This 1999 book is about the kind of mathematics usually encountered in first year university courses. A key feature of the book is that this mathematics is explored in depth using the popular and powerful package MATLAB. The emphasis is on understanding and investigating the mathematics, and putting it into practice in a wide variety of modelling situations. In the process, the reader will gain some fluency with MATLAB, no starting knowledge of the package being assumed. The range of material is wide: matrices, whole numbers, complex numbers, geometry of curves and families of lines, data analysis, random numbers and simulations, and differential equations form the basic mathematics. This is applied to a large number of investigations and modelling problems, from sequences of real numbers to cafeteria queues, from card shuffling to models of fish growth. All extras to the standard MATLAB package are supplied on the World Wide Web.

Mathematical Explorations with MATLAB examines the mathematics most frequently encountered in first-year university courses. A key feature of the book is its use of MATLAB, a popular and powerful software package. The book's emphasis is on understanding and investigating the mathematics by putting the mathematical tools into practice in a wide variety of modeling situations. Even readers who have no prior experience with MATLAB will gain fluency. The book covers a wide range of material: matrices, whole numbers, complex numbers, geometry of curves and families of lines, data analysis, random numbers and simulations, and differential equations from the basic mathematics. These lessons are applied to a rich variety of investigations and modeling problems, from sequences of real numbers to cafeteria queues, from card shuffling to models of fish growth. All extras to the standard MATLAB package are supplied on the World Wide Web.

Praise for the first edition: "One of my biggest complaints when I teach introductory statistics classes is that it takes me most of the semester to get to the good stuff-inferential statistics. The author manages to do this very quickly....if one were looking for a book that efficiently covers basic statistical methodology and also introduces statistical software [this text] fits the bill." -The American Statistician Applied Statistical Inference with MINITAB, Second Edition distinguishes itself from other introductory statistics textbooks by focusing on the applications of statistics without compromising mathematical rigor. It presents the material in a seamless step-by-step approach so that readers are first introduced to a topic, given the details of the underlying mathematical foundations along with a detailed description of how to interpret the findings, and are shown how to use the statistical software program Minitab to perform the same analysis. Gives readers a solid foundation in how to apply many different statistical methods. MINITAB is fully integrated throughout the text. Includes fully worked out examples so students can easily follow the calculations. Presents many new topics such as one- and two-sample variances, one- and two-sample Poisson rates, and more nonparametric statistics. Features mostly new exercises as well as the addition of Best Practices sections that describe some common pitfalls and provide some practical advice on statistical inference. This book is written to be user-friendly for students and practitioners who are not experts in statistics, but who want to gain a solid understanding of basic statistical inference. This book is oriented towards the practical use of statistics. The examples, discussions, and exercises are based on data and scenarios that are common to students in their everyday lives.

The R Companion to Elementary Applied Statistics includes traditional applications covered in elementary statistics courses as well as some additional methods that address questions that might arise during or after the application of commonly used methods. Beginning with basic tasks and computations with R, readers are then guided through ways to bring data into R, manipulate the data as needed, perform common statistical computations and elementary exploratory data analysis tasks, prepare customized graphics, and take advantage of R for a wide range of methods that find use in many elementary applications of statistics. Features: Requires no familiarity with R or programming to begin using this book. Can be used as a resource for a project-based elementary applied statistics course, or for researchers and professionals who wish to delve more deeply into R. Contains an extensive array of examples that illustrate ideas on various ways to use pre-packaged routines, as well as on developing individualized code. Presents quite a few methods that may be considered non-traditional, or advanced. Includes accompanying carefully documented script files that contain code for all examples presented, and more. R is a powerful and free product that is gaining popularity across the scientific community in both the professional and academic arenas. Statistical methods discussed in this book are used to introduce the fundamentals of using R functions and provide ideas for developing further skills in writing R code. These ideas are illustrated through an extensive collection of examples. About the Author: Christopher Hay-Jahans received his Doctor of Arts in mathematics from Idaho State University in 1999. After spending three years at University of South Dakota, he moved to Juneau, Alaska, in 2002 where he has taught a wide range of undergraduate courses at University of Alaska Southeast.

