DESCRIPTION Contains a collection of diverse, computer-related topics, with an emphasis on research Connects varied topics through numerous, real-world case studies Describes open source technologies and open standards Devotes separate chapters to each computer language, including HTML, XML, SQL, and R Includes explanatory diagrams that aid in understanding important concepts Provides a suite of exercises as well as the code and data sets used in the case studies on the author’s website Summary Providing key information on how to work with research data, Introduction to Data Technologies presents ideas and techniques for performing critical, behind-the-scenes tasks that take up so much time and effort yet typically receive little attention in formal education. With a focus on computational tools, the book shows readers how to improve their awareness of what tasks can be achieved and describes the correct approach to perform these tasks. Practical examples demonstrate the most important points The author first discusses how to write computer code using HTML as a concrete example. He then covers a variety of data storage topics, including different file formats, XML, and the structure and design issues of relational databases. After illustrating how to extract data from a relational database using SQL, the book presents tools and techniques for searching, sorting, tabulating, and manipulating data. It also introduces some very basic programming concepts as well as the R language for statistical computing. Each of these topics has supporting chapters that offer reference material on HTML, CSS, XML, DTD, SQL, R, and regular expressions. One-stop shop of introductory computing information Written by a member of the R Development Core Team, this resource shows readers how to apply data technologies to tasks within a research setting. Collecting material otherwise scattered across many books and the web, it explores how to publish information via the web, how to access information stored in different formats, and how to write small programs to automate simple, repetitive tasks. CONTENTS Introduction Case Study: Point Nemo Writing Computer Code Case Study: Point Nemo (continued) Syntax Semantics Writing Code Checking Code Running Code The DRY Principle HTML Reference HTML Syntax HTML Semantics CSS Reference CSS Syntax CSS Semantics Linking CSS to HTML CSS Tips Data Storage Case Study: YBC 7289 Plain Text Formats Binary Formats Spreadsheets XML Databases XML Reference XML Syntax Document Type Definitions Data Queries Case Study: The Data Expo (continued) Querying Databases Querying XML SQL Reference SQL Syntax SQL Queries Other SQL Commands Data Processing Case Study: The Population Clock The R Environment The R Language Data Types and Data Structures Subsetting More on Data Structures Data Import/Export Data Manipulation Text Processing Data Display Programming Other Software R Reference R Syntax Data Types and Data Structures Functions Getting Help Packages Searching for Functions Regular Expressions Reference Literals Metacharacters Conclusion Attributions Bibliography Index Further Reading appears at the end of each chapter ABOUT THE AUTHOR Paul Murrell is a Senior Lecturer in the Department of Statistics at the University of Auckland, New Zealand. Author of the bestselling R Graphics (2006), he is also part of the development team for the R and Omegahat statistical computing projects. Dr. Murrell’s research interests include computational and graphical statistics.

