorb course material and understand difficult concepts. NOTE: You are purchasing a standalone product; MyLab Statistics does not come packaged with this content. If you would like to purchase both the physical text and MyLab Statistics, search for: 0134701364 / 9780134701363 Statistical Reasoning for Everyday Life Plus NEW MyLab Statistics with Pearson eText -- Access Card Package, 5/e Package consists of: 0134494040 / 9780134494043 Statistical Reasoning for Everyday Life 0134678524 / 9780134678528 MyLab Statistics with Pearson eText -- Standalone Access Card -- for Statistical Reasoning for Everyday Life 0134678559 / 9780134678559 MyLab Statistics-- Royalty Bearing Content -- for Statistical Reasoning for Everyday Life
Book Synopsis Bayesian Reasoning and Machine Learning by : David Barber
Download or read book Bayesian Reasoning and Machine Learning written by David Barber and published by Cambridge University Press. This book was released on 2012-02-02 with total page 739 pages. Available in PDF, EPUB and Kindle. Book excerpt: A practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus.
Book Synopsis Fundamentals of Statistical Reasoning in Education by : Theodore Coladarci
Download or read book Fundamentals of Statistical Reasoning in Education written by Theodore Coladarci and published by John Wiley & Sons. This book was released on 2013-12-31 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fundamentals of Statistical Reasoning in Education 4th Edition, like the first three editions, is written largely with students of education in mind. Accordingly, Theodore Coladarci and Casey D. Cobb have drawn primarily on examples and issues found in school settings, such as those having to do with instruction, learning, motivation, and assessment. The emphasis on educational applications notwithstanding, the authors are confident that readers will find Fundamentals of Statistical Reasoning in Education 4th Edition of general relevance to other disciplines in the behavioral sciences as well. The 4th Edition of Fundamentals is still designed as a “one semester” book. The authors intentionally sidestep topics that few introductory courses cover (e.g., factorial analysis of variance, repeated measures analysis of variance, multiple regression). At the same time, effect size and confidence intervals are incorporated throughout, which today are regarded as essential to good statistical practice.
Book Synopsis Statistical Foundations, Reasoning and Inference by : Göran Kauermann
Download or read book Statistical Foundations, Reasoning and Inference written by Göran Kauermann and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook provides a comprehensive introduction to statistical principles, concepts and methods that are essential in modern statistics and data science. The topics covered include likelihood-based inference, Bayesian statistics, regression, statistical tests and the quantification of uncertainty. Moreover, the book addresses statistical ideas that are useful in modern data analytics, including bootstrapping, modeling of multivariate distributions, missing data analysis, causality as well as principles of experimental design. The textbook includes sufficient material for a two-semester course and is intended for master's students in data science, statistics and computer science with a rudimentary grasp of probability theory. It will also be useful for data science practitioners who want to strengthen their statistics skills.
Book Synopsis Essential Statistics, Regression, and Econometrics by : Gary Smith
Download or read book Essential Statistics, Regression, and Econometrics written by Gary Smith and published by Academic Press. This book was released on 2015-06-08 with total page 397 pages. Available in PDF, EPUB and Kindle. Book excerpt: Essential Statistics, Regression, and Econometrics, Second Edition, is innovative in its focus on preparing students for regression/econometrics, and in its extended emphasis on statistical reasoning, real data, pitfalls in data analysis, and modeling issues. This book is uncommonly approachable and easy to use, with extensive word problems that emphasize intuition and understanding. Too many students mistakenly believe that statistics courses are too abstract, mathematical, and tedious to be useful or interesting. To demonstrate the power, elegance, and even beauty of statistical reasoning, this book provides hundreds of new and updated interesting and relevant examples, and discusses not only the uses but also the abuses of statistics. The examples are drawn from many areas to show that statistical reasoning is not an irrelevant abstraction, but an important part of everyday life. - Includes hundreds of updated and new, real-world examples to engage students in the meaning and impact of statistics - Focuses on essential information to enable students to develop their own statistical reasoning - Ideal for one-quarter or one-semester courses taught in economics, business, finance, politics, sociology, and psychology departments, as well as in law and medical schools - Accompanied by an ancillary website with an instructors solutions manual, student solutions manual and supplementing chapters
Book Synopsis The Nature of Statistical Learning Theory by : Vladimir Vapnik
Download or read book The Nature of Statistical Learning Theory written by Vladimir Vapnik and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists.
