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Features the best practices in the art and science of constructing software--topics include design, applying good techniques to construction, eliminating errors, planning, managing construction activities, and relating personal character to superior software. Original. (Intermediate)
Ralph Kimball, Margy Ross
Ralph Kimball invented a data warehousing technique called ?dimensional modelling? and popularised it in his first Wiley bestseller The Data Warehouse Toolkit. Since then dimensional modelling has become the most widely accepted technique for data warehouse design. Since the first edition, Kimball has improved on his earlier techniques and created many new ones. In this second edition, he provides a comprehensive collection of all of them, from basic to advanced, and strategies for optimising data warehouse design for common business applications. He includes examples for retail sales, inventory management, procurement, orders and invoices, customer relationship management, accounting, financial services, telecommunication and utilities, health care, insurance and more. He also presents unique modelling techniques for e-commerce and shows strategies for optimising performance. A companion Web site provides updates on dimensional modelling techniques, links to related sites and source code where appropriate.
Richard O. Duda, Peter E. Hart, David G. Stork
This edition has been completely revised, enlarged and formatted in two colour. It is a systematic account of the major topics in pattern recognition, based on the fundamental principles. It includes extensive examples, exercises and a solutions manual.
John Chambers turns his attention to R, the enormously successful open-source system based on the S language. His book guides the reader through programming with R, beginning with simple interactive use and progressing by gradual stages, starting with simple functions. More advanced programming techniques can be added as needed, allowing users to grow into software contributors, benefiting their careers and the community. R packages provide a powerful mechanism for contributions to be organized and communicated. This is the only advanced programming book on R, written by the author of the S language from which R evolved.
William Lidwell, Kritina Holden, Jill Butler
A cross-disciplinary reference of design. Pairs common design concepts with examples that illustrate them in practice.
Kenneth H. Rosen
This text is designed for the sophomore/junior level introduction to discrete mathematics taken by students preparing for future coursework in areas such as math, computer science and engineering. Rosen has become a bestseller largely due to how effectively it addresses the main portion of the discrete market, which is typically characterized as the mid to upper level in rigor. The strength of Rosen's approach has been the effective balance of theory with relevant applications, as well as the overall comprehensive nature of the topic coverage.
This book was written for statisticians, computer scientists, geographers, research and applied scientists, and others interested in visualizing data. It presents a unique foundation for producing almost every quantitative graphic found in scientific journals, newspapers, statistical packages, and data visualization systems. This foundation was designed for a distributed computing environment (Internet, Intranet, client-server), with special attention given to conserving computer code and system resources. While the tangible result of this work is a Java production graphics library (GPL) developed in collaboration with Dan Rope and Dan Carr, this book focuses on the deep structures involved in producing quantitative graphics from data. What are the rules that underly the production of pie charts, bar charts, scatterplots, function plots, maps, mosaics, radar charts? These rules are abstracted from the work of Bertin, Cleveland, Kosslyn, MacEachren, Pinker, Tufte, Tukey, Tobler, and other theorists of quantitative graphics. Those less interested in the theoretical and mathematical foundations can still get a sense of the richness and structure of the system by examining the numerous and often unique color graphics it can produce. Leland Wilkinson is Senior VP, SYSTAT Products at SPSS Inc. and Adjunct Professor of Statistics at Northwestern University. He wrote the SYSTAT statistical package and founded SYSTAT Inc. in 1984. Wilkinson joined SPSS in a 1994 acquisition and now works on research and development of graphical applications for data mining and statistics. He is a Fellow of the ASA and an Associate Editor of The American Statistician. In addition to journal articles and theoriginal SYSTAT computer program and manuals, Wilkinson is the author (with Grant Blank and Chris Gruber) of Desktop Data Analysis with SYSTAT.
