SINGLE VARIABLE CALCULUS: EARLY TRANSCENDENTALS, Metric, 9th Edition, provides you with the strongest foundation for a STEM future. James Stewart’s Calculus, Metric series is the top-seller in the world because of its problem-solving focus, mathematical precision and accuracy, and outstanding examples and problem sets. Selected and mentored by Stewart, Daniel Clegg and Saleem Watson continue his legacy and their careful refinements retain Stewart’s clarity of exposition and make the 9th e...

Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan, Second Edition provides an accessible approach for conducting Bayesian data analysis, as material is explained clearly with concrete examples. Included are step-by-step instructions on how to carry out Bayesian data analyses in the popular and free software R and WinBugs, as well as new programs in JAGS and Stan. The new programs are designed to be much easier to use than the scripts in the first edition.

In particular, there ar...

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.

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.