With over a million users around the world, the Mathematica software system created by Stephen Wolfram has defined the direction of technical computing for the past decade. The enhanced text and hypertext processing and state-of-the-art numerical computation features ensure that Mathematica 4 takes scientific computing into the next century. New to this version: visual tour of key features, practical tutorial introduction, full descriptions of 1100 built-in functions, a thousand illustrative examples, easy-to-follow descriptive tables, essays highlighting key concepts, examples of data import and export, award-winning gallery of Mathematica graphics, gallery of mathematical typesetting, dictionary of 700 special characters, a complete guide to the MathLink API, notes on internal implementation, and an index with over 10,000 entries copublished with Wolfram Media.

ENGINEERING APPLICATIONS A comprehensive text on the fundamental principles of mechanical engineering Engineering Applications presents the fundamental principles and applications of the statics and mechanics of materials in complex mechanical systems design. Using MATLAB to help solve problems with numerical and analytical calculations, authors and noted experts on the topic Mihai Dupac and Dan B. Marghitu offer an understanding of the static behaviour of engineering structures and components while considering the mechanics of materials knowledge as the most important part of their design. The authors explore the concepts, derivations, and interpretations of general principles and discuss the creation of mathematical models and the formulation of mathematical equations. This practical text also highlights the solutions of problems solved analytically and numerically using MATLAB. The figures generated with MATLAB reinforce visual learning for students and professionals as they study the programs. This important text: Shows how mechanical principles are applied to engineering design Covers basic material with both mathematical and physical insight Provides an understanding of classical mechanical principles Offers problem solutions using MATLAB Reinforces learning using visual and computational techniques Written for students and professional mechanical engineers, Engineering Applications helpshone reasoning skills in order to interpret data and generate mathematical equations, offering different methods of solving them for evaluating and designing engineering systems.

This book will help readers understand fundamental and advanced statistical models and deep learning models for robust speaker recognition and domain adaptation. This useful toolkit enables readers to apply machine learning techniques to address practical issues, such as robustness under adverse acoustic environments and domain mismatch, when deploying speaker recognition systems. Presenting state-of-the-art machine learning techniques for speaker recognition and featuring a range of probabilistic models, learning algorithms, case studies, and new trends and directions for speaker recognition based on modern machine learning and deep learning, this is the perfect resource for graduates, researchers, practitioners and engineers in electrical engineering, computer science and applied mathematics.

The interaction between computers and mathematics is becoming more and more important at all levels as computers become more sophisticated. This book shows how simple programs can be used to do significant mathematics. The purpose of this book is to give those with some mathematical background a wealth of material with which to appreciate both the power of the microcomputer and its relevance to the study of mathematics. The authors cover topics such as number theory, approximate solutions, differential equations and iterative processes, with each chapter self contained. Many exercises and projects are included giving ready-made material for demonstrating mathematical ideas. Only fundamental knowledge of mathematics is assumed and programming is restricted to "basic BASIC" which will be understood by any microcomputer. The book may be used as a textbook for algorithmic mathematics at several levels, since all the topics covered appear in any undergraduate mathematics course.

Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. Need to Learn MATLAB? Problem SOLVED! Get started using MATLAB right away with help from this hands-on guide. MATLAB Demystified offers an effective and enlightening method for learning how to get the most out this powerful computational mathematics tool. Using an easy-to-follow format, this book explains the basics of MATLAB up front. You'll find out how to plot functions, solve algebraic equations, and compute integrals. You'll also learn how to solve differential equations, generate numerical solutions of ODEs, and work with special functions. Packed with hundreds of sample equations and explained solutions, and featuring end-of-chapter quizzes and a final exam, this book will teach you MATLAB essentials in no time at all. This self-teaching guide offers: The quickest way to get up and running on MATLAB Hundreds of worked examples with solutions Coverage of MATLAB 7 A quiz at the end of each chapter to reinforce learning and pinpoint weaknesses A final exam at the end of the book A time-saving approach to performing better on homework or on the job Simple enough for a beginner, but challenging enough for an advanced user, MATLAB Demystified is your shortcut to computational precision.

