An Introduction to Numerical Methods and Analysis

Analysis of Structures offers an original way of introducing engineering students to the subject of stress and deformation analysis of solid objects, and helps them become more familiar with how numerical methods such as the finite element method are used in industry. Eisley and Waas secure for the reader a thorough understanding of the basic numerical skills and insight into interpreting the results these methods can generate. Throughout the text, they include analytical development alongside the computational equivalent, providing the student with the understanding that is necessary to interpret and use the solutions that are obtained using software based on the finite element method. They then extend these methods to the analysis of solid and structural components that are used in modern aerospace, mechanical and civil engineering applications. Analysis of Structures is accompanied by a book companion website www.wiley.com/go/waas housing exercises and examples that use modern software which generates color contour plots of deformation and internal stress.It offers invaluable guidance and understanding to senior level and graduate students studying courses in stress and deformation analysis as part of aerospace, mechanical and civil engineering degrees as well as to practicing engineers who want to re-train or re-engineer their set of analysis tools for contemporary stress and deformation analysis of solids and structures. Provides a fresh, practical perspective to the teaching of structural analysis using numerical methods for obtaining answers to real engineering applications Proposes a new way of introducing students to the subject of stress and deformation analysis of solid objects that are used in a wide variety of contemporary engineering applications Casts axial, torsional and bending deformations of thin walled objects in a framework that is closely amenable to the methods by which modern stress analysis software operates.

Master the fundamentals of correspondence analysis with this illuminating resource An Introduction to Correspondence Analysis assists researchers in improving their familiarity with the concepts, terminology, and application of several variants of correspondence analysis. The accomplished academics and authors deliver a comprehensive and insightful treatment of the fundamentals of correspondence analysis, including the statistical and visual aspects of the subject. Written in three parts, the book begins by offering readers a description of two variants of correspondence analysis that can be applied to two-way contingency tables for nominal categories of variables. Part Two shifts the discussion to categories of ordinal variables and demonstrates how the ordered structure of these variables can be incorporated into a correspondence analysis. Part Three describes the analysis of multiple nominal categorical variables, including both multiple correspondence analysis and multi-way correspondence analysis. Readers will benefit from explanations of a wide variety of specific topics, for example: Simple correspondence analysis, including how to reduce multidimensional space, measuring symmetric associations with the Pearson Ratio, constructing low-dimensional displays, and detecting statistically significant points Non-symmetrical correspondence analysis, including quantifying asymmetric associations Simple ordinal correspondence analysis, including how to decompose the Pearson Residual for ordinal variables Multiple correspondence analysis, including crisp coding and the indicator matrix, the Burt Matrix, and stacking Multi-way correspondence analysis, including symmetric multi-way analysis Perfect for researchers who seek to improve their understanding of key concepts in the graphical analysis of categorical data, An Introduction to Correspondence Analysis will also assist readers already familiar with correspondence analysis who wish to review the theoretical and foundational underpinnings of crucial concepts.

Introduces both the fundamentals of time dependent differential equations and their numerical solutions Introduction to Numerical Methods for Time Dependent Differential Equations delves into the underlying mathematical theory needed to solve time dependent differential equations numerically. Written as a self-contained introduction, the book is divided into two parts to emphasize both ordinary differential equations (ODEs) and partial differential equations (PDEs). Beginning with ODEs and their approximations, the authors provide a crucial presentation of fundamental notions, such as the theory of scalar equations, finite difference approximations, and the Explicit Euler method. Next, a discussion on higher order approximations, implicit methods, multistep methods, Fourier interpolation, PDEs in one space dimension as well as their related systems is provided. Introduction to Numerical Methods for Time Dependent Differential Equations features: A step-by-step discussion of the procedures needed to prove the stability of difference approximations Multiple exercises throughout with select answers, providing readers with a practical guide to understanding the approximations of differential equations A simplified approach in a one space dimension Analytical theory for difference approximations that is particularly useful to clarify procedures Introduction to Numerical Methods for Time Dependent Differential Equations is an excellent textbook for upper-undergraduate courses in applied mathematics, engineering, and physics as well as a useful reference for physical scientists, engineers, numerical analysts, and mathematical modelers who use numerical experiments to test designs or predict and investigate phenomena from many disciplines.

This book is a quantitative text, which focuses on the real issues behind serious modeling and analysis of communications networks. The author covers all the necessary mathematics and theory in order for students to understand the tools that optimize computer networks today. Covers both classical (e.g. queueing theory) and modern (e.g. pricing) aspects of networking Integrates material on communication networks with material on modeling/analyzing and designing such networks Includes a Solution Manual

A comprehensive introduction to bootstrap methods in the R programming environment Bootstrap methods provide a powerful approach to statistical data analysis, as they have more general applications than standard parametric methods. An Introduction to Bootstrap Methods with Applications to R explores the practicality of this approach and successfully utilizes R to illustrate applications for the bootstrap and other resampling methods. This book provides a modern introduction to bootstrap methods for readers who do not have an extensive background in advanced mathematics. Emphasis throughout is on the use of bootstrap methods as an exploratory tool, including its value in variable selection and other modeling environments. The authors begin with a description of bootstrap methods and its relationship to other resampling methods, along with an overview of the wide variety of applications of the approach. Subsequent chapters offer coverage of improved confidence set estimation, estimation of error rates in discriminant analysis, and applications to a wide variety of hypothesis testing and estimation problems, including pharmaceutical, genomics, and economics. To inform readers on the limitations of the method, the book also exhibits counterexamples to the consistency of bootstrap methods. An introduction to R programming provides the needed preparation to work with the numerous exercises and applications presented throughout the book. A related website houses the book's R subroutines, and an extensive listing of references provides resources for further study. Discussing the topic at a remarkably practical and accessible level, An Introduction to Bootstrap Methods with Applications to R is an excellent book for introductory courses on bootstrap and resampling methods at the upper-undergraduate and graduate levels. It also serves as an insightful reference for practitioners working with data in engineering, medicine, and the social sciences who would like to acquire a basic understanding of bootstrap methods.

