LibGuides: Statistical Methods: Books & eBooks

Featured eBooks

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PDQ Statistics PDQ Statistics, Third Edition offers an expert overview of all major statistical methods, giving the reader a thorough understanding of statistics and how they are used in research articles. The book covers the major categories - variable and descriptive statistics, parametric statistics, non-parametric statistics, and multivariate statistics. The explanations are clear, succinct, and loaded with practical examples. This third edition includes new chapters on hierarchical and logistic regression, path analysis, and structural equation modeling. PDQ Statistics, Third Edition also helps the reader identify those statistical analyses with basic flaws in design or research. The book's attractive design and humorous writing style make the subject matter accessible and engaging.
Principles of Probability and Statistics Probability and statistics are essential tools for nearly every field of analysis. This volume presents the basic principles and applications of probability and statistics, as well as methods of data collection, proper interpretation of data, and many other topics, allowing readers to acquire a sound knowledge base for their understanding of probability and statistics in the real world.
Also available in print: QA276.18 N53 2024
Statistics Statistics is a class that is required in many college majors, and it's an increasingly popular Advanced Placement high school course. In addition to math and technical students, many business and liberal arts students are required to take it as a fundamental component of their majors. A knowledge of statistical interpretation is vital for many careers. Idiot's Guides: Statistics explains the fundamental tenets in language anyone can understand.
Statistics for the Utterly Confused Statistics for the Utterly Confused, Second Edition. When it comes to understanding statistics, even good students can be confused. Perfect for students in any introductory non-calculus-based statistics course, and equally useful to professionals working in the world, Statistics for the Utterly Confused is your ticket to success. Statistical concepts are explained step-by-step and applied to such diverse fields as business, economics, finance, and more. The message of Statistics for the Utterly Confused is simple: you don't have to be confused anymore. Updated and expanded to give you the latest changes in the field, this up-to-the-minute edition includes many new examples of Excel output, the most widely used of all statistics programs; a new chapter on Analysis of Variance (ANOVA); and 200 additions to the 700 self-testing questions and answers. The expert author's Web site also gives you tons of fresh examples, practice problems, and strategies--so you can go from utterly confused to totally prepared in no time! Inside, you'll discover how to: Grasp the meaning of everyday statistical concepts; Find out what's probable and what isn't; Read, understand, and solve statistics problems; Improve your scores on exams; Use your skills in any field.
Statistics from A to Z Statistics 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).
Statistics in Plain English Statistics in Plain English, Second Edition provides a brief, simple overview of statistics to help readers gain a better understanding of how statistics work and how to interpret them correctly. It presents brief explanations of statistical concepts and techniques in simple, everyday language. Each self-contained chapter consists of three sections. The first describes the statistic, including how it is used and what information it provides. The second section reviews how it works, how to calculate the formula, the strengths and weaknesses of the technique, and the conditions needed for its use. The final section provides examples that use and interpret the statistic. A glossary of terms and symbols is also included.
Straightforward Statistics: Understanding the Tools of Research Straightforward Statistics: Understanding the Tools of Research is a clear and direct introduction to statistics for the social, behavioral, and life sciences. Based on the author's extensive experience teaching undergraduate statistics, this book provides a narrative presentation of the core principles that provide the foundation for modern-day statistics. With step-by-step guidance on the nuts and bolts of computing these statistics, the book includes detailed tutorials how to use state-of-the-art software, SPSS, to compute the basic statistics employed in modern academic and applied research. Across 13 succinct chapters, this text presents statistics using a conceptual approach along with information on the relevance of the different tools in different contexts and summaries of current research examples. Students should find this book easy useful and engaging in its presentation while instructors should find it detailed, comprehensive, accessible, and helpful in complementing a basic course in statistics.

