There are plenty of books on specialized risk topics but few that deal with the broad diversity and daily applicability of this subject. Risk applications require a robust knowledge of many attributes of this seemingly simple subject. This book teaches the reader through examples and case studies the fundamental (and subtle) aspects of risk - regardless of the specific situation. The text allows the reader to understand the concept of risk analysis while not getting too involved in the mathematics; in this method the reader can apply these techniques across a wide range of situations. The second edition includes new examples from NASA and several other industries as well as new case studies from legal databases. The many real-life discussion topics enable the reader to form an understanding of the concepts of risk and risk management and apply them to day-to-day issues.

IBM SPSS Statistics 23 Step by Step: A Simple Guide and Reference, 14e, takes a straightforward, step-by-step approach that makes SPSS software clear to beginners and experienced researchers alike. Extensive use of vivid, four-color screen shots, clear writing, and step-by-step boxes guide readers through the program. Exercises at the end of each chapter support students by providing additional opportunities to practice using SPSS. All datasets used in the book are available for download at: https://www.routledge.com/products/ 9780134320250

IBM SPSS Statistics 26 Step by Step: A Simple Guide and Reference, sixteenth edition, takes a straightforward, step-by-step approach that makes SPSS software clear to beginners and experienced researchers alike. Extensive use of four-color screen shots, clear writing, and step-by-step boxes guide readers through the program. Output for each procedure is explained and illustrated, and every output term is defined. Exercises at the end of each chapter support students by providing additional opportunities to practice using SPSS. This book covers the basics of statistical analysis and addresses more advanced topics such as multi-dimensional scaling, factor analysis, discriminant analysis, measures of internal consistency, MANOVA (between- and within-subjects), cluster analysis, Log-linear models, logistic regression and a chapter describing residuals. Back matter includes a description of data files used in exercises, an exhaustive glossary, suggestions for further reading and a comprehensive index. IMB SPSS Statistics 26 Step by Step is distributed in 85 countries, has been an academic best seller through most of the earlier editions, and has proved invaluable aid to thousands of researchers and students. New to this edition: Screenshots, explanations, and step-by-step boxes have been fully updated to reflect SPSS 26 How to handle missing data has been revised and expanded and now includes a detailed explanation of how to create regression equations to replace missing data More explicit coverage of how to report APA style statistics; this primarily shows up in the Output sections of Chapters 6 through 16, though changes have been made throughout the text.

IBM SPSS Statistics 25 Step by Step: A Simple Guide and Reference, fifteenth edition, takes a straightforward, step-by-step approach that makes SPSS software clear to beginners and experienced researchers alike. Extensive use of four-color screen shots, clear writing, and step-by-step boxes guide readers through the program. Exercises at the end of each chapter support students by providing additional opportunities to practice using SPSS. This book covers both the basics of descriptive statistical analysis using SPSS through to more advanced topics such as multiple regression, multidimensional scaling and MANOVA, including instructions for Windows and Mac. This makes it ideal for both undergraduate statistics courses and for postgraduates looking to further develop their statistics and SPSS knowledge. New to this edition: Updated throughout to SPSS 25 Updated / restructured material on: Chart Builder; Univariate ANOVA; moderation on two- and three-way ANOVA; and Factor Analytic Techniques (formerly Factor Analysis structure) New material on computing z and T scores, and on computing z scores within descriptive statistics Clearer in-chapter links between the type of data and type of research question that the procedure can answer Updated / additional datasets, exercises, and expanded Companion Website material, including Powerpoint slides for instructors

Praise for Bayes Rules!: An Introduction to Applied Bayesian Modeling “A thoughtful and entertaining book, and a great way to get started with Bayesian analysis.” Andrew Gelman, Columbia University “The examples are modern, and even many frequentist intro books ignore important topics (like the great p-value debate) that the authors address. The focus on simulation for understanding is excellent.” Amy Herring, Duke University “I sincerely believe that a generation of students will cite this book as inspiration for their use of – and love for – Bayesian statistics. The narrative holds the reader’s attention and flows naturally – almost conversationally. Put simply, this is perhaps the most engaging introductory statistics textbook I have ever read. [It] is a natural choice for an introductory undergraduate course in applied Bayesian statistics." Yue Jiang, Duke University “This is by far the best book I’ve seen on how to (and how to teach students to) do Bayesian modeling and understand the underlying mathematics and computation. The authors build intuition and scaffold ideas expertly, using interesting real case studies, insightful graphics, and clear explanations. The scope of this book is vast – from basic building blocks to hierarchical modeling, but the authors’ thoughtful organization allows the reader to navigate this journey smoothly. And impressively, by the end of the book, one can run sophisticated Bayesian models and actually understand the whys, whats, and hows.” Paul Roback, St. Olaf College “The authors provide a compelling, integrated, accessible, and non-religious introduction to statistical modeling using a Bayesian approach. They outline a principled approach that features computational implementations and model assessment with ethical implications interwoven throughout. Students and instructors will find the conceptual and computational exercises to be fresh and engaging.” Nicholas Horton, Amherst College An engaging, sophisticated, and fun introduction to the field of Bayesian statistics, Bayes Rules!: An Introduction to Applied Bayesian Modeling brings the power of modern Bayesian thinking, modeling, and computing to a broad audience. In particular, the book is an ideal resource for advanced undergraduate statistics students and practitioners with comparable experience. Bayes Rules! empowers readers to weave Bayesian approaches into their everyday practice. Discussions and applications are data driven. A natural progression from fundamental to multivariable, hierarchical models emphasizes a practical and generalizable model building process. The evaluation of these Bayesian models reflects the fact that a data analysis does not exist in a vacuum. Features • Utilizes data-driven examples and exercises. • Emphasizes the iterative model building and evaluation process. • Surveys an interconnected range of multivariable regression and classification models. • Presents fundamental Markov chain Monte Carlo simulation. • Integrates R code, including RStan modeling tools and the bayesrules package. • Encourages readers to tap into their intuition and learn by doing. • Provides a friendly and inclusive introduction to technical Bayesian concepts. • Supports Bayesian applications with foundational Bayesian theory.

A fundamental resource for preparing Australia's primary industries for the challenges and opportunities of climate change for primary industry professionals, land managers, policy makers. researchers and students.

Thinking and reasoning are key activities for human beings. In this book a distinguished set of contributors provides a wide readership with up-to-date scientific advances in the developmental psychology of thinking and reasoning, both at the theoretical and empirical levels. The first part of the book illustrates how modern approaches to the study of thinking and reasoning have gone beyond the Piagetian legacy: through the investigation of avenues previously not explored, and by demonstrating that young children have higher capacities than was assumed within the Piagetian tradition. The second part focuses upon theoretical and empirical investigations of the interplay between logic and intuition in reasoning and decision making, and how these forms of thinking evolve with age, through the general framework of what is known as dual-process theories. Contrary to Piaget’s claim, it becomes apparent that elaborate adult reasoning could rely on some form of intuition. The Development of Thinking and Reasoning provides psychologists, educators and everyone interested in child development with an integrated and up-to-date series of chapters, written by prominent specialists in the areas of thinking, reasoning, and decision making.