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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 BoslaughNeed to learn statistics for your job? Want help passing a statistics course? Statistics in a Nutshell 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.Each chapter presents easy-to-follow descriptions, along with graphics, formulas, solved examples, and hands-on exercises. If you want to perform common statistical analyses and learn a wide range of techniques without getting in over your head, this is your book.Learn basic concepts of measurement and probability theory, data management, and research designDiscover basic statistical procedures, including correlation, the t-test, the chi-square and Fisherâ??s exact tests, and techniques for analyzing nonparametric dataLearn advanced techniques based on the general linear model, including ANOVA, ANCOVA, multiple linear regression, and logistic regressionUse and interpret statistics for business and quality improvement, medical and public health, and education and psychologyCommunicate with statistics and critique statistical information presented by others
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: A Canadian Outlook
by Jerald F. LawlessCommissioned by the Statistical Society of Canada (SSC), Statistics in Action: A Canadian Outlook helps both general readers and users of statistics better appreciate the scope and importance of statistics. It presents the ways in which statistics is used while highlighting key contributions that Canadian statisticians are making to science, techno
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 Drug Research: Methodologies and Recent Developments
by Shein-Chung Chow Jun ShaoEmphasizing the role of good statistical practices (GSP) in drug research and formulation, this book outlines important statistics applications for each stage of pharmaceutical development to ensure the valid design, analysis, and assessment of drug products under investigation and establish the safety and efficacy of pharmaceutical compounds. Cove
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 Human Genetics and Molecular Biology (Chapman & Hall/CRC Texts in Statistical Science)
by Cavan ReillyFocusing on the roles of different segments of DNA, Statistics in Human Genetics and Molecular Biology provides a basic understanding of problems arising in the analysis of genetics and genomics. It presents statistical applications in genetic mapping, DNA/protein sequence alignment, and analyses of gene expression data from microarray experiments.
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.
Statistics in Musicology
by Jan BeranTraditionally, statistics and music are not generally associated with each other. However, ...intelligent... music software, computer digitization, and other advanced techniques and technologies have precipitated the need for standard statistical models to answer basic musicological questions. Statistics In Musicology presents an unprecedented intr
Statistics in Plain English
by Timothy C. UrdanStatistics in Plain English is a straightforward, conversational introduction to statistics that delivers exactly what its title promises. Each chapter begins with a brief overview of a statistic (or set of statistics) that describes what the statistic does and when to use it, followed by a detailed step-by-step explanation of how the statistic works and exactly what information it provides. Chapters also include an example of the statistic (or statistics) used in real-world research, "Worked Examples," "Writing It Up" sections that demonstrate how to write about each statistic, "Wrapping Up and Looking Forward" sections, and practice work problems. Thoroughly updated throughout, this edition features several key additions and changes. First, a new chapter on person-centered analyses, including cluster analysis and latent class analysis (LCA) has been added, providing an important alternative to the more commonly used variable-centered analyses (e.g., t tests, ANOVA, regression). Next, the chapter on non-parametric statistics has been enhanced with in-depth descriptions of Mann-Whitney U, Kruskal-Wallis, and Wilcoxon Signed-Rank analyses, in addition to the detailed discussion of the Chi-square statistic found in the previous edition. These nonparametric statistics are widely used when dealing with nonnormally distributed data. This edition also includes more information about the assumptions of various statistics, including a detailed explanation of the assumptions and consequences of violating the assumptions of regression, as well as more coverage of the normal distribution in statistics. Finally, the book features a multitude of real-world examples throughout to aid student understanding and provides them with a solid understanding of how several statistics techniques commonly used by researchers in the social sciences work. Statistics in Plain English is suitable for a wide range of readers, including students taking their first statistics course, professionals who want to refresh their statistical memory, and undergraduate or graduate students who need a concise companion to a more complicated text used in their class. The text works as a standalone or as a supplement and covers a range of statistical concepts from descriptive statistics to factor analysis and person-centered analyses.
Statistics in Plain English, Fourth Edition
by Timothy C. UrdanThis introductory textbook provides an inexpensive, brief overview of statistics to help readers gain a better understanding of how statistics work and how to interpret them correctly. Each chapter describes a different statistical technique, ranging from basic concepts like central tendency and describing distributions to more advanced concepts such as t tests, regression, repeated measures ANOVA, and factor analysis. Each chapter begins with a short description of the statistic and when it should be used. This is followed by a more in-depth explanation of how the statistic works. Finally, each chapter ends with an example of the statistic in use, and a sample of how the results of analyses using the statistic might be written up for publication. A glossary of statistical terms and symbols is also included. Using the author's own data and examples from published research and the popular media, the book is a straightforward and accessible guide to statistics. New features in the fourth edition include: sets of work problems in each chapter with detailed solutions and additional problems online to help students test their understanding of the material, new "Worked Examples" to walk students through how to calculate and interpret the statistics featured in each chapter, new examples from the author's own data and from published research and the popular media to help students see how statistics are applied and written about in professional publications, many more examples, tables, and charts to help students visualize key concepts, clarify concepts, and demonstrate how the statistics are used in the real world. a more logical flow, with correlation directly preceding regression, and a combined glossary appearing at the end of the book, a Quick Guide to Statistics, Formulas, and Degrees of Freedom at the start of the book, plainly outlining each statistic and when students should use them, greater emphasis on (and description of) effect size and confidence interval reporting, reflecting their growing importance in research across the social science disciplines an expanded website at www.routledge.com/cw/urdan with PowerPoint presentations, chapter summaries, a new test bank, interactive problems and detailed solutions to the text's work problems, SPSS datasets for practice, links to useful tools and resources, and videos showing how to calculate statistics, how to calculate and interpret the appendices, and how to understand some of the more confusing tables of output produced by SPSS. Statistics in Plain English, Fourth Edition is an ideal guide for statistics, research methods, and/or for courses that use statistics taught at the undergraduate or graduate level, or as a reference tool for anyone interested in refreshing their memory about key statistical concepts. The research examples are from psychology, education, and other social and behavioral sciences.
