Numerical Approximation Methods

An introductory review of electric energy using examples, numerical calculations and theory to describe the basic elements of the field. Coverage includes electric infrastructure, the grid, system operations, renewable energy, alternating current circuits, three-phase power, power electronics, blackouts and future power systems.

Statistics Explained, now in its fully revised Fourth Edition, is for students and researchers who wish to understand the statistical analyses used to analyse quantitative (numerical) research data in

This book demonstrates how to formulate the equations of mechanical systems. Providing methods of analysis of complex mechanical systems, the book has a clear focus on efficiency, equipping the reader with knowledge of algorithms that provide accurate results in reduced simulation time.The book uses Kane's method due to its efficiency, and the simple resulting equations it produces in comparison to other methods and extends it with algorithms such as order-nKane's method compensates for the errors of premature linearization, which are often inherent within vibrations modes found in a great deal of public domain softwareDescribing how to build mathematical models of multibody systems with elastic components, the book applies this to systems such as construction cranes, trailers, helicopters, spacecraft, tethered satellites, and underwater vehiclesIt also looks at topics such as vibration, rocket dynamics, simulation of beams, deflection, and matrix formulationFlexible Multibody Dynamics

Valuable resource for researchers and postgraduate students from statistics, biostatistics, and other fields. It could be used as a textbook for a course on model-based clustering methods, and as a su

The purpose of this book is to provide an account of survival analysis. The authors intend to accomplish it from two fronts: (i) methods in survival analysis developed over the past two decades and ex

Designed as a textbook for a one or two-term introduction to mathematical statistics for students training to become data scientists, Foundations of Statistics for Data Scientists: With R and Python is an in-depth presentation of the topics in statistical science with which any data scientist should be familiar, including probability distributions, descriptive and inferential statistical methods, and linear modelling. The book assumes knowledge of basic calculus, so the presentation can focus on 'why it works' as well as 'how to do it.' Compared to traditional "mathematical statistics" textbooks, however, the book has less emphasis on probability theory and more emphasis on using software to implement statistical methods and to conduct simulations to illustrate key concepts. All statistical analyses in the book use R software, with an appendix showing the same analyses with Python.The book also introduces modern topics that do not normally appear in mathematical statistics texts but ar

Cure Models: Methods, Applications and Implementation is the first book in the last 25 years that provides a comprehensive and systematic introduction to the basics of modern cure models, including es

This timely and original guide to qualitative internet research offers thought-provoking case studies and debates on the methods and theories surrounding contemporary internet research.