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Statistics for the Behavioral Sciences (Fourth Edition)
by Susan A. Nolan Thomas E. HeinzenNolan and Heinzen offer an introduction to the basics of statistics that is uniquely suited for behavioral science students, with coverage anchor to real-world stories, a highly visual approach, helpful mathematical support, and step-by-step examples. The new edition focuses on emerging trends that are redefining contemporary behavioral statistics.
Statistics for the Behavioural Sciences: An Introduction
by Riccardo RussoDo you find statistics overwhelming and confusing? Have you ever wished for someone to explain the basics in a clear and easy-to-follow style? This accessible textbook gives a step-by-step introduction to all the topics covered in introductory statistics courses for the behavioural sciences, with plenty of examples discussed in depth, based on real psychology experiments utilising the statistical techniques described. Advanced sections are also provided, for those who want to learn a particular topic in more depth.Statistics for the Behavioural Sciences: An Introduction begins with an introduction to the basic concepts, before providing a detailed explanation of basic statistical tests and concepts such as descriptive statistics, probability, the binomial distribution, continuous random variables, the normal distribution, the Chi-Square distribution, the analysis of categorical data, t-tests, correlation and regression. This timely and highly readable text will be invaluable to undergraduate students of psychology, and students of research methods courses in related disciplines, as well as anyone with an interest in the basic concepts and tests associated with statistics in the behavioural sciences.
Statistics for the Behavioural Sciences: An Introduction to Frequentist and Bayesian Approaches
by Riccardo RussoThis accessible textbook is for those without a mathematical background (just some notions of basic algebra are sufficient) and provides a comprehensive introduction to all topics covered in introductory behavioural science statistics courses. It includes plenty of real examples to demonstrate approaches in depth based on real psychology experiments utilizing the statistical techniques described. New content in this thoroughly updated second edition includes an introduction to Bayesian statistics which complements the coverage of Classical/Frequentist statistics present in the first edition. It also offers practical details on how to perform analyses using JASP – a globally employed, freely downloadable statistical package. The updated eResources also feature a range of new material including additional exercises so readers can test themselves on what they have learned in the book. This timely and highly readable text will be invaluable to undergraduate students of psychology and research methods courses in related disciplines, as well as anyone with an interest in understanding and applying the basic concepts and inferential techniques associated with statistics in the behavioural sciences.
Statistics for the Health Sciences: A Non-Mathematical Introduction
by Christine Dancey Richard Rowe John ReidyStatistics for the Health Sciences is a highly readable and accessible textbook on understanding statistics for the health sciences, both conceptually and via the SPSS programme. The authors give clear explanations of the concepts underlying statistical analyses and descriptions of how these analyses are applied in health science research without complex maths formulae. The textbook takes students from the basics of research design, hypothesis testing and descriptive statistical techniques through to more advanced inferential statistical tests that health science students are likely to encounter. The strengths and weaknesses of different techniques are critically appraised throughout, and the authors emphasise how they may be used both in research and to inform best practice care in health settings. Exercises and tips throughout the book allow students to practice using SPSS. The companion website provides further practical experience of conducting statistical analyses. Features include: * multiple choice questions for both student and lecturer use * full Powerpoint slides for lecturers * practical exercises using SPSS * additional practical exercises using SAS and R This is an essential textbook for students studying beginner and intermediate level statistics across the health sciences.
Statistics for the Social Sciences
by Russell T. WarneWritten by a quantitative psychologist, this textbook explains complex statistics in accessible language to undergraduates in all branches of the social sciences. Built around the central framework of the General Linear Model (GLM), Statistics for the Social Sciences teaches students how different statistical methods are interrelated to one another. With the GLM as a basis, students with varying levels of background are better equipped to interpret statistics and learn more advanced methods in their later courses. Russell Warne makes statistics relevant to students' varying majors by using fascinating real-life examples from the social sciences. Students who use this book will benefit from clear explanations, warnings against common erroneous beliefs about statistics, and the latest developments in the philosophy, reporting and practice of statistics in the social sciences. The textbook is packed with helpful pedagogical features including learning goals, guided practice and reflection questions.
Statistics for the Terrified
by John H. KranzlerStatistics for the Terrified, 3/E, is a user-friendly introduction to elementary statistics, intended primarily for the reluctant, math anxious/avoidant person. Written in a personal and informal style, with healthy doses of humor and encouragement, the aim of the book is to help readers make the leap from apprehension to comprehension of elementary statistics. The book presents state-of-the-art, empirically supported self-help strategies (based on the cognitive behavioral techniques of rational emotive therapy) that help readers manage their math anxiety so they can relax and build confidence while still learning statistics. For those who need to learn statistics to further their career.
