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Difference Equations Theory Applications and Advanced Topics Third Edition

Difference Equations Theory Applications and Advanced Topics Third Edition

Difference Equations: Theory Applications and Advanced Topics Third Edition provides a broad introduction to the mathematics of difference equations and some of their applications. Many worked examples illustrate how to calculate both exact and approximate solutions to special classes of difference equations. Along with adding several advanced topics this edition continues to cover general linear first- second- and n-th order difference equations; nonlinear equations that may be reduced to linear equations; and partial difference equations. New to the Third Edition New chapter on special topics including discrete Cauchy–Euler equations; gamma beta and digamma functions; Lambert W-function; Euler polynomials; functional equations; and exact discretizations of differential equations New chapter on the application of difference equations to complex problems arising in the mathematical modeling of phenomena in engineering and the natural and social sciences Additional problems in all chapters Expanded bibliography to include recently published texts related to the subject of difference equations Suitable for self-study or as the main text for courses on difference equations this book helps readers understand the fundamental concepts and procedures of difference equations. It uses an informal presentation style avoiding the minutia of detailed proofs and formal explanations. | Difference Equations Theory Applications and Advanced Topics Third Edition

GBP 59.99
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Statistical Process Control For Quality Improvement- Hardcover Version

Statistical Process Control For Quality Improvement- Hardcover Version

While the common practice of Quality Assurance aims to prevent bad units from being shipped beyond some allowable proportion statistical process control (SPC) ensures that bad units are not created in the first place. Its philosophy of continuous quality improvement to a great extent responsible for the success of Japanese manufacturing is rooted in a paradigm as process-oriented as physics yet produces a friendly and fulfilling work environment. The first edition of this groundbreaking text showed that the SPC paradigm of W. Edwards Deming was not at all the same as the Quality Control paradigm that has dominated American manufacturing since World War II. Statistical Process Control: The Deming Paradigm and Beyond Second Edition reveals even more of Deming's philosophy and provides more techniques for use at the managerial level. Explaining that CEOs and service industries need SPC at least as much as production managers it offers precise methods and guidelines for their use. Using the practical experience of the authors working both in America and Europe this book shows how SPC can be implemented in a variety of settings from health care to manufacturing. It also provides you with the necessary technical background through mathematical and statistical appendices. According to the authors companies with managers who have adopted the philosophy of statistical process control tend to survive. Those with managers who do not are likely to fail. In which group will your company be? | Statistical Process Control For Quality Improvement- Hardcover Version

GBP 44.99
1

Medical Risk Prediction Models With Ties to Machine Learning

Medical Risk Prediction Models With Ties to Machine Learning

Medical Risk Prediction Models: With Ties to Machine Learning is a hands-on book for clinicians epidemiologists and professional statisticians who need to make or evaluate a statistical prediction model based on data. The subject of the book is the patient’s individualized probability of a medical event within a given time horizon. Gerds and Kattan describe the mathematical details of making and evaluating a statistical prediction model in a highly pedagogical manner while avoiding mathematical notation. Read this book when you are in doubt about whether a Cox regression model predicts better than a random survival forest. Features: All you need to know to correctly make an online risk calculator from scratch Discrimination calibration and predictive performance with censored data and competing risks R-code and illustrative examples Interpretation of prediction performance via benchmarks Comparison and combination of rival modeling strategies via cross-validation Thomas A. Gerds is a professor at the Biostatistics Unit at the University of Copenhagen and is affiliated with the Danish Heart Foundation. He is the author of several R-packages on CRAN and has taught statistics courses to non-statisticians for many years. Michael W. Kattan is a highly cited author and Chair of the Department of Quantitative Health Sciences at Cleveland Clinic. He is a Fellow of the American Statistical Association and has received two awards from the Society for Medical Decision Making: the Eugene L. Saenger Award for Distinguished Service and the John M. Eisenberg Award for Practical Application of Medical Decision-Making Research. | Medical Risk Prediction Models With Ties to Machine Learning

GBP 48.99
1

Multilevel Modeling Using R

Multilevel Modeling Using R

Like its bestselling predecessor Multilevel Modeling Using R Second Edition provides the reader with a helpful guide to conducting multilevel data modeling using the R software environment. After reviewing standard linear models the authors present the basics of multilevel models and explain how to fit these models using R. They then show how to employ multilevel modeling with longitudinal data and demonstrate the valuable graphical options in R. The book also describes models for categorical dependent variables in both single level and multilevel data. New in the Second Edition: Features the use of lmer (instead of lme) and including the most up to date approaches for obtaining confidence intervals for the model parameters. Discusses measures of R2 (the squared multiple correlation coefficient) and overall model fit. Adds a chapter on nonparametric and robust approaches to estimating multilevel models including rank based heavy tailed distributions and the multilevel lasso. Includes a new chapter on multivariate multilevel models. Presents new sections on micro-macro models and multilevel generalized additive models. This thoroughly updated revision gives the reader state-of-the-art tools to launch their own investigations in multilevel modeling and gain insight into their research. About the Authors: W. Holmes Finch is the George and Frances Ball Distinguished Professor of Educational Psychology at Ball State University. Jocelyn E. Bolin is a Professor in the Department of Educational Psychology at Ball State University. Ken Kelley is the Edward F. Sorin Society Professor of IT Analytics and Operations and the Associate Dean for Faculty and Research for the Mendoza College of Business at the University of Notre Dame.

GBP 56.99
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Introduction to Real Analysis

Introduction to Real Analysis

This classic textbook has been used successfully by instructors and students for nearly three decades. This timely new edition offers minimal yet notable changes while retaining all the elements presentation and accessible exposition of previous editions. A list of updates is found in the Preface to this edition. This text is based on the author’s experience in teaching graduate courses and the minimal requirements for successful graduate study. The text is understandable to the typical student enrolled in the course taking into consideration the variations in abilities background and motivation. Chapters one through six have been written to be accessible to the average student w hile at the same time challenging the more talented student through the exercises. Chapters seven through ten assume the students have achieved some level of expertise in the subject. In these chapters the theorems examples and exercises require greater sophistication and mathematical maturity for full understanding. In addition to the standard topics the text includes topics that are not always included in comparable texts. Chapter 6 contains a section on the Riemann-Stieltjes integral and a proof of Lebesgue’s t heorem providing necessary and sufficient conditions for Riemann integrability. Chapter 7 also includes a section on square summable sequences and a brief introduction to normed linear spaces. C hapter 8 contains a proof of the Weierstrass approximation theorem using the method of aapproximate identities. The inclusion of Fourier series in the text allows the student to gain some exposure to this important subject. The final chapter includes a detailed treatment of Lebesgue measure and the Lebesgue integral using inner and outer measure. The exercises at the end of each section reinforce the concepts. Notes provide historical comments or discuss additional topics. | Introduction to Real Analysis

GBP 46.99
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