Statistical Reasoning And Methods
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| Author | : Rudolf J. Freund |
| Publisher | : Elsevier |
| Total Pages | : 694 |
| Release | : 2003-01-07 |
| Genre | : Mathematics |
| ISBN | : 0080498221 |
This broad text provides a complete overview of most standard statistical methods, including multiple regression, analysis of variance, experimental design, and sampling techniques. Assuming a background of only two years of high school algebra, this book teaches intelligent data analysis and covers the principles of good data collection. * Provides a complete discussion of analysis of data including estimation, diagnostics, and remedial actions * Examples contain graphical illustration for ease of interpretation * Intended for use with almost any statistical software * Examples are worked to a logical conclusion, including interpretation of results * A complete Instructor's Manual is available to adopters
| Author | : Jeff Bennett |
| Publisher | : Pearson |
| Total Pages | : 449 |
| Release | : 2017-01-09 |
| Genre | : Mathematics |
| ISBN | : 0134509889 |
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
| Author | : Josh Tabor |
| Publisher | : Macmillan |
| Total Pages | : 709 |
| Release | : 2011-12-23 |
| Genre | : Juvenile Nonfiction |
| ISBN | : 1429274379 |
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!
| Author | : Thomas Dietz |
| Publisher | : John Wiley & Sons |
| Total Pages | : 613 |
| Release | : 2009-03-02 |
| Genre | : Mathematics |
| ISBN | : 1405169028 |
Introduction to Social Statistics is a basic statistics text with a focus on the use of models for thinking through statistical problems, an accessible and consistent structure with ongoing examples across chapters, and an emphasis on the tools most commonly used in contemporary research. Lively introductory textbook that uses three strategies to help students master statistics: use of models throughout; repetition with variation to underpin pedagogy; and emphasis on the tools most commonly used in contemporary research Demonstrates how more than one statistical method can be used to approach a research question Enhanced learning features include a ‘walk-through’ of statistical concepts, applications, features, advanced topics boxes, and a ‘What Have We Learned’ section at the end of each chapter Supported by a website containing instructor materials including chapter-by-chapter PowerPoint slides, answers to exercises, and an instructor guide Visit www.wiley.com/go/dietz for additional student and instructor resources.
| Author | : Peter Walley |
| Publisher | : Chapman and Hall/CRC |
| Total Pages | : 728 |
| Release | : 1991 |
| Genre | : Mathematics |
| ISBN | : |
An examination of topics involved in statistical reasoning with imprecise probabilities. The book discusses assessment and elicitation, extensions, envelopes and decisions, the importance of imprecision, conditional previsions and coherent statistical models.
| Author | : Göran Kauermann |
| Publisher | : Springer Nature |
| Total Pages | : 361 |
| Release | : 2021-09-30 |
| Genre | : Mathematics |
| ISBN | : 3030698270 |
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.
| Author | : JOHN H. MUBLLER, KARL F. SCHUESSLER |
| Publisher | : |
| Total Pages | : 460 |
| Release | : 1961 |
| Genre | : |
| ISBN | : |
| Author | : Bruce M. King |
| Publisher | : John Wiley & Sons |
| Total Pages | : 976 |
| Release | : 2018-04-24 |
| Genre | : Psychology |
| ISBN | : 1119379733 |
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.
| Author | : Jeffrey M. Stanton |
| Publisher | : Guilford Publications |
| Total Pages | : 336 |
| Release | : 2017-05-22 |
| Genre | : Social Science |
| ISBN | : 1462530265 |
Engaging and accessible, this book teaches readers how to use inferential statistical thinking to check their assumptions, assess evidence about their beliefs, and avoid overinterpreting results that may look more promising than they really are. It provides step-by-step guidance for using both classical (frequentist) and Bayesian approaches to inference. Statistical techniques covered side by side from both frequentist and Bayesian approaches include hypothesis testing, replication, analysis of variance, calculation of effect sizes, regression, time series analysis, and more. Students also get a complete introduction to the open-source R programming language and its key packages. Throughout the text, simple commands in R demonstrate essential data analysis skills using real-data examples. The companion website provides annotated R code for the book's examples, in-class exercises, supplemental reading lists, and links to online videos, interactive materials, and other resources. ÿ Pedagogical Features *Playful, conversational style and gradual approach; suitable for students without strong math backgrounds. *End-of-chapter exercises based on real data supplied in the free R package. *Technical explanation and equation/output boxes. *Appendices on how to install R and work with the sample datasets.ÿ
| Author | : Gilbert Harman |
| Publisher | : MIT Press |
| Total Pages | : 119 |
| Release | : 2012-01-13 |
| Genre | : Psychology |
| ISBN | : 0262263157 |
The implications for philosophy and cognitive science of developments in statistical learning theory. In Reliable Reasoning, Gilbert Harman and Sanjeev Kulkarni—a philosopher and an engineer—argue that philosophy and cognitive science can benefit from statistical learning theory (SLT), the theory that lies behind recent advances in machine learning. The philosophical problem of induction, for example, is in part about the reliability of inductive reasoning, where the reliability of a method is measured by its statistically expected percentage of errors—a central topic in SLT. After discussing philosophical attempts to evade the problem of induction, Harman and Kulkarni provide an admirably clear account of the basic framework of SLT and its implications for inductive reasoning. They explain the Vapnik-Chervonenkis (VC) dimension of a set of hypotheses and distinguish two kinds of inductive reasoning. The authors discuss various topics in machine learning, including nearest-neighbor methods, neural networks, and support vector machines. Finally, they describe transductive reasoning and suggest possible new models of human reasoning suggested by developments in SLT.
