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Analyzing athlete data throughout a competitive sports season requires a thorough understanding of research design combined with the appropriate statistical analysis. Interpretation of sports science data is equally important and the textbook, Data Analysis in Sports Science, clearly articulates the multifaceted nature of sports science, research design and statistical analysis. The textbook provides in-depth guides for running and interpreting descriptive statistics, correlations, t-tests, ANOVAs, and regressions in SPSS. The examples in the textbook were created from actual sports science data.

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This book provides readers with an understanding of the important considerations directly related to analyzing various forms of data - both qualitative and quantitative. While practice problems are included so that readers can self-test their knowledge of major concepts, the book does not drown its readers in page after page of problem sets and tedious calculations. The numerous practice examples of concepts, and the engaging, conversational writing style will be appealing and effective for many types of learners. For anyone with an interest in introductory statistics as it relates to social work.

Discover what you can do with R! Introducing the R system, covering standard regression methods, then tackling more advanced topics, this book guides users through the practical, powerful tools that the R system provides. The emphasis is on hands-on analysis, graphical display, and interpretation of data. The many worked examples, from real-world research, are accompanied by commentary on what is done and why. The companion website has code and datasets, allowing readers to reproduce all analyses, along with solutions to selected exercises and updates. Assuming basic statistical knowledge and some experience with data analysis (but not R), the book is ideal for research scientists, final-year undergraduate or graduate-level students of applied statistics, and practicing statisticians. It is both for learning and for reference. This third edition expands upon topics such as Bayesian inference for regression, errors in variables, generalized linear mixed models, and random forests.

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Good condition. This is the average used book, that has all pages or leaves present, but may include writing. Book may be ex-library with stamps and stickers. 0.79

With clear and concrete examples from elementary and high schools, this book helps you effectively gather, analyze, and use data to improve student learning. It demonstrates how to make better decisions, identify root causes of problems, and communicate and report results.

Data Analysis Methods in Physical Oceanography, Third Edition is a practical reference to established and modern data analysis techniques in earth and ocean sciences. Its five major sections address data acquisition and recording, data processing and presentation, statistical methods and error handling, analysis of spatial data fields, and time series analysis methods. The revised Third Edition updates the instrumentation used to collect and analyze physical oceanic data and adds new techniques including Kalman Filtering. Additionally, the sections covering spectral, wavelet, and harmonic analysis techniques are completely revised since these techniques have attracted significant attention over the past decade as more accurate and efficient data gathering and analysis methods.Completely updated and revised to reflect new filtering techniques and major updating of the instrumentation used to collect and analyze dataCo-authored by scientists from academe and industry, both of whom have more than 30 years of experience in oceanographic research and field workSignificant revision of sections covering spectral, wavelet, and harmonic analysis techniquesExamples address typical data analysis problems yet provide the reader with formulaic “recipes for working with their own dataSignificant expansion to 350 figures, illustrations, diagrams and photos

Introductory text covers the statistical methods and procedures needed to analyze research findings. Special focus is given to quantitative methods. Other features include management of data, how to "clean" data, and how to work around missing data. Graduate nursing students.

Paperback. An initial course in scientific data analysis and hypothesis testing designed for students in all science, technology, engineering, and mathematics disciplines Data Analysis for the Geosciences: Essentials of Uncertainty, Comparison, and Visualization is a textbook for upper-level undergraduate STEM students, designed to be their statistics course in a degree program. This volume provides a comprehensive introduction to data analysis, visualization, and data-model comparisons and metrics, within the framework of the uncertainty around the values. It offers a learning experience based on real data from the Earth, ocean, atmospheric, space, and planetary sciences. About this volume: Serves as an initial course in scientific data analysis and hypothesis testingFocuses on the methods of data processingIntroduces a wide range of analysis techniquesDescribes the many ways to compare data with modelsCenters on applications rather than derivationsExplains how to select appropriate statistics for meaningful decisionsExplores the importance of the concept of uncertaintyUses examples from real geoscience observationsHomework problems at the end of chapters The American Geophysical Union promotes discovery in Earth and space science for the benefit of humanity. Its publications disseminate scientific knowledge and provide resources for researchers, students, and professionals. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Designed especially for business and social science students who are familiar with the fundamentals of statistics, this text explores both the theory and practice of regression analysis. Describes the interaction between data analysis and regression models used to represent the data -- to help students learn how to analyze regression data, understand regression models, and how to specify an appropriate model to represent a data set. The main narrative in each chapter stresses application and interpretation of results in applied statistical methods from a user's point of view. Principles are introduced as needed.