This book explains basic principles of MuPAD commands. It teaches how to write simple programs and develop interactive environments for teaching mathematics. The text gives a large number of useful examples from different areas of undergraduate mathematics developed by the author during his long teaching experience. All the book examples are available online. Flash, SVG and JVX formats are used to display interactive and animated graphics.

Most biologists use nonlinear regression more than any other statistical technique, but there are very few places to learn about curve-fitting. This book, by the author of the very successful Intuitive Biostatistics, addresses this relatively focused need of an extraordinarily broad range of scientists.

This open access book provides insights into the novel Locally Refined B-spline (LR B-spline) surface format, which is suited for representing terrain and seabed data in a compact way. It provides an alternative to the well know raster and triangulated surface representations. An LR B-spline surface has an overall smooth behavior and allows the modeling of local details with only a limited growth in data volume. In regions where many data points belong to the same smooth area, LR B-splines allow a very lean representation of the shape by locally adapting the resolution of the spline space to the size and local shape variations of the region. The iterative method can be modified to improve the accuracy in particular domains of a point cloud. The use of statistical information criterion can help determining the optimal threshold, the number of iterations to perform as well as some parameters of the underlying mathematical functions (degree of the splines, parameter representation). The resulting surfaces are well suited for analysis and computing secondary information such as contour curves and minimum and maximum points. Also deformation analysis are potential applications of fitting point clouds with LR B-splines.

Mathematica (R) in the Laboratory is a hands-on guide which shows how to harness the power and flexibility of Mathematica in the control of data-acquisition equipment and the analysis of experimental data. It explains how to use Mathematica to import, manipulate, visualise and analyse data from existing files. The generation and export of test data are also covered. The control of laboratory equipment is dealt with in detail, including the use of Mathematica's MathLink (R) system in instrument control, data processing, and interfacing. Many practical examples are given, which can either be used directly or adapted to suit a particular application. The book sets out clearly how Mathematica can provide a truly unified data-handling environment, and will be invaluable to anyone who collects or analyses experimental data, including astronomers, biologists, chemists, mathematicians, geologists, physicists and engineers. The book is fully compatible with Mathematica 3.0.

Intended as a companion for textbooks in mathematical methods for science and engineering, this book presents a large number of numerical topics and exercises together with discussions of methods for solving such problems using Mathematica(R). The accompanying CD contains Mathematica Notebooks for illustrating most of the topics in the text and for solving problems in mathematical physics. Although it is primarily designed for use with the author's "Mathematical Methods: For Students of Physics and Related Fields," the discussions in the book sufficiently self-contained that the book can be used as a supplement to any of the standard textbooks in mathematical methods for undergraduate students of physical sciences or engineering.

In biological research, the amount of data available to researchers has increased so much over recent years, it is becoming increasingly difficult to understand the current state of the art without some experience and understanding of data analytics and bioinformatics. An Introduction to Bioinformatics with R: A Practical Guide for Biologists leads the reader through the basics of computational analysis of data encountered in modern biological research. With no previous experience with statistics or programming required, readers will develop the ability to plan suitable analyses of biological datasets, and to use the R programming environment to perform these analyses. This is achieved through a series of case studies using R to answer research questions using molecular biology datasets. Broadly applicable statistical methods are explained, including linear and rank-based correlation, distance metrics and hierarchical clustering, hypothesis testing using linear regression, proportional hazards regression for survival data, and principal component analysis. These methods are then applied as appropriate throughout the case studies, illustrating how they can be used to answer research questions. Key Features: * Provides a practical course in computational data analysis suitable for students or researchers with no previous exposure to computer programming. * Describes in detail the theoretical basis for statistical analysis techniques used throughout the textbook, from basic principles * Presents walk-throughs of data analysis tasks using R and example datasets. All R commands are presented and explained in order to enable the reader to carry out these tasks themselves. * Uses outputs from a large range of molecular biology platforms including DNA methylation and genotyping microarrays; RNA-seq, genome sequencing, ChIP-seq and bisulphite sequencing; and high-throughput phenotypic screens. * Gives worked-out examples geared towards problems encountered in cancer research, which can also be applied across many areas of molecular biology and medical research. This book has been developed over years of training biological scientists and clinicians to analyse the large datasets available in their cancer research projects. It is appropriate for use as a textbook or as a practical book for biological scientists looking to gain bioinformatics skills.