An engaging and accessible introduction to mathematical proof incorporating ideas from real analysis A mathematical proof is an inferential argument for a mathematical statement. Since the time of the ancient Greek mathematicians, the proof has been a cornerstone of the science of mathematics. The goal of this book is to help students learn to follow and understand the function and structure of mathematical proof and to produce proofs of their own. An Introduction to Proof through Real Analysis is based on course material developed and refined over thirty years by Professor Daniel J. Madden and was designed to function as a complete text for both first proofs and first analysis courses. Written in an engaging and accessible narrative style, this book systematically covers the basic techniques of proof writing, beginning with real numbers and progressing to logic, set theory, topology, and continuity. The book proceeds from natural numbers to rational numbers in a familiar way, and justifies the need for a rigorous definition of real numbers. The mathematical climax of the story it tells is the Intermediate Value Theorem, which justifies the notion that the real numbers are sufficient for solving all geometric problems. • Concentrates solely on designing proofs by placing instruction on proof writing on top of discussions of specific mathematical subjects • Departs from traditional guides to proofs by incorporating elements of both real analysis and algebraic representation • Written in an engaging narrative style to tell the story of proof and its meaning, function, and construction • Uses a particular mathematical idea as the focus of each type of proof presented • Developed from material that has been class-tested and fine-tuned over thirty years in university introductory courses An Introduction to Proof through Real Analysis is the ideal introductory text to proofs for second and third-year undergraduate mathematics students, especially those who have completed a calculus sequence, students learning real analysis for the first time, and those learning proofs for the first time. Daniel J. Madden, PhD, is an Associate Professor of Mathematics at The University of Arizona, Tucson, Arizona, USA. He has taught a junior level course introducing students to the idea of a rigorous proof based on real analysis almost every semester since 1990. Dr. Madden is the winner of the 2015 Southwest Section of the Mathematical Association of America Distinguished Teacher Award. Jason A. Aubrey, PhD, is Assistant Professor of Mathematics and Director, Mathematics Center of the University of Arizona.

Provides well-organized coverage of statistical analysis and applications in biology, kinesiology, and physical anthropology with comprehensive insights into the techniques and interpretations of R, SPSS®, Excel®, and Numbers® output An Introduction to Statistical Analysis in Research: With Applications in the Biological and Life Sciences develops a conceptual foundation in statistical analysis while providing readers with opportunities to practice these skills via research-based data sets in biology, kinesiology, and physical anthropology. Readers are provided with a detailed introduction and orientation to statistical analysis as well as practical examples to ensure a thorough understanding of the concepts and methodology. In addition, the book addresses not just the statistical concepts researchers should be familiar with, but also demonstrates their relevance to real-world research questions and how to perform them using easily available software packages including R, SPSS®, Excel®, and Numbers®. Specific emphasis is on the practical application of statistics in the biological and life sciences, while enhancing reader skills in identifying the research questions and testable hypotheses, determining the appropriate experimental methodology and statistical analyses, processing data, and reporting the research outcomes. In addition, this book: • Aims to develop readers’ skills including how to report research outcomes, determine the appropriate experimental methodology and statistical analysis, and identify the needed research questions and testable hypotheses • Includes pedagogical elements throughout that enhance the overall learning experience including case studies and tutorials, all in an effort to gain full comprehension of designing an experiment, considering biases and uncontrolled variables, analyzing data, and applying the appropriate statistical application with valid justification • Fills the gap between theoretically driven, mathematically heavy texts and introductory, step-by-step type books while preparing readers with the programming skills needed to carry out basic statistical tests, build support figures, and interpret the results • Provides a companion website that features related R, SPSS, Excel, and Numbers data sets, sample PowerPoint® lecture slides, end of the chapter review questions, software video tutorials that highlight basic statistical concepts, and a student workbook and instructor manual An Introduction to Statistical Analysis in Research: With Applications in the Biological and Life Sciences is an ideal textbook for upper-undergraduate and graduate-level courses in research methods, biostatistics, statistics, biology, kinesiology, sports science and medicine, health and physical education, medicine, and nutrition. The book is also appropriate as a reference for researchers and professionals in the fields of anthropology, sports research, sports science, and physical education. KATHLEEN F. WEAVER, PhD, is Associate Dean of Learning, Innovation, and Teaching and Professor in the Department of Biology at the University of La Verne. The author of numerous journal articles, she received her PhD in Ecology and Evolutionary Biology from the University of Colorado. VANESSA C. MORALES, BS, is Assistant Director of the Academic Success Center at the University of La Verne. SARAH L. DUNN, PhD, is Associate Professor in the Department of Kinesiology at the University of La Verne and is Director of Research and Sponsored Programs. She has authored numerous journal articles and received her PhD in Health and Exercise Science from the University of New South Wales. KANYA GODDE, PhD, is Assistant Professor in the Department of Anthropology and is Director/Chair of Institutional Review Board at the University of La Verne. The author of numerous j