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Statistics Learning From Data

Author : Roxy Peck
ISBN : 9781337672153
Genre : Mathematics
File Size : 90.87 MB
Format : PDF, ePub, Mobi
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STATISTICS: LEARNING FROM DATA, Second Edition, addresses common problems faced by learners of elementary statistics with an innovative approach. The authors have paid particular attention to areas learners often struggle with -- probability, hypothesis testing, and selecting an appropriate method of analysis. Probability coverage is based on current research on how students best learn the subject. A unique chapter that provides an informal introduction to the ideas of statistical inference helps students to develop a framework for choosing an appropriate method. Supported by learning objectives, real-data examples and exercises, and technology notes, this book helps learners to develop conceptual understanding, mechanical proficiency, and the ability to put knowledge into practice. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.
Category: Mathematics

Learning From Data

Author : Doug Fisher
ISBN : 9781461224044
Genre : Mathematics
File Size : 63.12 MB
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Ten years ago Bill Gale of AT&T Bell Laboratories was primary organizer of the first Workshop on Artificial Intelligence and Statistics. In the early days of the Workshop series it seemed clear that researchers in AI and statistics had common interests, though with different emphases, goals, and vocabularies. In learning and model selection, for example, a historical goal of AI to build autonomous agents probably contributed to a focus on parameter-free learning systems, which relied little on an external analyst's assumptions about the data. This seemed at odds with statistical strategy, which stemmed from a view that model selection methods were tools to augment, not replace, the abilities of a human analyst. Thus, statisticians have traditionally spent considerably more time exploiting prior information of the environment to model data and exploratory data analysis methods tailored to their assumptions. In statistics, special emphasis is placed on model checking, making extensive use of residual analysis, because all models are 'wrong', but some are better than others. It is increasingly recognized that AI researchers and/or AI programs can exploit the same kind of statistical strategies to good effect. Often AI researchers and statisticians emphasized different aspects of what in retrospect we might now regard as the same overriding tasks.
Category: Mathematics


Author : Olsen Peck
ISBN : 1285085248
Genre : Mathematical statistics
File Size : 85.86 MB
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Category: Mathematical statistics


Author : Maria Ripol
ISBN : 0136037356
Genre : Mathematics
File Size : 85.27 MB
Format : PDF, Kindle
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Written as a study tool, the Lab Workbook is keyed directly to the text to provide section by section review and practice for the first ten chapters of Agresti/Franklin 2/e. Print outs of the activities found on the Student CD are included in the Lab Workbook.
Category: Mathematics

Learning From Data

Author : Vladimir Cherkassky
ISBN : 0470140518
Genre : Computers
File Size : 29.86 MB
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An interdisciplinary framework for learning methodologies—covering statistics, neural networks, and fuzzy logic, this book provides a unified treatment of the principles and methods for learning dependencies from data. It establishes a general conceptual framework in which various learning methods from statistics, neural networks, and fuzzy logic can be applied—showing that a few fundamental principles underlie most new methods being proposed today in statistics, engineering, and computer science. Complete with over one hundred illustrations, case studies, and examples making this an invaluable text.
Category: Computers

Learning From Data

Author : Arthur Glenberg
ISBN : 9781136676628
Genre : Education
File Size : 74.16 MB
Format : PDF, Docs
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Learning from Data focuses on how to interpret psychological data and statistical results. The authors review the basics of statistical reasoning to helpstudents better understand relevant data that affecttheir everyday lives. Numerous examples based on current research and events are featured throughout.To facilitate learning, authors Glenberg and Andrzejewski: Devote extra attention to explaining the more difficult concepts and the logic behind them Use repetition to enhance students’ memories with multiple examples, reintroductions of the major concepts, and a focus on these concepts in the problems Employ a six-step procedure for describing all statistical tests from the simplest to the most complex Provide end-of-chapter tables to summarize the hypothesis testing procedures introduced Emphasizes how to choose the best procedure in the examples, problems and endpapers Focus on power with a separate chapter and power analyses procedures in each chapter Provide detailed explanations of factorial designs, interactions, and ANOVA to help students understand the statistics used in professional journal articles. The third edition has a user-friendly approach: Designed to be used seamlessly with Excel, all of the in-text analyses are conducted in Excel, while the book’s CD contains files for conducting analyses in Excel, as well as text files that can be analyzed in SPSS, SAS, and Systat Two large, real data sets integrated throughout illustrate important concepts Many new end-of-chapter problems (definitions, computational, and reasoning) and many more on the companion CD Online Instructor’s Resources includes answers to all the exercises in the book and multiple-choice test questions with answers Boxed media reports illustrate key concepts and their relevance to realworld issues The inclusion of effect size in all discussions of power accurately reflects the contemporary issues of power, effect size, and significance. Learning From Data, Third Edition is intended as a text for undergraduate or beginning graduate statistics courses in psychology, education, and other applied social and health sciences.
Category: Education

Scientific Inference

Author : Simon Vaughan
ISBN : 9781107024823
Genre : Mathematics
File Size : 23.15 MB
Format : PDF
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Providing the knowledge and practical experience to begin analysing scientific data, this book is ideal for physical sciences students wishing to improve their data handling skills. The book focuses on explaining and developing the practice and understanding of basic statistical analysis, concentrating on a few core ideas, such as the visual display of information, modelling using the likelihood function, and simulating random data. Key concepts are developed through a combination of graphical explanations, worked examples, example computer code and case studies using real data. Students will develop an understanding of the ideas behind statistical methods and gain experience in applying them in practice.
Category: Mathematics

Learning From Data

Author : Yaser S. Abu-Mostafa
ISBN : 1600490069
Genre : Machine learning
File Size : 43.49 MB
Format : PDF, Docs
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Category: Machine learning

Student Laboratory Workbook For Statistics

Author : Megan Mocko
ISBN : 0133860892
Genre : Mathematics
File Size : 80.35 MB
Format : PDF
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This workbook is a study tool for the first 10 chapters of the text. This workbook provides section-by-section review and practice, and additional activities that cover fundamental statistical topics.
Category: Mathematics

Student Solutions Manual For Peck Short S Statistics Learning From Data 2nd

Author : Roxy Peck
ISBN : 1337558389
Genre : Mathematics
File Size : 84.85 MB
Format : PDF, Mobi
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Go beyond the answers -- see what it takes to get there and improve your grade! This manual provides worked-out, step-by-step solutions to selected problems in the text, giving you the information you need to truly understand how these problems are solved.
Category: Mathematics