Christian Kleiber, Achim Zeileis
R is a language and environment for data analysis and graphics. It may be considered an implementation of S, an award-winning language initially - veloped at Bell Laboratories since the late 1970s. The R project was initiated by Robert Gentleman and Ross Ihaka at the University of Auckland, New Zealand, in the early 1990s, and has been developed by an international team since mid-1997. Historically, econometricians have favored other computing environments, some of which have fallen by the wayside, and also a variety of packages with canned routines. We believe that R has great potential in econometrics, both for research and for teaching. There are at least three reasons for this: (1) R is mostly platform independent and runs on Microsoft Windows, the Mac family of operating systems, and various ?avors of Unix/Linux, and also on some more exotic platforms. (2) R is free software that can be downloaded and installed at no cost from a family of mirror sites around the globe, the Comprehensive R Archive Network (CRAN); hence students can easily install it on their own machines. (3) R is open-source software, so that the full source code is available and can be inspected to understand what it really does, learn from it, and modify and extend it. We also like to think that platform independence and the open-source philosophy make R an ideal environment for reproducible econometric research.
John K. Kruschke
There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis obtainable to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS provides an accessible approach to Bayesian data analysis, as material is explained clearly with concrete examples. The book begins with the basics, including essential concepts of probability and random sampling, and gradually progresses to advanced hierarchical modeling methods for realistic data. The text delivers comprehensive coverage of all scenarios addressed by non-Bayesian textbooks--t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chi-square (contingency table analysis). This book is intended for first year graduate students or advanced undergraduates. It provides a bridge between undergraduate training and modern Bayesian methods for data analysis, which is becoming the accepted research standard. Prerequisite is knowledge of algebra and basic calculus. Free software now includes programs in JAGS, which runs on Macintosh, Linux, and Windows. Author website: http://www.indiana.edu/~kruschke/DoingBayesianDataAnalysis/ -Accessible, including the basics of essential concepts of probability and random sampling -Examples with R programming language and BUGS software -Comprehensive coverage of all scenarios addressed by non-bayesian textbooks- t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chi-square (contingency table analysis). -Coverage of experiment planning -R and BUGS computer programming code on website -Exercises have explicit purposes and guidelines for accomplishment
Jacques Bertin, William J. Berg
Originally published in French in 1967, "Semiology of Graphics" holds a significant place in the theory of information design. It presents a close study of graphic techniques including shape, orientation, color, texture, volume, and size in an array of more than 1,000 maps and diagrams.
This book reflects decades of important research on the mathematical foundations of speech recognition. It focuses on underlying statistical techniques such as hidden Markov models, decision trees, the expectation-maximization algorithm, information theoretic goodness criteria, maximum entropy probability estimation, parameter and data clustering, and smoothing of probability distributions. The author's goal is to present these principles clearly in the simplest setting, to show the advantages of self-organization from real data, and to enable the reader to apply the techniques.
Hans Schneider, George Phillip Barker
Linear algebra is one of the central disciplines in mathematics. A student of pure mathematics must know linear algebra if he is to continue with modern algebra or functional analysis. Much of the mathematics now taught to engineers and physicists requires it. This well-known and highly regarded text makes the subject accessible to undergraduates with little mathematical experience. Written mainly for students in physics, engineering, economics, and other fields outside mathematics, the book gives the theory of matrices and applications to systems of linear equations, as well as many related topics such as determinants, eigenvalues, and differential equations. Table of Contents: l. The Algebra of Matrices 2. Linear Equations 3. Vector Spaces 4. Determinants 5. Linear Transformations 6. Eigenvalues and Eigenvectors 7. Inner Product Spaces 8. Applications to Differential Equations For the second edition, the authors added several exercises in each chapter and a brand new section in Chapter 7. The exercises, which are both true-false and multiple-choice, will enable the student to test his grasp of the definitions and theorems in the chapter. The new section in Chapter 7 illustrates the geometric content of Sylvester's Theorem by means of conic sections and quadric surfaces. 6 line drawings. lndex. Two prefaces. Answer section.