Statistics for Toxicologists
by David S. SalsburgThis book explains how the computer programs work and why and when they can be applied to problems in toxicology. It discusses the statistical models used and their applications in a general fashion. The book overviews the problems that can arise from the blind use of the statistical models.
Statistics (Fourth Edition)
by David Friedman Robert Pisani Roger PurvesWe are going to tell you about some interesting problems which have been studied with the help of statistical methods, and show you how to use these methods yourself. We will try to explain why the methods work, and what to watch out for when others use them. Mathematical notation only seems to confuse things for many people, so this book relies on words, charts, and tables; there are hardly any x's or y's. As a matter of fact, even when professional mathematicians read technical books, their eyes tend to skip over the equations. What they really want is a sympathetic friend who will explain the ideas and draw the pictures behind the equations. We will try to be that friend, for those who read our book.
Statistics from A to Z: Confusing Concepts Clarified
by Andrew A. JawlikStatistics is confusing, even for smart, technically competent people. And many students and professionals find that existing books and web resources don't give them an intuitive understanding of confusing statistical concepts. That is why this book is needed. Some of the unique qualities of this book are: * Easy to Understand: Uses unique "graphics that teach" such as concept flow diagrams, compare-and-contrast tables, and even cartoons to enhance "rememberability." * Easy to Use: Alphabetically arranged, like a mini-encyclopedia, for easy lookup on the job, while studying, or during an open-book exam. * Wider Scope: Covers Statistics I and Statistics II and Six Sigma Black Belt, adding such topics as control charts and statistical process control, process capability analysis, and design of experiments. As a result, this book will be useful for business professionals and industrial engineers in addition to students and professionals in the social and physical sciences. In addition, each of the 60+ concepts is covered in one or more articles. The 75 articles in the book are usually 5-7 pages long, ensuring that things are presented in "bite-sized chunks." The first page of each article typically lists five "Keys to Understanding" which tell the reader everything they need to know on one page. This book also contains an article on "Which Statistical Tool to Use to Solve Some Common Problems", additional "Which to Use When" articles on Control Charts, Distributions, and Charts/Graphs/Plots, as well as articles explaining how different concepts work together (e.g., how Alpha, p, Critical Value, and Test Statistic interrelate). ANDREW A. JAWLIK received his B.S. in Mathematics and his M.S. in Mathematics and Computer Science from the University of Michigan. He held jobs with IBM in marketing, sales, finance, and information technology, as well as a position as Process Executive. In these jobs, he learned how to communicate difficult technical concepts in easy - to - understand terms. He completed Lean Six Sigma Black Belt coursework at the IASSC - accredited Pyzdek Institute. In order to understand the confusing statistics involved, he wrote explanations in his own words and graphics. Using this material, he passed the certification exam with a perfect score. Those statistical explanations then became the starting point for this book.
Statistics Hacks
by Bruce FreyWant to calculate the probability that an event will happen? Be able to spot fake data? Prove beyond doubt whether one thing causes another? Or learn to be a better gambler? You can do that and much more with 75 practical and fun hacks packed into Statistics Hacks. These cool tips, tricks, and mind-boggling solutions from the world of statistics, measurement, and research methods will not only amaze and entertain you, but will give you an advantage in several real-world situations-including business. This book is ideal for anyone who likes puzzles, brainteasers, games, gambling, magic tricks, and those who want to apply math and science to everyday circumstances. Several hacks in the first chapter alone-such as the "central limit theorem,", which allows you to know everything by knowing just a little-serve as sound approaches for marketing and other business objectives. Using the tools of inferential statistics, you can understand the way probability works, discover relationships, predict events with uncanny accuracy, and even make a little money with a well-placed wager here and there. Statistics Hacks presents useful techniques from statistics, educational and psychological measurement, and experimental research to help you solve a variety of problems in business, games, and life. You'll learn how to: Play smart when you play Texas Hold 'Em, blackjack, roulette, dice games, or even the lottery Design your own winnable bar bets to make money and amaze your friends Predict the outcomes of baseball games, know when to "go for two" in football, and anticipate the winners of other sporting events with surprising accuracy Demystify amazing coincidences and distinguish the truly random from the only seemingly random--even keep your iPod's "random" shuffle honest Spot fraudulent data, detect plagiarism, and break codes How to isolate the effects of observation on the thing observedWhether you're a statistics enthusiast who does calculations in your sleep or a civilian who is entertained by clever solutions to interesting problems, Statistics Hacks has tools to give you an edge over the world's slim odds.