Statistics in Plain English, Third Edition
by Timothy C. UrdanThis inexpensive paperback provides a brief, simple overview of statistics to help readers gain a better understanding of how statistics work and how to interpret them correctly. Each chapter describes a different statistical technique, ranging from basic concepts like central tendency and describing distributions to more advanced concepts such as t tests, regression, repeated measures ANOVA, and factor analysis. Each chapter begins with a short description of the statistic and when it should be used. This is followed by a more in-depth explanation of how the statistic works. Finally, each chapter ends with an example of the statistic in use, and a sample of how the results of analyses using the statistic might be written up for publication. A glossary of statistical terms and symbols is also included. New features in the third edition include: a new chapter on Factor and Reliability Analysis especially helpful to those who do and/or read survey research, new "Writing it Up" sections demonstrate how to write about and interpret statistics seen in books and journals, a website at http://www.psypress.com/statistics-in-plain-english with PowerPoint presentations, interactive problems (including an overview of the problem's solution for Instructors) with an IBM SPSS dataset for practice, videos of the author demonstrating how to calculate and interpret most of the statistics in the book, links to useful websites, and an author blog, new section on understanding the distribution of data (ch. 1) to help readers understand how to use and interpret graphs, many more examples, tables, and charts to help students visualize key concepts. Statistics in Plain English, Third Edition is an ideal supplement for statistics, research methods, and/or for courses that use statistics taught at the undergraduate or graduate level, or as a reference tool for anyone interested in refreshing their memory about key statistical concepts. The research examples are from psychology, education, and other social and behavioral sciences.
Statistics in Precision Health: Theory, Methods and Applications (ICSA Book Series in Statistics)
by Ding-Geng Chen Yichuan ZhaoThis book discusses statistical methods and their innovative applications in precision health. It serves as a valuable resource to foster the development of this growing field within the context of the big data era. The chapters cover a wide range of topics, including foundational principles, statistical theories, new procedures, advanced methods, and practical applications in precision medicine. Particular attention is devoted to the interplay between precision health, big data, and mobile health research, while also exploring precision medicine's role in clinical trials, electronic health record data analysis, survival analysis, and genomic studies. Targeted at data scientists, statisticians, graduate students, and researchers in academia, industry, and government, this book offers insights into the latest advances in personalized medicine using advanced statistical techniques.
Statistics in Psychology Using R and SPSS
by Klaus Kubinger Dieter Rasch Takuya YanagidaStatistics in Psychology covers all statistical methods needed in education and research in psychology. This book looks at research questions when planning data sampling, that is to design the intended study and to calculate the sample sizes in advance. In other words, no analysis applies if the minimum size is not determined in order to fulfil certain precision requirements.The book looks at the process of empirical research into the following seven stages:Formulation of the problemStipulation of the precision requirementsSelecting the statistical model for the planning and analysisThe (optimal) design of the experiment or surveyPerforming the experiment or the surveyStatistical analysis of the observed resultsInterpretation of the results.
Statistics in Research and Development (Chapman & Hall/CRC Texts in Statistical Science)
by R. CaulcuttMany scientists and technologists would like to carry out their own statistical analyses without reference to a professional statistician. Often, however, they have no knowledge of statistics or otherwise do not know how to apply it to research and development problems. The first edition of Statistics in Research and Development was written for the
Statistics in Social Work: An Introduction to Practical Applications
by Professor Amy BatchelorUnderstanding statistical concepts is essential for social work professionals. It is key to understanding research and reaching evidence-based decisions in your own practice—but that is only the beginning. If you understand statistics, you can determine the best interventions for your clients. You can use new tools to monitor and evaluate the progress of your client or team. You can recognize biased systems masked by complex models and the appearance of scientific neutrality. For social workers, statistics are not just math, they are a critical practice tool.This concise and approachable introduction to statistics limits its coverage to the concepts most relevant to social workers. Statistics in Social Work guides students through concepts and procedures from descriptive statistics and correlation to hypothesis testing and inferential statistics. Besides presenting key concepts, it focuses on real-world examples that students will encounter in a social work practice. Using concrete illustrations from a variety of potential concentrations and populations, Amy Batchelor creates clear connections between theory and practice—and demonstrates the important contributions statistics can make to evidence-based and rigorous social work practice.
Statistics in the 21st Century (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)
by Adrian E. Raftery Martin A. Tanner Martin T. WellsThis volume discusses an important area of statistics and highlights the most important statistical advances. It is divided into four sections: statistics in the life and medical sciences, business and social science, the physical sciences and engineering, and theory and methods of statistics.