Book Synopsis Developing Students’ Statistical Reasoning by : Joan Garfield
Download or read book Developing Students’ Statistical Reasoning written by Joan Garfield and published by Springer Science & Business Media. This book was released on 2008-09-08 with total page 407 pages. Available in PDF, EPUB and Kindle. Book excerpt: Increased attention is being paid to the need for statistically educated citizens: statistics is now included in the K-12 mathematics curriculum, increasing numbers of students are taking courses in high school, and introductory statistics courses are required in college. However, increasing the amount of instruction is not sufficient to prepare statistically literate citizens. A major change is needed in how statistics is taught. To bring about this change, three dimensions of teacher knowledge need to be addressed: their knowledge of statistical content, their pedagogical knowledge, and their statistical-pedagogical knowledge, i.e., their specific knowledge about how to teach statistics. This book is written for mathematics and statistics educators and researchers. It summarizes the research and highlights the important concepts for teachers to emphasize, and shows the interrelationships among concepts. It makes specific suggestions regarding how to build classroom activities, integrate technological tools, and assess students’ learning. This is a unique book. While providing a wealth of examples through lessons and data sets, it is also the best attempt by members of our profession to integrate suggestions from research findings with statistics concepts and pedagogy. The book’s message about the importance of listening to research is loud and clear, as is its message about alternative ways of teaching statistics. This book will impact instructors, giving them pause to consider: "Is what I’m doing now really the best thing for my students? What could I do better?" J. Michael Shaughnessy, Professor, Dept of Mathematical Sciences, Portland State University, USA This is a much-needed text for linking research and practice in teaching statistics. The authors have provided a comprehensive overview of the current state-of-the-art in statistics education research. The insights they have gleaned from the literature should be tremendously helpful for those involved in teaching and researching introductory courses. Randall E. Groth, Assistant Professor of Mathematics Education, Salisbury University, USA
Book Synopsis Learning Statistics with R by : Daniel Navarro
Download or read book Learning Statistics with R written by Daniel Navarro and published by Lulu.com. This book was released on 2013-01-13 with total page 617 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Learning Statistics with R" covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software and adopting a light, conversational style throughout. The book discusses how to get started in R, and gives an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book. For more information (and the opportunity to check the book out before you buy!) visit http://ua.edu.au/ccs/teaching/lsr or http://learningstatisticswithr.com
Book Synopsis Machine Learning in Action by : Peter Harrington
Download or read book Machine Learning in Action written by Peter Harrington and published by Simon and Schuster. This book was released on 2012-04-03 with total page 558 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification. About the Book A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interestingor useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many. Machine Learning in Action is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you'll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You'll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification. Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book. What's Inside A no-nonsense introduction Examples showing common ML tasks Everyday data analysis Implementing classic algorithms like Apriori and Adaboos Table of Contents PART 1 CLASSIFICATION Machine learning basics Classifying with k-Nearest Neighbors Splitting datasets one feature at a time: decision trees Classifying with probability theory: naïve Bayes Logistic regression Support vector machines Improving classification with the AdaBoost meta algorithm PART 2 FORECASTING NUMERIC VALUES WITH REGRESSION Predicting numeric values: regression Tree-based regression PART 3 UNSUPERVISED LEARNING Grouping unlabeled items using k-means clustering Association analysis with the Apriori algorithm Efficiently finding frequent itemsets with FP-growth PART 4 ADDITIONAL TOOLS Using principal component analysis to simplify data Simplifying data with the singular value decomposition Big data and MapReduce
Book Synopsis An Elementary Introduction to Statistical Learning Theory by : Sanjeev Kulkarni
Download or read book An Elementary Introduction to Statistical Learning Theory written by Sanjeev Kulkarni and published by John Wiley & Sons. This book was released on 2011-06-09 with total page 267 pages. Available in PDF, EPUB and Kindle. Book excerpt: A thought-provoking look at statistical learning theory and its role in understanding human learning and inductive reasoning A joint endeavor from leading researchers in the fields of philosophy and electrical engineering, An Elementary Introduction to Statistical Learning Theory is a comprehensive and accessible primer on the rapidly evolving fields of statistical pattern recognition and statistical learning theory. Explaining these areas at a level and in a way that is not often found in other books on the topic, the authors present the basic theory behind contemporary machine learning and uniquely utilize its foundations as a framework for philosophical thinking about inductive inference. Promoting the fundamental goal of statistical learning, knowing what is achievable and what is not, this book demonstrates the value of a systematic methodology when used along with the needed techniques for evaluating the performance of a learning system. First, an introduction to machine learning is presented that includes brief discussions of applications such as image recognition, speech recognition, medical diagnostics, and statistical arbitrage. To enhance accessibility, two chapters on relevant aspects of probability theory are provided. Subsequent chapters feature coverage of topics such as the pattern recognition problem, optimal Bayes decision rule, the nearest neighbor rule, kernel rules, neural networks, support vector machines, and boosting. Appendices throughout the book explore the relationship between the discussed material and related topics from mathematics, philosophy, psychology, and statistics, drawing insightful connections between problems in these areas and statistical learning theory. All chapters conclude with a summary section, a set of practice questions, and a reference sections that supplies historical notes and additional resources for further study. An Elementary Introduction to Statistical Learning Theory is an excellent book for courses on statistical learning theory, pattern recognition, and machine learning at the upper-undergraduate and graduate levels. It also serves as an introductory reference for researchers and practitioners in the fields of engineering, computer science, philosophy, and cognitive science that would like to further their knowledge of the topic.
Book Synopsis Statistical Inference as Severe Testing by : Deborah G. Mayo
Download or read book Statistical Inference as Severe Testing written by Deborah G. Mayo and published by Cambridge University Press. This book was released on 2018-09-20 with total page 503 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mounting failures of replication in social and biological sciences give a new urgency to critically appraising proposed reforms. This book pulls back the cover on disagreements between experts charged with restoring integrity to science. It denies two pervasive views of the role of probability in inference: to assign degrees of belief, and to control error rates in a long run. If statistical consumers are unaware of assumptions behind rival evidence reforms, they can't scrutinize the consequences that affect them (in personalized medicine, psychology, etc.). The book sets sail with a simple tool: if little has been done to rule out flaws in inferring a claim, then it has not passed a severe test. Many methods advocated by data experts do not stand up to severe scrutiny and are in tension with successful strategies for blocking or accounting for cherry picking and selective reporting. Through a series of excursions and exhibits, the philosophy and history of inductive inference come alive. Philosophical tools are put to work to solve problems about science and pseudoscience, induction and falsification.
