The author's goal for the book is that it's clearly written, could be read by a calculus student and would motivate them to engage in the material and learn more. Moreover, to create a text in which exposition, graphics, and layout would work together to enhance all facets of a student's calculus experience. They paid special attention to certain aspects of the text:1.

Clear, accessible exposition that anticipates and addresses student difficulties.2. Layout and figures that communicate the f...

Designed for all teachers, this book provides a wealth of materials and resources to support the needs of learners, aged 6 to 14 years, who have difficulty with maths and number.

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...

Matrixes CAN be used for the calculation of Magic Squares. Matrixes can be used to ADAPT magic squares. About the use of magic squares and also something about magic squares freemasony has overlooked.

Matrixen KUNNEN worden gebruikt om Magische Getalsvierkanten te berekenen. Matrixen kunnen gebruikt worden om deze Magische Vierkanten toe te passen. Het gaat over het gebruik van Magische Vierkanten en er is ook nog iets dat de vrijmetselarij m.b.t. Magische Vierkanten over het hoofd heeft gez...

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...

Note: You are purchasing a standalone product; MyLab Math does not come packaged with this content. Students, if interested in purchasing this title with MyLab Math, ask your instructor for the correct package ISBN and Course ID. Instructors, contact your Pearson representative for more information.

Proven in North America and abroad, this classic text has earned a reputation for excellent accuracy and mathematical rigour. Previous editions have been praised for providing complete and precise...

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...

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.