Making sense of sports performance data can be a challenging task but is nevertheless an essential part of performance analysis investigations. Focusing on techniques used in the analysis of sport performance, this book introduces the fundamental principles of data analysis, explores the most important tools used in data analysis, and offers guidance on the presentation of results. The book covers key topics such as: The purpose of data analysis, from statistical analysis to algorithmic processing Commercial packages for performance and data analysis, including Focus, Sportscode, Dartfish, Prozone, Excel, SPSS and Matlab Effective use of statistical procedures in sport performance analysis Analysing data from manual notation systems, player tracking systems and computerized match analysis systems Creating visually appealing ‘dashboard’ interfaces for presenting data Assessing reliability. The book includes worked examples from real sport, offering clear guidance to the reader and bringing the subject to life. This book is invaluable reading for any student, researcher or analyst working in sport performance or undertaking a sport-related research project or methods course

Data Analysis Using Stata, Third Edition is a comprehensive introduction to both statistical methods and Stata. Beginners will learn the logic of data analysis and interpretation and easily become self-sufficient data analysts. Readers already familiar with Stata will find it an enjoyable resource for picking up new tips and tricks. The book is written as a self-study tutorial and organized around examples. It interactively introduces statistical techniques such as data exploration, description, and regression techniques for continuous and binary dependent variables. Step by step, readers move through the entire process of data analysis and in doing so learn the principles of Stata, data manipulation, graphical representation, and programs to automate repetitive tasks. This third edition includes advanced topics, such as factor-variables notation, average marginal effects, standard errors in complex survey, and multiple imputation in a way, that beginners of both data analysis and Stata can understand. Using data from a longitudinal study of private households, the authors provide examples from the social sciences that are relatable to researchers from all disciplines. The examples emphasize good statistical practice and reproducible research. Readers are encouraged to download the companion package of datasets to replicate the examples as they work through the book. Each chapter ends with exercises to consolidate acquired skills.

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Data analysis for database design is a subject of great practical value to systems analysts and designers. This classic text has been updated to include chapters on distributed database systems, query optimisation and object-orientation.The SQL content now includes features of SQL92 and SQL 99. With new databases coming online all the time and the general expansion of the information age, it is increasingly important to ensure that the analysis and model of a database design is accurate and robust. This is an ideal book for helping you to ensure that your database is well designed and therefore user friendly.Increased material on SQL including the latest developmentsPractical approach to explaining techniques and conceptsContains many questions and answer pointers

Accessible, engaging, and informative, this text will help any social science student approach statistics with confidence. With a well-paced and well-judged integrated approach rather than a simple linear trajectory, this book progresses at a realistic speed that matches the pace at which statistics novices actually learn. Packed with global, interdisciplinary examples that ground statistical theory and concepts in real-world situations, it shows readers not only how to apply newfound knowledge using IBM® SPSS® Statistics, but also why they would want to. Spanning statistics basics like variables, constants, and sampling to t-tests, multiple regression, and factor analysis, it builds statistical literacy while also covering key research principles like research questions, error types, and results reliability. Readers will learn how to: Describe data with graphs, tables, and numbers Calculate probability and value distributions Test a priori and post hoc hypotheses Conduct Chi-squared tests and observational studies Structure ANOVA, ANCOVA, and factorial designs Supported by extensive visuals and a companion website with interactive demonstrations, author video, and practice datasets, this book is the student-focused companion to support learners through their statistics journeys.

LIGHTNING FAST SHIPPING! A heavily used, but still working copy. Coffee stain and wrinkling to the edge of the pages when a tired undergrad fell asleep and knocked their cup over on their books. The binding and pages of the book have been reinforced with tape, has tape and stickers on the cover, as well as lots of notes (some of the answers in the learning activities may be filled in) on the pages. Definitely not pretty, but it's a working copy at a great price that ships fast. ~ Book does NOT contain an access code or CD/DVD.

Book is in Used-VeryGood condition. Pages and cover are clean and intact. Used items may not include supplementary materials such as CDs or access codes. May show signs of minor shelf wear and contain very limited notes and highlighting. 1.06

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Data analysis is of central importance in the education of scientists. This book offers a compact and readable introduction to techniques relevant to physical science students. The material is thoroughly integrated with the popular and powerful spreadsheet package Excel by Microsoft. Excel features of most relevance to the analysis of experimental data in the physical sciences are dealt with in some detail. Fully worked problems reinforce basic principles. Underlying assumptions and range of applicability of techniques are discussed, though detailed derivations of basic equations are mostly avoided or confined to the appendices.