Statistics I Essentials
by Emil G. MilewskiREA's Essentials provide quick and easy access to critical information in a variety of different fields, ranging from the most basic to the most advanced. As its name implies, these concise, comprehensive study guides summarize the essentials of the field covered. Essentials are helpful when preparing for exams, doing homework and will remain a lasting reference source for students, teachers, and professionals. Statistics I covers include frequency distributions, numerical methods of describing data, measures of variability, parameters of distributions, probability theory, and distributions.
Statistics II Essentials
by Emil MilewskiREA's Essentials provide quick and easy access to critical information in a variety of different fields, ranging from the most basic to the most advanced. As its name implies, these concise, comprehensive study guides summarize the essentials of the field covered. Essentials are helpful when preparing for exams, doing homework and will remain a lasting reference source for students, teachers, and professionals. Statistics II discusses sampling theory, statistical inference, independent and dependent variables, correlation theory, experimental design, count data, chi-square test, and time series.
Statistics II for Dummies
by Deborah J. RumseyThe ideal supplement and study guide for students preparing for advanced statisticsPacked with fresh and practical examples appropriate for a range of degree-seeking students, Statistics II For Dummies helps any reader succeed in an upper-level statistics course. It picks up with data analysis where Statistics For Dummies left off, featuring new and updated examples, real-world applications, and test-taking strategies for success. This easy-to-understand guide covers such key topics as sorting and testing models, using regression to make predictions, performing variance analysis (ANOVA), drawing test conclusions with chi-squares, and making comparisons with the Rank Sum Test.
Statistics II For Dummies
by Deborah J. RumseyContinue your statistics journey with this all-encompassing reference Completed Statistics through standard deviations, confidence intervals, and hypothesis testing? Then you’re ready for the next step: Statistics II. And there’s no better way to tackle this challenging subject than with Statistics II For Dummies! Get a brief overview of Statistics I in case you need to brush up on earlier topics, and then dive into a full explanation of all Statistic II concepts, including multiple regression, analysis of variance (ANOVA), Chi-square tests, nonparametric procedures, and analyzing large data sets. By the end of the book, you’ll know how to use all the statistics tools together to create a great story about your data. For each Statistics II technique in the book, you get an overview of when and why it’s used, how to know when you need it, step-by-step directions on how to do it, and tips and tricks for working through the solution. You also find: What makes each technique distinct and what the results say How to apply techniques in real life An interpretation of the computer output for data analysis purposes Instructions for using Minitab to work through many of the calculations Practice with a lot of examples With Statistics II For Dummies, you will find even more techniques to analyze a set of data. Get a head start on your Statistics II class, or use this in conjunction with your textbook to help you thrive in statistics!
Statistics in a Nutshell: A Desktop Quick Reference (In A Nutshell (o'reilly) Ser.)
by Sarah Boslaugh<p>Need to learn statistics for your job? Want help passing a statistics course? <i>Statistics in a Nutshell</i> is a clear and concise introduction and reference for anyone new to the subject. Thoroughly revised and expanded, this edition helps you gain a solid understanding of statistics without the numbing complexity of many college texts.</p>
Statistics in a Nutshell
by Sarah Boslaugh Paul Andrew WattersNeed to learn statistics as part of your job, or want some help passing a statistics course? Statistics in a Nutshell is a clear and concise introduction and reference that's perfect for anyone with no previous background in the subject. This book gives you a solid understanding of statistics without being too simple, yet without the numbing complexity of most college texts. You get a firm grasp of the fundamentals and a hands-on understanding of how to apply them before moving on to the more advanced material that follows. Each chapter presents you with easy-to-follow descriptions illustrated by graphics, formulas, and plenty of solved examples. Before you know it, you'll learn to apply statistical reasoning and statistical techniques, from basic concepts of probability and hypothesis testing to multivariate analysis. Organized into four distinct sections, Statistics in a Nutshell offers you:Introductory material: Different ways to think about statistics Basic concepts of measurement and probability theoryData management for statistical analysis Research design and experimental design How to critique statistics presented by others Basic inferential statistics: Basic concepts of inferential statistics The concept of correlation, when it is and is not an appropriate measure of association Dichotomous and categorical data The distinction between parametric and nonparametric statistics Advanced inferential techniques: The General Linear Model Analysis of Variance (ANOVA) and MANOVA Multiple linear regression Specialized techniques: Business and quality improvement statistics Medical and public health statistics Educational and psychological statistics Unlike many introductory books on the subject, Statistics in a Nutshell doesn't omit important material in an effort to dumb it down. And this book is far more practical than most college texts, which tend to over-emphasize calculation without teaching you when and how to apply different statistical tests. With Statistics in a Nutshell, you learn how to perform most common statistical analyses, and understand statistical techniques presented in research articles. If you need to know how to use a wide range of statistical techniques without getting in over your head, this is the book you want.
