Through examples and analogies, Computational Thinking for the Modern Problem Solver introduces computational thinking as part of an introductory computing course and shows how computer science concepts are applicable to other fields. It keeps the material accessible and relevant to noncomputer science majors. With numerous color figures, this classroom-tested book focuses on both foundational computer science concepts and engineering topics.

It covers abstraction, algorithms, logic, graph th...

The second edition of a bestselling textbook, Using R for Introductory Statistics guides students through the basics of R, helping them overcome the sometimes steep learning curve. The author does this by breaking the material down into small, task-oriented steps. The second edition maintains the features that made the first edition so popular, while updating data, examples, and changes to R in line with the current version.

With a more specific focus than the all-encompassing textbook, each title in the Foundations of Psychology series enables students who are new to psychology to get to grips with a key area of psychological research, while also developing an understanding of basic concepts, debates, and research methodologies. In this book Diana Jackson-Dwyer presents an introductory survey of classic and recent research on relationships and the theories that underpin them. The book starts with a brief overvie...

Offers a unified discussion of generalized linear models and correlated data methods. Provides information suitable for graduate non-statistics majors or advanced undergraduate statistics majors. Includes case studies with real data. Offers material on R at the end of each chapter. Provides a solutions manual.

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage app...