What are the models used in phylogenetic analysis and what exactly is involved in Bayesian evolutionary analysis using Markov chain Monte Carlo (MCMC) methods? How can you choose and apply these models, which parameterisations and priors make sense, and how can you diagnose Bayesian MCMC when things go wrong? These are just a few of the questions answered in this comprehensive overview of Bayesian approaches to phylogenetics. This practical guide: * Addresses the theoretical aspects of the field * Advises on how to prepare and perform phylogenetic analysis * Helps with interpreting analyses and visualisation of phylogenies * Describes the software architecture * Helps developing BEAST 2.2 extensions to allow these models to be extended further. With an accompanying website providing example files and tutorials (http://beast2.org/), this one-stop reference to applying the latest phylogenetic models in BEAST 2 will provide essential guidance for all users - from those using phylogenetic tools, to computational biologists and Bayesian statisticians.

This textbook teaches crucial statistical methods to answer research questions using a unique range of statistical software programs, including MINITAB and R. This textbook is developed for undergraduate students in agriculture, nursing, biology and biomedical research. Graduate students will also find it to be a useful way to refresh their statistics skills and to reference software options. The unique combination of examplesis approached using MINITAB and R for their individual strengths. Subjects covered include among others data description, probability distributions, experimental design, regression analysis, randomized design and biological assay. Unlike other biostatistics textbooks, this text also includes outliers, influential observations in regression and an introduction to survival analysis. Material is taken from the author's extensive teaching and research in Africa, USA and the UK. Sample problems, references and electronic supplementary material accompany each chapter."

To date, statistics has tended to be neatly divided into two theoretical approaches or frameworks: frequentist (or classical) and Bayesian. Scientists typically choose the statistical framework to analyse their data depending on the nature and complexity of the problem, and based on their personal views and prior training on probability and uncertainty. Although textbooks and courses should reflect and anticipate this dual reality, they rarely do so. This accessible textbook explains, discusses, and applies both the frequentist and Bayesian theoretical frameworks to fit the different types of statistical models that allow an analysis of the types of data most commonly gathered by life scientists. It presents the material in an informal, approachable, and progressive manner suitable for readers with only a basic knowledge of calculus and statistics. Statistical Modeling with R is aimed at senior undergraduate and graduate students, professional researchers, and practitioners throughout the life sciences, seeking to strengthen their understanding of quantitative methods and to apply them successfully to real world scenarios, whether in the fields of ecology, evolution, environmental studies, or computational biology.

For first-year or introductory Engineering courses Best-selling author Delores Etter provides an up-to-date introduction to MATLAB. Using a consistent five-step problem-solving methodology, Etter describes the computational and visualization capabilities of MATLAB and illustrates the problem solving process through a variety of engineering examples and applications. Teaching and Learning Experience This program will provide a better teaching and learning experience-for you and your students. It will help: *Customize your Course with ESource: Instructors can adopt this title as is, or use the ESource website to select the chapters they need, in the sequence they want. *Present a Consistent Methodology for Solving Engineering Problems: Chapter 1 introduces a five-step process for solving engineering problems using the computer *Describe the Exceptional Computational and Visualization Capabilities of MATLAB: Students will gain a clear understanding of how to use MATLAB.*Illustrate the Problem-solving Process through a Variety of Engineering Examples and Applications: Numerous examples emphasize the creation of readable and simple solutions to develop and reinforce problem-solving skills. *Keep your Course Current with Discussion of the Latest Technologies: The discussions, screen captures, examples, and problem solutions have been updated to reflect MATLAB Version 8.2, R2013b.

We have sold 4300 copies worldwide of the first edition (1999).

This new edition contains five completely new chapters covering new developments.