Statistics in Action, Understanding a World of Data
by Ann E. Watkins Richard L. Scheaffer George W. CobbNIMAC-sourced textbook
Statistics in Clinical and Observational Vaccine Studies (Springer Series in Pharmaceutical Statistics)
by Jozef NautaThis book offers an overview of the statistical methods used in clinical and observational vaccine studies. Pursuing a practical rather than theoretical approach, it presents a range of real-world examples with SAS codes, making the application of the methods straightforward. This revised edition has been significantly expanded to reflect the current interest in this area. It opens with two introductory chapters on the immunology of vaccines to provide readers with the necessary background knowledge. It then continues with an in-depth exploration of the analysis of immunogenicity data. Discussed are, amongst others, maximum likelihood estimation for censored antibody titers, ANCOVA for antibody values, analysis of data of equivalence, and non-inferiority immunogenicity studies. Other topics covered include fitting protection curves to data from vaccine efficacy studies, and the analysis of vaccine safety data. In addition, the book features four new chapters on vaccine field studies: an introductory one, one on randomized vaccine efficacy studies, one on observational vaccine effectiveness studies, and one on the meta-analysis of vaccine efficacy studies. The book offers useful insights for statisticians and epidemiologists working in the pharmaceutical industry or at vaccines institutes, as well as graduate students interested in pharmaceutical statistics.
Statistics in Clinical Vaccine Trials
by Jozef NautaThis monograph offers well-founded training and expertise on the statistical analysis of data from clinical vaccine trials, i.e., immunogenicity and vaccine field efficacy studies. The book's scope is practical rather than theoretical. It opens with two introductory chapters on the immunology of vaccines to provide readers with the necessary background knowledge. It then continues with an in-depth exploration of the statistical methodology. Many real-life examples and SAS codes are presented, making application of the methods straightforward. Topics discussed include maximum likelihood estimation for censored antibody titers, ANCOVA for antibody values, analysis of equivalence and non-inferiority immunogenicity trial data, analysis of data from vaccine field efficacy trials (including data from studies with recurrent infection data), fitting protection curves to data of challenge or field efficacy studies, and the analysis of vaccine safety data.
Statistics In Context
by Barbara BlatchleyStatistics in Context offers a fresh approach to teaching statistics. Designed to reduce students' fear of numbers, the text aims to put statistics-wary readers at ease with uncomplicated explanations and practical examples drawn from real research and everyday life. Written in lively, accessible prose, the narrative describes the who, what, when, where, and why, and also the how, of statistics. DISTINCTIVE FEATURES * "Everyday Statistics" boxes examine practical applications and relate them to the themes of the chapter * "The Historical Context" features tell the story of how different statistical procedures developed * "CheckPoint "exercises give students the chance to review the material and assess their understanding at the end of each section * "Think About It" boxes challenge students to apply what they have learned to a difficult problem * A variety of figures, tables, and worked examples guide students step by step through the calculations described in the text * Abundant end-of-chapter practice problems give students many opportunities to test their mastery of the procedures described in the text * The "Using Statistical Software" supplement teaches students how to perform statistical analysis using either SPSS or R
Statistics in Early Childhood and Primary Education: Supporting Early Statistical and Probabilistic Thinking (Early Mathematics Learning and Development)
by Aisling Leavy Maria Meletiou-Mavrotheris Efi PaparistodemouThis compilation focuses on the theory and conceptualisation of statistics and probability in the early years and the development of young children’s (ages 3-10) understanding of data and chance. It provides a comprehensive overview of cutting-edge international research on the development of young learners’ reasoning about data and chance in formal, informal, and non-formal educational contexts. The authors share insights into young children’s statistical and probabilistic reasoning and provide early childhood educators and researchers with a wealth of illustrative examples, suggestions, and practical strategies on how to address the challenges arising from the introduction of statistical and probabilistic concepts in pre-school and school curricula. This collection will inform practices in research and teaching by providing a detailed account of current best practices, challenges, and issues, and of future trends and directions in early statistical and probabilistic learning worldwide. Further, it will contribute to future research and theory building by addressing theoretical, epistemological, and methodological considerations regarding the design of probability and statistics learning environments for young children.