This COMPSTAT 2002 book contains the Keynote, Invited, and Full Contributed papers presented in Berlin, August 2002. A companion volume including Short Communications and Posters is published on CD. The COMPSTAT 2002 is the 15th conference in a serie of biannual conferences with the objective to present the latest developments in Computational Statistics and is taking place from August 24th to August 28th, 2002. Previous COMPSTATs were in Vienna (1974), Berlin (1976), Leiden (1978), Edinburgh (1980), Toulouse (1982), Pra ue (1984), Rome (1986), Copenhagen (1988), Dubrovnik (1990), Neuchatel (1992), Vienna (1994), Barcelona (1996), Bris tol (1998) and Utrecht (2000). COMPSTAT 2002 is organised by CASE, Center of Applied Statistics and Eco nomics at Humboldt-Universitat zu Berlin in cooperation with F'reie Universitat Berlin and University of Potsdam. The topics of COMPSTAT include methodological applications, innovative soft ware and mathematical developments, especially in the following fields: statistical risk management, multivariate and robust analysis, Markov Chain Monte Carlo Methods, statistics of E-commerce, new strategies in teaching (Multimedia, In ternet), computerbased sampling/questionnaires, analysis of large databases (with emphasis on computing in memory), graphical tools for data analysis, classification and clustering, new statistical software and historical development of software."

From the Foreword: "Dietmar Hildenbrand's new book, Introduction to Geometric Algebra Computing, in my view, fills an important gap in Clifford's geometric algebra literature...I can only congratulate the author for the daring simplicity of his novel educational approach taken in this book, consequently combined with hands on computer based exploration. Without noticing, the active reader will thus educate himself in elementary geometric algebra algorithm development, geometrically intuitive, highly comprehensible, and fully optimized." --Eckhard Hitzer, International Christian University, Tokyo, Japan Geometric Algebra is a very powerful mathematical system for an easy and intuitive treatment of geometry, but the community working with it is still very small. The main goal of this book is to close this gap with an introduction to Geometric Algebra from an engineering/computing perspective. This book is intended to give a rapid introduction to computing with Geometric Algebra and its power for geometric modeling. From the geometric objects point of view, it focuses on the most basic ones, namely points, lines and circles. This algebra is called Compass Ruler Algebra, since it is comparable to working with a compass and ruler. The book explores how to compute with these geometric objects, and their geometric operations and transformations, in a very intuitive way. The book follows a top-down approach, and while it focuses on 2D, it is also easily expandable to 3D computations. Algebra in engineering applications such as computer graphics, computer vision and robotics are also covered.

Commonly there is no natural place in a traditional curriculum for mathematics or statistics, where a bridge between theory and practice fits into. On the other hand, the demand for an education designed to supplement theoretical training by practial experience has been rapidly increasing.

There exists, consequently, a bit of a dichotomy between theoretical and applied statistics, and this book tries to straddle that gap. It links up the theory of a selection of statistical procedures used in general practice with their application to real world data sets using the statistical software package SAS (Statistical Analysis System). These applications are intended to illustrate the theory and to provide, simultaneously, the ability to use the knowledge effectively and readily in execution.

¿The best book on Maple just got better. This lively book is bursting with clear descriptions, revealing examples and top tips. It is gentle enough to act as an introduction and yet sufficiently comprehensive and well organised to serve as a reference manual. Maple Release 7 is significantly different to earlier releases, so this book will appeal even to hardened users who want to catch up fast.¿ ¿Des Higham, University of Strathclyde, UK This book provides an accelerated introduction to Maple for scientific programmers who already have experience in other computer languages (such as C, Pascal, or FORTRAN). It gives an overview of the most commonly used constructs and provides an elementary introduction to Maple programming. This edition of the book has been extensively updated for Maple Release 7 with future releases in mind. This has involved a substantial update of all programs, examples and exercises. Extensive new material has also been added, including an appendix on complex variables in a computer algebra context.