This volume contains the papers presented at the 8th International Conf- ence on Independent Component Analysis (ICA) and Source Separation held in Paraty, Brazil, March 15-18, 2009. This year's event resulted from scienti?c collaborations between a team of researchers from ?ve di?erent Brazilian u- versities and received the support of the Brazilian Telecommunications Society (SBrT) as well as the ?nancial sponsorship of CNPq, CAPES and FAPERJ. Independent component analysis and signal separation is one of the most - citing current areas of research in statistical signal processing and unsupervised machine learning. The area has received attention from severalresearchcom- nities including machine learning, neural networks, statistical signal processing and Bayesian modeling. Independent component analysis and signal separation has applications at the intersection of many science and engineering disciplines concerned with understanding and extracting useful information from data as diverse as neuronal activity and brain images, bioinformatics, communications, the World Wide Web, audio, video, sensor signals, and time series.

Data Presentation with SPSS Explained provides students with all the information they need to conduct small scale analysis of research projects using SPSS and present their results appropriately in their reports. Quantitative data can be collected in the form of a questionnaire, survey or experimental study. This book focuses on presenting this data clearly, in the form of tables and graphs, along with creating basic summary statistics. Data Presentation with SPSS Explained uses an example survey that is clearly explained step-by-step throughout the book. This allows readers to follow the procedures, and easily apply each step in the process to their own research and findings. No prior knowledge of statistics or SPSS is assumed, and everything in the book is carefully explained in a helpful and user-friendly way using worked examples. This book is the perfect companion for students from a range of disciplines including psychology, business, communication, education, health, humanities, marketing and nursing - many of whom are unaware that this extremely helpful program is available at their institution for their use.

"Modeling with Data" fully explains how to execute computationally intensive analyses on very large data sets, showing readers how to determine the best methods for solving a variety of different problems, how to create and debug statistical models, and how to run an analysis and evaluate the results.

Ben Klemens introduces a set of open and unlimited tools, and uses them to demonstrate data management, analysis, and simulation techniques essential for dealing with large data sets and computationally intensive procedures. He then demonstrates how to easily apply these tools to the many threads of statistical technique, including classical, Bayesian, maximum likelihood, and Monte Carlo methods. Klemens's accessible survey describes these models in a unified and nontraditional manner, providing alternative ways of looking at statistical concepts that often befuddle students. The book includes nearly one hundred sample programs of all kinds. Links to these programs will be available on this page at a later date.

"Modeling with Data" will interest anyone looking for a comprehensive guide to these powerful statistical tools, including researchers and graduate students in the social sciences, biology, engineering, economics, and applied mathematics.

This innovative approach to teaching the finite element method blends theoretical, textbook-based learning with practical application using online and video resources. This hybrid teaching package features computational software such as MATLAB (R), and tutorials presenting software applications such as PTC Creo Parametric, ANSYS APDL, ANSYS Workbench and SolidWorks, complete with detailed annotations and instructions so students can confidently develop hands-on experience. Suitable for senior undergraduate and graduate level classes, students will transition seamlessly between mathematical models and practical commercial software problems, empowering them to advance from basic differential equations to industry-standard modelling and analysis. Complete with over 120 end-of chapter problems and over 200 illustrations, this accessible reference will equip students with the tools they need to succeed in the workplace.

Statistical methods are a key tool for all scientists working with data, but learning the basics continues to challenge successive generations of students. This accessible textbook provides an up-to-date introduction to the classical techniques and modern extensions of linear model analysis-one of the most useful approaches for investigating scientific data in the life and environmental sciences. While some of the foundational analyses (e.g. t tests, regression, ANOVA) are as useful now as ever, best practice moves on and there are many new general developments that offer great potential. The book emphasizes an estimation-based approach that takes account of recent criticisms of over-use of probability values and introduces the alternative approach that uses information criteria. This new edition includes the latest advances in R and related software and has been thoroughly "road-tested" over the last decade to create a proven textbook that teaches linear and generalized linear model analysis to students of ecology, evolution, and environmental studies (including worked analyses of data sets relevant to all three disciplines). While R is used throughout, the focus remains firmly on statistical analysis. The New Statistics with R is suitable for senior undergraduate and graduate students, professional researchers, and practitioners in the fields of ecology, evolution and environmental studies.