Statistics in Engineering: With Examples in MATLAB® and R, Second Edition (Chapman & Hall/CRC Texts in Statistical Science)
by Andrew Metcalfe David Green Tony Greenfield Mayhayaudin Mansor Andrew Smith Jonathan TukeEngineers are expected to design structures and machines that can operate in challenging and volatile environments, while allowing for variation in materials and noise in measurements and signals. Statistics in Engineering, Second Edition: With Examples in MATLAB and R covers the fundamentals of probability and statistics and explains how to use these basic techniques to estimate and model random variation in the context of engineering analysis and design in all types of environments. The first eight chapters cover probability and probability distributions, graphical displays of data and descriptive statistics, combinations of random variables and propagation of error, statistical inference, bivariate distributions and correlation, linear regression on a single predictor variable, and the measurement error model. This leads to chapters including multiple regression; comparisons of several means and split-plot designs together with analysis of variance; probability models; and sampling strategies. Distinctive features include: All examples based on work in industry, consulting to industry, and research for industry Examples and case studies include all engineering disciplines Emphasis on probabilistic modeling including decision trees, Markov chains and processes, and structure functions Intuitive explanations are followed by succinct mathematical justifications Emphasis on random number generation that is used for stochastic simulations of engineering systems, demonstration of key concepts, and implementation of bootstrap methods for inference Use of MATLAB and the open source software R, both of which have an extensive range of statistical functions for standard analyses and also enable programing of specific applications Use of multiple regression for times series models and analysis of factorial and central composite designs Inclusion of topics such as Weibull analysis of failure times and split-plot designs that are commonly used in industry but are not usually included in introductory textbooks Experiments designed to show fundamental concepts that have been tested with large classes working in small groups Website with additional materials that is regularly updated Andrew Metcalfe, David Green, Andrew Smith, and Jonathan Tuke have taught probability and statistics to students of engineering at the University of Adelaide for many years and have substantial industry experience. Their current research includes applications to water resources engineering, mining, and telecommunications. Mahayaudin Mansor worked in banking and insurance before teaching statistics and business mathematics at the Universiti Tun Abdul Razak Malaysia and is currently a researcher specializing in data analytics and quantitative research in the Health Economics and Social Policy Research Group at the Australian Centre for Precision Health, University of South Australia. Tony Greenfield, formerly Head of Process Computing and Statistics at the British Iron and Steel Research Association, is a statistical consultant. He has been awarded the Chambers Medal for outstanding services to the Royal Statistical Society; the George Box Medal by the European Network for Business and Industrial Statistics for Outstanding Contributions to Industrial Statistics; and the William G. Hunter Award by the American Society for Quality.
Statistics in Environmental Sciences
by Valerie DavidStatistical tools are indispensable for the environmental sciences. They have become an integral part of the scientific process, from the development of the sampling plan to the obtainment of results. Statistics in Environmental Sciences provides the foundation for the interpretation of quantitative data (basic vocabulary, main laws of probabilities, etc.) and the thinking behind sampling and experimental methodology. It also introduces the principles of statistical tests such as decision theory and examines the key choices in statistical tests, while keeping the established objectives in mind. The book examines the most used statistics in the field of environmental sciences. Detailed descriptions based on concrete examples are given, as well as descriptions obtained through the use of the free software R (whose usage is also presented).
Statistics in Food Science and Nutrition
by Are Hugo PrippMany statistical innovations are linked to applications in food science. For example, the student t-test (a statistical method) was developed to monitor the quality of stout at the Guinness Brewery and multivariate statistical methods are applied widely in the spectroscopic analysis of foods. Nevertheless, statistical methods are most often associated with engineering, mathematics, and the medical sciences, and are rarely thought to be driven by food science. Consequently, there is a dearth of statistical methods aimed specifically at food science, forcing researchers to utilize methods intended for other disciplines. The objective of this Brief will be to highlight the most needed and relevant statistical methods in food science and thus eliminate the need to learn about these methods from other fields. All methods and their applications will be illustrated with examples from research literature.
Statistics in MATLAB: A Primer (Chapman & Hall/CRC Computer Science & Data Analysis)
by Wendy L. Martinez MoonJung ChoThis primer provides an accessible introduction to MATLAB version 8 and its extensive functionality for statistics. Fulfilling the need for a practical user's guide, the book covers capabilities in the main MATLAB package, the Statistics Toolbox, and the student version of MATLAB, presenting examples of how MATLAB can be used to analyze data. It explains how to determine what method should be used for analysis, and includes figures, visual aids, and access to a companion website with data sets and additional examples.