This book constitutes the thoroughly refereed post-proceedings of the 9th International Symposium on Graph Drawing, GD 2001, held in Vienna, Austria, in September 2001.The 32 revised full papers presented were carefully reviewed and selected from 66 paper submissions. Also included are a corrected version of a paper from the predecessor volume, short reports on the software systems exhibition, two papers of the special session on graph exchange formats, and a report on the annual graph drawing contests. The papers are organized in topical sections on hierarchical drawing, planarity, crossing theory, compaction, planar graphs, symmetries, interactive drawing, representations, aesthetics, 2D- and 3D-embeddings, data visualization, floor planning, and planar drawing.

Quantum Methods with Mathematica, the first book of its kind, has achieved worldwide success and critical acclaim.

This book constitutes the thoroughly refereed post-proceedings of the 10th International Symposium on Graph Drawing, GD 2002, held in Irvine, CA, USA, in August 2002.The 24 revised full papers, 9 short papers, and 7 software demonstrations presented together with a report on the GD 2002 graph drawing contest were carefully reviewed and selected from a total of 48 regular paper submissions. All current aspects of graph drawing are addressed.

Biological systems are extremely complex and have emergent properties that cannot be explained or even predicted by studying their individual parts in isolation. The reductionist approach, although successful in the early days of molecular biology, underestimates this complexity. As the amount of available data grows, so it will become increasingly important to be able to analyse and integrate these large data sets. This book introduces novel approaches and solutions to the Big Data problem in biomedicine, and presents new techniques in the field of graph theory for handling and processing multi-type large data sets. By discussing cutting-edge problems and techniques, researchers from a wide range of fields will be able to gain insights for exploiting big heterogonous data in the life sciences through the concept of 'network of networks'.

This book offers a detailed application guide to XploRe - an interactive statistical computing environment. As a guide it contains case studies of real data analysis situations. It helps the beginner in statistical data analysis to learn how XploRe works in real life applications. Many examples from practice are discussed and analysed in full length. Great emphasis is put on a graphic based understanding of the data interrelations. The case studies include: Survival modelling with Cox's proportional hazard regression, Vitamin C data analysis with Quantile Regression, and many others.

The most widely used statistical method in seasonal adjustment is without doubt that implemented in the X-11 Variant of the Census Method II Seasonal Adjustment Program. Developed at the US Bureau of the Census in the 1950's and 1960's, this computer program has undergone numerous modifications and improvements, leading especially to the X-11-ARIMA software packages in 1975 and 1988 and X-12-ARIMA, the first beta version of which is dated 1998. While these software packages integrate, to varying degrees, parametric methods, and especially the ARIMA models popularized by Box and Jenkins, they remain in essence very close to the initial X-11 method, and it is this "core" that Seasonal Adjustment with the X-11 Method focuses on. With a Preface by Allan Young, the authors document the seasonal adjustment method implemented in the X-11 based software. It will be an important reference for government agencies, macroeconomists, and other serious users of economic data. After some historical notes, the authors outline the X-11 methodology. One chapter is devoted to the study of moving averages with an emphasis on those used by X-11. Readers will also find a complete example of seasonal adjustment, and have a detailed picture of all the calculations. The linear regression models used for trading-day effects and the process of detecting and correcting extreme values are studied in the example. The estimation of the Easter effect is dealt with in a separate chapter insofar as the models used in X-11-ARIMA and X-12-ARIMA are appreciably different. Dominique Ladiray is an Administrateur at the French Institut National de la Statistique et des Etudes Economiques. He is also a Professor at the Ecole Nationale de la Statistique et de l'Administration Economique, and at the Ecole Nationale de la Statistique et de l'Analyse de l'Information. He currently works on short-term economic analysis. Benoît Quenneville is a methodologist with Statistics Canada Time Series Research and Analysis Centre. He holds a Ph.D. from the University of Western Ontario. His research interests are in time series analysis with an emphasis on official statistics.

