This book describes methods for designing and analyzing experiments that are conducted using a computer code, a computer experiment, and, when possible, a physical experiment. Computer experiments continue to increase in popularity as surrogates for and adjuncts to physical experiments. Since the publication of the first edition, there have been many methodological advances and software developments to implement these new methodologies. The computer experiments literature has emphasized the construction of algorithms for various data analysis tasks (design construction, prediction, sensitivity analysis, calibration among others), and the development of web-based repositories of designs for immediate application. While it is written at a level that is accessible to readers with Masters-level training in Statistics, the book is written in sufficient detail to be useful for practitioners and researchers. New to this revised and expanded edition: * An expanded presentation of basic material on computer experiments and Gaussian processes with additional simulations and examples * A new comparison of plug-in prediction methodologies for real-valued simulator output * An enlarged discussion of space-filling designs including Latin Hypercube designs (LHDs), near-orthogonal designs, and nonrectangular regions * A chapter length description of process-based designs for optimization, to improve good overall fit, quantile estimation, and Pareto optimization * A new chapter describing graphical and numerical sensitivity analysis tools * Substantial new material on calibration-based prediction and inference for calibration parameters * Lists of software that can be used to fit models discussed in the book to aid practitioners

Now available with Macmillan's new online learning tool Achieve, the ninth edition of The Basic Practice of Statistics 9e teaches statistical thinking by guiding students through an investigative process of problem-solving with pedagogy designed to help students of all levels. Examples and exercises from a wide variety of topic areas use current, real data to provide students insight into how and why statistics are used to make decisions in the real world. Achieve for The Basic Practice of Statistics connects the trusted Four-Step problem-solving approach and real world examples in the book to rich digital resources that foster further understanding and application of statistics. Assets in Achieve support learning before, during, and after class for students, while providing instructors with class performance analytics in an easy-to-use interface. Achieve Online Homework Macmillan's new online learning tool Achieve features intuitive design, assessment, insights, and reporting built with the direct input of students, educators, and our learning science team. Achieve for The Basic Practice of Statistics features: Learning Objectives tagged to all assessments within Achieve. In-Class Activity Guides to facilitate active learning during class time. over 3,000 homework questions, each with hints, answer-specific feedback, and a fully worked solution. LearningCurve adaptive quizzing. an interactive e-book, powered by VitalSource. multimedia student resources, such as interactive applets and videos. data sets for common statistical software, video technology manuals, and access to Macmillan's proprietary statistical software, CrunchIt! Content Updates to the Ninth Edition: Examples and exercises more clearly emphasize the decision-making process. Chapter Summaries and Review Chapters have been revised to help students check their knowledge and review for exams. - Summaries are in concise list form, and Skills Reviews (in Review Chapters) refer back to relevant chapter sections. Data in examples and exercises have been updated for currency, and new examples and exercises explore contemporary issues such as social media usage.

This practical textbook by David Moore and William Notz introduces a conceptual approach to statistics and shows students how use these ideas to think about the statistical claims they see every day from polls, campaigns, advertising, public policy, and many different fields of study. The ultimate goal is to equip students with solid statistical reasoning skills that will help them understand impact of statistics on all aspects of their lives.

This book describes methods for designing and analyzing experiments conducted using computer code in lieu of a physical experiment. It discusses how to select the values of the factors at which to run the code (the design of the computer experiment). It also provides techniques for analyzing the resulting data so as to achieve these research goals.

This 1998 book describes the sampling and statistical methods used most often by behavioral ecologists and field biologists. Written by a biologist and two statisticians, it provides a rigorous discussion together with worked examples of statistical concepts and methods that are generally not covered in introductory courses, and which are consequently poorly understood and applied by field biologists. The first section reviews important issues such as defining the statistical population and the sampling plan when using non-random methods for sample selection, bias, interpretation of statistical tests, confidence intervals and multiple comparisons. After a detailed discussion of sampling methods and multiple regression, subsequent chapters discuss specialized problems such as pseudoreplication, and their solutions. It will quickly become the statistical handbook for all field biologists.

This book describes the sampling and statistical methods used most often by behavioral ecologists and field biologists. Written by a biologist and two statisticians, it provides a rigorous discussion together with worked examples of statistical concepts and methods that are generally not covered in introductory courses, and which are consequently poorly understood and applied by field biologists. The first section reviews important issues such as defining the statistical population and the sampling plan when using nonrandom methods for sample selection, bias, interpretation of statistical tests, confidence intervals, and multiple comparisons. After a detailed discussion of sampling methods and multiple regression, subsequent chapters discuss specialized problems such as pseudoreplication, and their solutions. This volume will quickly become the favorite statistical handbook for all field biologists.

This volume reveals the applications of statistics to a wide range of fields, from public policy to the sciences. It introduces new technology and pedagogical features.