Experiments, surveys, measurements, and observations all generate data. These data can provide useful insights for solving problems, guiding decisions, and formulating strategy. Progressing from relatively unprocessed data to insight, and doing so efficiently, reliably, and confidently, does not come easily, and yet gaining insights from data is a fundamental skill for science as well as many other fields and often overlooked in most textbooks of statistics and data analysis. This accessible and engaging book provides readers with the knowledge, experience, and confidence to work with data and unlock essential information (insights) from data summaries and visualisations. Based on a proven and successful undergraduate course structure, it charts the journey from initial question, through data preparation, import, cleaning, tidying, checking, double-checking, manipulation, and final visualization. These basic skills are sufficient to gain useful insights from data without the need for any statistics; there is enough to learn about even before delving into that world! The book focuses on gaining insights from data via visualisations and summaries. The journey from raw data to insights is clearly illustrated by means of a comprehensive Workflow Demonstration in the book featuring data collected in a real-life study and applicable to many types of question, study, and data. Along the way, readers discover how to efficiently and intuitively use R, RStudio, and tidyverse software, learning from the detailed descriptions of each step in the instructional journey to progress from the raw data to creating elegant and informative visualisations that reveal answers to the initial questions posed. There are an additional three demonstrations online! Insights from Data with R is suitable for undergraduate students and their instructors in the life and environmental sciences seeking to harness the power of R, RStudio, and tidyverse software to master the valuable and prerequisite skills of working with and gaining insights from data.

Experiments, surveys, measurements, and observations all generate data. These data can provide useful insights for solving problems, guiding decisions, and formulating strategy. Progressing from relatively unprocessed data to insight, and doing so efficiently, reliably, and confidently, does not come easily, and yet gaining insights from data is a fundamental skill for science as well as many other fields and often overlooked in most textbooks of statistics and data analysis. This accessible and engaging book provides readers with the knowledge, experience, and confidence to work with data and unlock essential information (insights) from data summaries and visualisations. Based on a proven and successful undergraduate course structure, it charts the journey from initial question, through data preparation, import, cleaning, tidying, checking, double-checking, manipulation, and final visualization. These basic skills are sufficient to gain useful insights from data without the need for any statistics; there is enough to learn about even before delving into that world! The book focuses on gaining insights from data via visualisations and summaries. The journey from raw data to insights is clearly illustrated by means of a comprehensive Workflow Demonstration in the book featuring data collected in a real-life study and applicable to many types of question, study, and data. Along the way, readers discover how to efficiently and intuitively use R, RStudio, and tidyverse software, learning from the detailed descriptions of each step in the instructional journey to progress from the raw data to creating elegant and informative visualisations that reveal answers to the initial questions posed. There are an additional three demonstrations online! Insights from Data with R is suitable for undergraduate students and their instructors in the life and environmental sciences seeking to harness the power of R, RStudio, and tidyverse software to master the valuable and prerequisite skills of working with and gaining insights from data.

This practical guide is designed for students and researchers with an existing knowledge of R who wish to learn how to apply it in an epidemiological context and exploit its versatility. It also serves as a broader introduction to the quantitative aspects of modern practical epidemiology. The standard tools used in epidemiology are described and the practical use of R for these is clearly explained and laid out. R code examples, many with output, are embedded throughout the text. The entire code is also available on the companion website so that readers can reproduce all the results and graphs featured in the book. Epidemiology with R is an advanced textbook suitable for senior undergraduate and graduate students, professional researchers, and practitioners in the fields of human and non-human epidemiology, public health, veterinary science, and biostatistics.

This practical guide is designed for students and researchers with an existing knowledge of R who wish to learn how to apply it in an epidemiological context and exploit its versatility. It also serves as a broader introduction to the quantitative aspects of modern practical epidemiology. The standard tools used in epidemiology are described and the practical use of R for these is clearly explained and laid out. R code examples, many with output, are embedded throughout the text. The entire code is also available on the companion website so that readers can reproduce all the results and graphs featured in the book. Epidemiology with R is an advanced textbook suitable for senior undergraduate and graduate students, professional researchers, and practitioners in the fields of human and non-human epidemiology, public health, veterinary science, and biostatistics.