This book covers the needs of scientists - be they mathematicians, physicists, chemists or engineers - in terms of symbolic computation, and allows them to locate quickly, via a detailed table of contents and index, the method they require for the precise problem they are adressing.It requires no prior experience of symbolic computation, nor specialized mathematical knowledge, and provides quick access to the practical use of symbolic computation software. The organization of the book in mutually independent chapters, each focusing on a specific topic, allows the user to select what is of interest without necessarily reading everything.

All disciplines of science and engineering use numerical methods for complex problem analysis, due to the highly mathematical nature of the field. Analytical methods alone are unable to solve many complex problems engineering students and professionals confront. Introduction to MATLAB (R) Programming for Engineers and Scientists examines the basic elements of code writing, and describes MATLAB (R) methods for solving common engineering problems and applications across the range of engineering disciplines. The text uses a class-tested learning approach and accessible two-color page design to guide students from basic programming to the skills needed for future coursework and engineering practice.

MATLAB , a software package developed by Math Works, Inc. is powerful, versatile and interactive software for scientific and technical computations including simulations. Specialised toolboxes provided with several built-in functions are a special feature of MATLAB .

This book titled System Design through MATLAB Control Toolbox and SIMULINK aims at getting the reader started with computations and simulations in system engineering quickly and easily and then proceeds to build concepts for advanced computations and simulations that includes the control and compensation of systems. Simulation through SIMULINK has also been described to allow the reader to get the feel of the real world situation.

This book is appropriate for undergraduate students undergoing final semester of their project work, postgraduate students who have MATLAB integrated in their course or wish to take up simulation problem in the area of system engineering for their dissertation work and research scholars for whom MATLAB along with toolboxes and SIMULINK is an indispensable tool for simulation.

To download interactive simulations of various systems (523Kb) described in the book, go to http//www.springer.co.uk/matlab.htm

It is generally accepted that training in statistics must include some exposure to the mechanics of computational statistics. This learning guide is intended for beginners in computer-aided statistical data analysis. The prerequisites for XploRe - the statistical computing environment - are an introductory course in statistics or mathematics. The reader of this book should be familiar with basic elements of matrix algebra and the use of HTML browsers. This guide is designed to help students to XploRe their data, to learn (via data interaction) about statistical methods and to disseminate their findings via the HTML outlet. The XploRe APSS (Auto Pilot Support System) is a powerful tool for finding the appropriate statistical technique (quantlet) for the data under analysis. Homogeneous quantlets are combined in XploRe into quantlibs. The XploRe language is intuitive and users with prior experience of other sta tistical programs will find it easy to reproduce the examples explained in this guide. The quantlets in this guide are available on the CD-ROM as well as on the Internet. The statistical operations that the student is guided into range from basic one-dimensional data analysis to more complicated tasks such as time series analysis, multivariate graphics construction, microeconometrics, panel data analysis, etc. The guide starts with a simple data analysis of pullover sales data, then in troduces graphics. The graphics are interactive and cover a wide range of dis plays of statistical data."

Increasing the designer's con dence that a piece of software or hardwareis c- pliant with its speci cation has become a key objective in the design process for software and hardware systems. Many approaches to reaching this goal have been developed, including rigorous speci cation, formal veri cation, automated validation, and testing. Finite-state model checking, as it is supported by the explicit-state model checkerSPIN, is enjoying a constantly increasingpopularity in automated property validation of concurrent, message based systems. SPIN has been in large parts implemented and is being maintained by Gerard Ho- mann, and is freely available via ftp fromnetlib.bell-labs.comor from URL http: //cm.bell-labs.com/cm/cs/what/spin/Man/README.html. The beauty of nite-state model checking lies in the possibility of building \push-button" validation tools. When the state space is nite, the state-space traversal will eventually terminate with a de nite verdict on the property that is being validated. Equally helpful is the fact that in case the property is inv- idated the model checker will return a counterexample, a feature that greatly facilitates fault identi cation. On the downside, the time it takes to obtain a verdict may be very long if the state space is large and the type of properties that can be validated is restricted to a logic of rather limited expressiveness.