Steven D. Levitt, Stephen J. Dubner
The New York Times bestselling Freakonomics was a worldwide sensation. Now, Steven D. Levitt and Stephen J. Dubner return with SuperFreakonomics, and fans and newcomers alike will find that the "freakquel" is even bolder, funnier, and more surprising than the first. SuperFreakonomics challenges the way we think all over again, exploring the hidden side of everything with such questions as: How is a street prostitute like a department store Santa? Who adds more value: a pimp or a Realtor? What do hurricanes, heart attacks, and highway deaths have in common? Did TV cause a rise in crime? Can eating kangaroo meat save the planet? Whether investigating a solution to global warming or explaining why the price of oral sex has fallen so drastically, Levitt and Dubner show the world for what it really is—good, bad, ugly, and, in the final analysis, superfreaky.
This applications oriented book features coverage of Markov chains and queuing theory which is of particular interest to communications professionals--a newer area where many professionals will need an update or refresher. It also features computer-based methods and exercises providing the most up-to-date training for those in the fields of telecommunications and computer engineering.
Robert H. Shumway, David S. Stoffer
Time series analysis includes techniques for drawing conclusions from data recorded over a period of time. This book provides a modern introduction to time series analysis that will be useful as a reference to students in statistics, engineering, medicine, and economics. Robert H. Shumway is Professor of Statistics at the University of California, Davis, and David Stoffer is Professor of Statistics at the University of Pittsburgh.
Helen Sharp, Yvonne Rogers, Jenny Preece
The classic text, Interaction Design by Sharp, Preece and Rogers is back in a fantastic new 2nd Edition! New to this edition: Completely updated to include new chapters on Interfaces, Data Gathering and Data Analysis and Interpretation, the latest information from recent research findings and new examples Now in full colour A lively and highly interactive Web site that will enable students to collaborate on experiments, compete in design competitions, collaborate on designs, find resources and communicate with others A new practical and process-oriented approach showing not just what principals ought to apply, but crucially how they can be applied "The best basis around for user-centered interaction design, both as a primer for students as an introduction to the field, and as a resource for research practitioners to fall back on. It should be labelled 'start here'." —Pieter Jan Stappers, ID-StudioLab, Delft University of Technology
Sheldon M. Ross
This updated text provides a superior introduction to applied probability and statistics for engineering or science majors. Numerous exercises, examples, and applications apply probability theory to everyday statistical problems and situations.
Execution plans show you what's going on behind the scenes in SQL Server. They can provide you with a wealth of information on how your queries are being executed by SQL Server, including: Which indexes are being used, and where no indexes are being used at all. How the data is being retrieved, and joined, from the tables defi ned in your query. How aggregations in GROUP BY queries are put together. The anticipated load and the estimated cost that all these operations place upon the system. Grant Fritchey's book is the only in-depth look at how to improve your SQL query performance through careful design of execution plans. Sample chapters of the ebook have garnered stunning reviews, such as: "All I can say is WOW. This has to be the best reference I have ever seen on Execution Plans in SQL Server. My hats off to Grant Fritchey" Jonathan Kehayias.
Ruey S. Tsay
Provides statistical tools and techniques needed to understand today's financial markets The Second Edition of this critically acclaimed text provides a comprehensive and systematic introduction to financial econometric models and their applications in modeling and predicting financial time series data. This latest edition continues to emphasize empirical financial data and focuses on real-world examples. Following this approach, readers will master key aspects of financial time series, including volatility modeling, neural network applications, market microstructure and high-frequency financial data, continuous-time models and Ito's Lemma, Value at Risk, multiple returns analysis, financial factor models, and econometric modeling via computation-intensive methods. The author begins with the basic characteristics of financial time series data, setting the foundation for the three main topics: Analysis and application of univariate financial time series Return series of multiple assets Bayesian inference in finance methods This new edition is a thoroughly revised and updated text, including the addition of S-Plus® commands and illustrations. Exercises have been thoroughly updated and expanded and include the most current data, providing readers with more opportunities to put the models and methods into practice. Among the new material added to the text, readers will find: Consistent covariance estimation under heteroscedasticity and serial correlation Alternative approaches to volatility modeling Financial factor models State-space models Kalman filtering Estimation of stochastic diffusion models The tools provided in this text aid readers in developing a deeper understanding of financial markets through firsthand experience in working with financial data. This is an ideal textbook for MBA students as well as a reference for researchers and professionals in business and finance.