Previous editions of this popular textbook offered an accessible and practical introduction to numerical analysis. An Introduction to Numerical Methods: A MATLAB (R) Approach, Fourth Edition continues to present a wide range of useful and important algorithms for scientific and engineering applications. The authors use MATLAB to illustrate each numerical method, providing full details of the computed results so that the main steps are easily visualized and interpreted. This edition also includes a new chapter on Dynamical Systems and Chaos. Features Covers the most common numerical methods encountered in science and engineering Illustrates the methods using MATLAB Presents numerous examples and exercises, with selected answers at the back of the book

Thoroughly updated with improved pedagogy, the fifth edition of this classic textbook continues to provide students with a clear and comprehensive introduction the fundamentals of the finite element method. New features include enhanced coverage of introductory topics in the context of simple 1D problems, providing students with a solid base from which to advance to 2D and 3D problems; expanded coverage of more advanced concepts, to reinforce students' understanding; over 30 additional solved problems; and downloadable MATLAB, Python, C, Javascript, Fortran and Excel VBA code packages, providing students with hands-on experience, and preparing them for commercial software. Accompanied by online solutions for instructors, this is the definitive text for senior undergraduate and graduate students studying a first course in the finite element method and finite element analysis, and for professional engineers keen to shore up their understanding of finite element fundamentals.

This book provides a comprehensive introduction to performing meta-analysis using the statistical software R. It is intended for quantitative researchers and students in the medical and social sciences who wish to learn how to perform meta-analysis with R. As such, the book introduces the key concepts and models used in meta-analysis. It also includes chapters on the following advanced topics: publication bias and small study effects; missing data; multivariate meta-analysis, network meta-analysis; and meta-analysis of diagnostic studies.

DESCRIPTION R is a powerful language for statistical computing and graphics that can handle virtually any data-crunching task. It runs on all important platforms and provides thousands of useful specialized modules and utilities. This makes R a great way to get meaningful information from mountains of raw data. R in Action, Second Edition is language tutorial focused on practical problems. Written by a research methodologist, it takes a direct and modular approach to quickly give readers the information they need to produce useful results. Focusing on realistic data analyses and a comprehensive integration of graphics, it follows the steps that real data analysts use to acquire their data, get it into shape, analyze it, and produce meaningful results that they can provide to clients. RETAIL SELLING POINTS Step-by-step guide to practical data analysis Gives quick access to the R language Written by seasoned researcher Uses over 130 graphics AUDIENCE This book is for social scientists, medical and pharmaceutical researchers, professors and students in quantitative fields, data analysts, data mining specialists, and statistical programmers who have a sense of what they want to do, but not how to do it in R. ABOUT THE TECHNOLOGY R is a powerful language for statistical computing and graphics that can handle virtually any data-crunching task. It runs on all important platforms and provides thousands of useful specialized modules and

R is a statistical computer program used and developed by statisticians around the world. It is probably the leading statistical program, at least among statisticians, and it is freely available. This book is intended for the newcomer who wants to do statistical analysis with R and needs a guide to get started. The book focuses on statistical data problems that are often encountered within the biosceinces. It puts special emphasis on linear models and analysis of repeated measurements data, but also deals with binary data and survival data, among others. Problems are presented and solutions -- along with the corresponding OR code and output -- are provided. The guide is divided into two parts: the first part on R basics and the second part on the statistical analyses using R. Various datasets are used for illustration and they are all available in the R package Guide1data.

Building SPSS Graphs to Understand Data is for anyone needing to understand large or small amounts of data. It describes how to build and interpret graphs, showing how understanding data means that the graph must clearly and succinctly answer questions about the data. In 16 of the 19 chapters research questions are presented, and the reader builds the appropriate graph needed to answer the questions. This handy guide can be used in conjunction with any introductory or intermediate statistics book where the focus is on in-depth presentation of how graphs are used. This book will also useful for graduate students doing research at the masters or doctoral level. The book also contains a chapter designed to address many of the ways that graphs can be used to mislead the graph reader.