Featured Print Books

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Causal Inference in Statistics Causality is central to the understanding and use of data. Without an understanding of cause-effect relationships, we cannot use data to answer questions as basic as "Does this treatment harm or help patients?" But though hundreds of introductory texts are available on statistical methods of data analysis, until now, no beginner-level book has been written about the exploding arsenal of methods that can tease causal information from data. Causal Inference in Statistics fills that gap. Using simple examples and plain language, the book lays out how to define causal parameters; the assumptions necessary to estimate causal parameters in a variety of situations; how to express those assumptions mathematically; whether those assumptions have testable implications; how to predict the effects of interventions; and how to reason counterfactually. These are the foundational tools that any student of statistics needs to acquire in order to use statistical methods to answer causal questions of interest. This book is accessible to anyone with an interest in interpreting data, from undergraduates, professors, researchers, or to the interested layperson. Examples are drawn from a wide variety of fields, including medicine, public policy, and law; a brief introduction to probability and statistics is provided for the uninitiated; and each chapter comes with study questions to reinforce the readers understanding.
Complete Idiot's Guide to Statistics Like other science/math Complete Idiot's Guides, this book is aimed at high school and college students who need to take statistics to fulfill a degree requirement and follows a standard statistics curriculum. But because statistics is a mathematical topic taught to many non-mathematical people in a manner that often overwhelms them, the presentation of the material focuses on the concepts rather than the computations. Students learn the required math involved, but the focus is on interpreting final results as opposed to the details of the technique.
How Data Happened: A History from the Age of Reason to the Age of Algorithms From facial recognition-capable of checking us onto flights or identifying undocumented residents-to automated decision systems that inform everything from who gets loans to who receives bail, each of us moves through a world determined by data-empowered algorithms. But these technologies didn't just appear: they are part of a history that goes back centuries, from the census enshrined in the US Constitution to the birth of eugenics in Victorian Britain to the development of Google search. Expanding on the popular course they created at Columbia University, Chris Wiggins and Matthew L. Jones illuminate the ways in which data has long been used as a tool and a weapon in arguing for what is true, as well as a means of rearranging or defending power. By understanding the trajectory of data-where it has been and where it might yet go-Wiggins and Jones argue that we can understand how to bend it to ends that we collectively choose, with intentionality and purpose.
Introduction to Probability and Statistics Written by renowned experts in their respective fields, Schaum's Outlines cover everything from math to science, nursing to language. The main feature for all these books is the solved problems. Step-by-step, authors walk readers through coming up with solutions to exercises in their topic of choice. 387 solved problems. Covers all probability fundamentals. No calculus needed. Supports and supplements the leading probability and statistics textbooks. Appropriate for the following courses: Introduction to Probability and Statistics, Probability, Statistics, Introduction to Statistics. Detailed explanations and practice problems in probability and statistics. Comprehensive review of specialized topics in probability and statistics.
Principles of Probability and Statistics Probability and statistics are essential tools for nearly every field of analysis. This volume presents the basic principles and applications of probability and statistics, as well as methods of data collection, proper interpretation of data, and many other topics, allowing readers to acquire a sound knowledge base for their understanding of probability and statistics in the real world.
Quantitative Social Science An introductory textbook on data analysis and statistics written especially for students in the social sciences and allied fields. Quantitative analysis is an increasingly essential skill for social science research, yet students in the social sciences and related areas typically receive little training in it--or if they do, they usually end up in statistics classes that offer few insights into their field. This textbook is a practical introduction to data analysis and statistics written especially for undergraduates and beginning graduate students in the social sciences and allied fields, such as economics, sociology, public policy, and data science. Quantitative Social Science engages directly with empirical analysis, showing students how to analyze data using the R programming language and to interpret the results--it encourages hands-on learning, not paper-and-pencil statistics. More than forty data sets taken directly from leading quantitative social science research illustrate how data analysis can be used to answer important questions about society and human behavior. Proven in the classroom, this one-of-a-kind textbook features numerous additional data analysis exercises and interactive R programming exercises, and also comes with supplementary teaching materials for instructors. Written especially for students in the social sciences and allied fields, including economics, sociology, public policy, and data science Provides hands-on instruction using R programming, not paper-and-pencil statistics. Includes more than forty data sets from actual research for students to test their skills on. Covers data analysis concepts such as causality, measurement, and prediction, as well as probability and statistical tools. Features a wealth of supplementary exercises, including additional data analysis exercises and interactive programming exercises. Offers a solid foundation for further study. Comes with additional course materials online, including notes, sample code, exercises and problem sets with solutions, and lecture slides.
Schaum's Outline of Theory and Problems of Beginning Statistics A concise guide to the complete course in beginning statistics. More than 600 fully worked problems of varying difficulty. Hundreds more practice problems.
The Seven Pillars of Statistical Wisdom What gives statistics its unity as a science? Stephen Stigler sets forth the seven foundational ideas of statistics--a scientific discipline related to but distinct from mathematics and computer science. Even the most basic idea--aggregation, exemplified by averaging--is counterintuitive. It allows one to gain information by discarding information, namely, the individuality of the observations. Stigler's second pillar, information measurement, challenges the importance of "big data" by noting that observations are not all equally important: the amount of information in a data set is often proportional to only the square root of the number of observations, not the absolute number. The third idea is likelihood, the calibration of inferences with the use of probability. Intercomparison is the principle that statistical comparisons do not need to be made with respect to an external standard. The fifth pillar is regression, both a paradox (tall parents on average produce shorter children; tall children on average have shorter parents) and the basis of inference, including Bayesian inference and causal reasoning. The sixth concept captures the importance of experimental design--for example, by recognizing the gains to be had from a combinatorial approach with rigorous randomization. The seventh idea is the residual: the notion that a complicated phenomenon can be simplified by subtracting the effect of known causes, leaving a residual phenomenon that can be explained more easily. The Seven Pillars of Statistical Wisdom presents an original, unified account of statistical science that will fascinate the interested layperson and engage the professional statistician.
Statistics DeMystified Trying to understand statistics but feeling a bit uncertain? Here's your solution. Statistics Demystifed, Second Edition helps you master this fundamental topic with ease. Written in a step-by-step format, this practical guide begins by reviewing background mathematics, probability basics, and descriptive measures. The book goes on to demonstrate statistics in action with coverage of sampling, estimation, hypotheses, prediction, regression, correlation, causation, order, and chaos. Detailed examples, concise explanations, and worked-out problems make it easy to understand the material, and end-of-chapter quizzes and a final exam help reinforce learning. It's a no-brainer! You'll learn about: The probability fallacy; Random sampling; Estimation; Assumptions and testing; Chaos, bounds, and randomness; Variance and standard deviation; Correlation and cause/effect relations; The use of statistics in medicine, law, math, and engineering. Simple enough for a beginner, but challenging enough for an advanced student, Statistics Demystified, Second Edition helps you master this essential subject.
The Tao of Statistics This Second Edition of The Tao of Statistics: A Path to Understanding (With No Math) provides a reader-friendly approach to statistics in plain English. Unlike other statistics books, this text explains what statistics mean and how they are used, rather than how to calculate them. The book walks readers through basic concepts as well as some of the most complex statistical models in use. The Second Edition adds coverage of big data to better address its impact on p-values and other key concepts; material on small data to show readers how to handle data with fewer data points than optimal; and other new topics like missing data and effect sizes. The book's two characters (a high school principal and a director of public health) return in the revised edition, with their examples expanded and updated with reference to contemporary concerns in the fields of education and health.