Andrew Gelman, Jennifer Hill
This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models.
Richard Burden, J. Faires
This well-respected text gives an introduction to the theory and application of modern numerical approximation techniques for students taking a one- or two-semester course in numerical analysis. With an accessible treatment that only requires a calculus prerequisite, Burden and Faires explain how, why, and when approximation techniques can be expected to work, and why, in some situations, they fail. A wealth of examples and exercises develop students’ intuition, and demonstrate the subject’s practical applications to important everyday problems in math, computing, engineering, and physical science disciplines. The first book of its kind built from the ground up to serve a diverse undergraduate audience, three decades later Burden and Faires remains the definitive introduction to a vital and practical subject. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.
Merran Evans, Nicholas Hastings, Brian Peacock
From the reviews: "Concise and useful summaries of the salient facts and formulas relating to [various] distributions." -Journal of the American Statistical Association "A worthwhile reference." -Journal of Quality Technology Since the previous edition of this popular guide to the most commonly used statistical distributions was published in 1993, statistical methods have found many new applications in science, medicine, engineering, business/finance, and the social sciences. To keep pace with these developments and to highlight the growing influence of statistical software and data management techniques, this new edition is now thoroughly updated and revised. Through clear, concise, easy-to-follow presentations, the authors discuss the key facts and formulas for 40 major probability distributions, fine-tune all existing material, and continue to offer ready access to vital information gleaned from hard-to-find places across the literature. Highly useful both as an introduction to basic principles and as a quick reference guide, Statistical Distributions, Third Edition: * Presents the 40 distributions in alphabetical order * Provides all key formulas for each distribution * Adds a new chapter on the Empirical Distribution Function * Expands the Weibull Distribution to cover the 3 and 5 parameter versions * Incorporates diagrams and tables illustrating the characteristics of each distribution * Discusses the types of application for which distributions are used * Features references to relevant software packages
Seymour Lipschutz, Marc Lipson
Tough Test Questions? Missed Lectures? Not Enough Time? Fortunately for you, there's Schaum's Outlines. More than 40 million students have trusted Schaum's to help them succeed in the classroom and on exams. Schaum's is the key to faster learning and higher grades in every subject. Each Outline presents all the essential course information in an easy-to-follow, topic-by-topic format. You also get hundreds of examples, solved problems, and practice exercises to test your skills. This Schaum's Outline gives you: Practice problems with full explanations that reinforce knowledge Coverage of the most up-to-date developments in your course field In-depth review of practices and applications Fully compatible with your classroom text, Schaum's highlights all the important facts you need to know. Use Schaum's to shorten your study time-and get your best test scores! Schaum's Outlines-Problem Solved.
Robert A. Muenchen
While SAS and SPSS have many things in common, R is very different. My goal in writing this book is to help you translate what you know about SAS or SPSS into a working knowledge of R as quickly and easily as possible. I point out how they differ using terminology with which you are familiar, and show you which add-on packages will provide results most like those from SAS or SPSS. I provide many example programs done in SAS, SPSS, and R so that you can see how they compare topic by topic. When finished, you should be able to use R to: Read data from various types of text files and SAS/SPSS datasets. Manage your data through transformations or recodes, as well as splitting, merging and restructuring data sets. Create publication quality graphs including bar, histogram, pie, line, scatter, regression, box, error bar, and interaction plots. Perform the basic types of analyses to measure strength of association and group differences, and be able to know where to turn to cover much more complex methods.