Nonlinear Time Series Analysis with R provides a practical guide to emerging empirical techniques allowing practitioners to diagnose whether highly fluctuating and random appearing data are most likely driven by random or deterministic dynamic forces. It joins the chorus of voices recommending 'getting to know your data' as an essential preliminary evidentiary step in modelling. Time series are often highly fluctuating with a random appearance. Observed volatility is commonly attributed to exogenous random shocks to stable real-world systems. However, breakthroughs in nonlinear dynamics raise another possibility: highly complex dynamics can emerge endogenously from astoundingly parsimonious deterministic nonlinear models. Nonlinear Time Series Analysis (NLTS) is a collection of empirical tools designed to aid practitioners detect whether stochastic or deterministic dynamics most likely drive observed complexity. Practitioners become 'data detectives' accumulating hard empirical evidence supporting their modelling approach. This book is targeted to professionals and graduate students in engineering and the biophysical and social sciences. Its major objectives are to help non-mathematicians - with limited knowledge of nonlinear dynamics - to become operational in NLTS; and in this way to pave the way for NLTS to be adopted in the conventional empirical toolbox and core coursework of the targeted disciplines. Consistent with modern trends in university instruction, the book makes readers active learners with hands-on computer experiments in R code directing them through NLTS methods and helping them understand the underlying logic (please see www.marco.bittelli.com). The computer code is explained in detail so that readers can adjust it for use in their own work. The book also provides readers with an explicit framework - condensed from sound empirical practices recommended in the literature - that details a step-by-step procedure for applying NLTS in real-world data diagnostics.

This book contains an extensive illustration of use of finite difference method in solving the boundary value problem numerically. A wide class of differential equations has been numerically solved in this book. Starting with differential equations of elementary functions like hyperbolic, sine and cosine, we have solved those of special functions like Hermite, Laguerre and Legendre. Those of Airy function, of stationary localised wavepacket, of the quantum mechanical problem of a particle in a 1D box, and the polar equation of motion under gravitational interaction have also been solved. Mathematica 6.0 has been used to solve the system of linear equations that we encountered and to plot the numerical data. Comparison with known analytic solutions showed nearly perfect agreement in every case. On reading this book, readers will become adept in using the method.

Uncertainties in GPS Positioning: A Mathematical Discourse describes the calculations performed by a GPS receiver and the problems associated with ensuring that the derived location is a close match to the actual location. Inaccuracies in calculating a location can have serious repercussions, so this book is a timely source for information on this rapidly evolving technology.

Mathematics for Neuroscientists, Second Edition, presents a comprehensive introduction to mathematical and computational methods used in neuroscience to describe and model neural components of the brain from ion channels to single neurons, neural networks and their relation to behavior. The book contains more than 200 figures generated using Matlab code available to the student and scholar. Mathematical concepts are introduced hand in hand with neuroscience, emphasizing the connection between experimental results and theory.

First Semester Calculus for Students of Mathematics and Related Disciplines equips students with a strong working knowledge of the fundamental principles of calculus, providing an engaging and accessible entry point into this critical field of study. It prepares students for more advanced courses in calculus and also helps them understand how to apply basic principles of calculus to solve problems within a wide range of disciplines, including business, biology, engineering, science, liberal arts and, of course, mathematics. The text employs rigorous treatment of early calculus topics and detailed explanations to facilitate deeper understanding of later material. Over the course of five chapters, students learn about symbolic logic, continuity and limits, derivatives, antiderivatives, and applications of each. Throughout, students are provided with rich guidance and copious opportunities to deepen their personal understanding of the subject matter. In the second edition, a more efficient layout better highlights major theorems and definitions. Additionally, over 300 new exercises have been added to further aid student learning. Highly readable and innovative, yet pedagogically solid and very applicable, First Semester Calculus for Students of Mathematics and Related Disciplines is an ideal resource for a variety of courses that apply concepts of calculus to solve mathematical and real-world problems.

INTRODUCTION TO LINEAR REGRESSION ANALYSIS A comprehensive and current introduction to the fundamentals of regression analysis Introduction to Linear Regression Analysis, 6th Edition is the most comprehensive, fulsome, and current examination of the foundations of linear regression analysis. Fully updated in this new sixth edition, the distinguished authors have included new material on generalized regression techniques and new examples to help the reader understand retain the concepts taught in the book. The new edition focuses on four key areas of improvement over the fifth edition: New exercises and data sets New material on generalized regression techniques The inclusion of JMP software in key areas Carefully condensing the text where possible Introduction to Linear Regression Analysis skillfully blends theory and application in both the conventional and less common uses of regression analysis in today's cutting-edge scientific research. The text equips readers to understand the basic principles needed to apply regression model-building techniques in various fields of study, including engineering, management, and the health sciences.

For a physicist, "noise" is not just about sounds, but refers to any random physical process that blurs measurements, and in so doing stands in the way of scientific knowledge. This book deals with the most common types of noise, their properties, and some of their unexpected virtues. The text explains the most useful mathematical concepts related to noise. Finally, the book aims at making this subject more widely known and to stimulate the interest for its study in young physicists.