William I. Notz – författare

Statistics as a Liberal DisciplineThere are books on statistical theory, and there books on statistical methods. This is neither. Now available with Macmillan's online learning platform Achieve, Statistics: Concepts and Controversies (SCC) is a book on statistical ideas and statistical reasoning and on their relevance to public policy and to the human sciences from medicine to sociology.

The text includes many elementary graphical and numerical techniques to give flesh to the ideas and muscle to the reasoning. Students learn to think about data by working with data. We have not, however, allowed technique to dominate concepts. The text's intention is to teach verbally rather than algebraically, to invite discussion and even argument rather than mere computation, though some computation remains essential. The coverage is considerably broader than one might traditionally cover in a one-term course, as the table of contents reveals. In the spirit of general education, the authors have preferred breadth to detail.

Achieve for Statistics: Concepts and Controversies connects the 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.

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