DESCRIPTION Blends a modern, sound statistical approach with extensive practical engineering applications Presents numerous examples that clarify the methods and their implementation Presents the most useful design and modeling methods, including some original contributions from the authors Covers uniform design, measures of uniformity, and their algebraic approaches Discusses special techniques for model interpretation such as ANOVA and the Fourier Amplitude Sensitivity Test Summary Computer simulations based on mathematical models have become ubiquitous across the engineering disciplines and throughout the physical sciences. Successful use of a simulation model, however, requires careful interrogation of the model through systematic computer experiments. While specific theoretical/mathematical examinations of computer experiment design are available, those interested in applying proposed methodologies need a practical presentation and straightforward guidance on analyzing and interpreting experiment results. Written by authors with strong academic reputations and real-world practical experience, Design and Modeling for Computer Experiments is exactly the kind of treatment you need. The authors blend a sound, modern statistical approach with extensive engineering applications and clearly delineate the steps required to successfully model a problem and provide an analysis that will help find the solution. Part I introduces the design and modeling of computer experiments and the basic concepts used throughout the book. Part II focuses on the design of computer experiments. The authors present the most popular space-filling designs - like Latin hypercube sampling and its modifications and uniform design - including their definitions, properties, construction and related generating algorithms. Part III discusses the modeling of data from computer experiments. Here the authors present various modeling techniques and discuss model interpretation, including sensitivity analysis. An appendix reviews the statistics and mathematics concepts needed, and numerous examples clarify the techniques and their implementation. The complexity of real physical systems means that there is usually no simple analytic formula that sufficiently describes the phenomena. Useful both as a textbook and professional reference, this book presents the techniques you need to design and model computer experiments for practical problem solving. CONTENTS PART I AN OVERVIEW INTRODUCTION Experiments and Their Statistical Designs Some Concepts in Experimental Design Computer Experiments Examples of Computer Experiments Space-Filling Designs Modeling Techniques Sensitivity Analysis Strategies for Computer Experiments and An Illustration Case Study Remarks on Computer Experiments Guidance of Reading This Book PART II DESIGNS FOR COMPUTER EXPERIMENTS Latin Hypercube Sampling and its Modifications Uniform Experimental Design Optimization in Construction of Designs for Computer Experiments PART III MODELING FOR COMPUTER EXPERIMENTS METAMODELING Model Interpretation Functional Response APPENDIX Abbreviation References Index Author Index Reviews: ". . . very well-organized text . . . makes a very valuable contribution to the field. I highly recommend it for anyone trying to learn design and modeling techniques for computer experiments. In particular, it will be a useful professional reference for scientists and engineers in practicing computer experiments, a comprehensive resource book for statisticians interested in developing new techniques for designing and modeling computation experiments, and an excellent book for undergraduate and graduate students. The authors’ careful and thorough presentation style makes the book a very enjoyable read." – Hao Helen Zhang, North Carolina State University, in JASA, December 2008

About the Book The book meets the requirements of the under-graduate students taking statistics as main subject of all the Indian universities. It specifically covers the complete syllabus prescribed by Delhi University for B.com (Hons) Regular as well as sol programme. The book also covers the syllabus of B.A (Eco. Hons). The language is simple, easily graspable and the coverage is comprehensive. The treatment of the subject assumes no background of mathematics. Latest solved question papers of different universities have been given along with other solved examples. Multiple choice questions and practice questions with hints and answers have also been given. Contents 1. Introduction to Statistics 2. Preparation of Frequency Distribution 3. Collection and Organisation of Statistical Data 4. Statistical Averages (Measures of Central Tendency) 5. Measures of Variation (Dispersion) 6. Moments, Skewness and Kurtosis 7. Correlation Analysis 8. Regression Analysis 9. Index Numbers 10. Times Series Analysis 11. Probability and Mathematical Expectation 12. Probability Distributions (Theoretical Distributions) 13. Sampling Theory and Testing of Hypothesis 14. Statistical Decision Theory. About the Author B. M. Aggarwal, did B.Sc. Hons in Mathematics from Punjab University, and M.Sc Mathematics from Merrut University. Later he graduated in B.Tech (FIETE) from the Institute of Electronics and Telecommunication Engineers, New Delhi. In addition, he passed certificate courses in Microwave and Satellite Engineering from ALT Centre, Ghaziabad. The author is a visiting professor in Statistics, Quantitative techniques, operations research and research methods at various premier institutes like IMT Ghaziabad, ICFAI Gurgaon, Asia Pacific Institutes of Management ,New Delhi Institute of Management, Delhi besides several other Institutes. He has about ten books to his credit. Dr Sukhvir Singh is a faculty member of Department of Commerce, SGTB Khalsa College, University of Delhi, Delhi. He did his B.Com(H) and M.Com from Punjab University, Chandigarh. Thereafter, he was awarded Ph.D. from University Business School, Punjab University , Chandigarh. He was awarded with Research Fellowship from UGC, Government of India. He has been teaching Business Mathematics and Statistics paper for more than 12 years in University of Delhi. He has been a visiting faculty at University Business School, Punjab University, Chandigarh and School of Open Learning, University of Delhi. He has been guiding and supervising students of Commerce for M. Phil and Ph.D. Programmes.

About the Book :- Takes readers through the stages of image analysis Encompasses many new approaches for processing microarray images, including novel subgrid detection, feature identification, and graph-cutting techniques Presents the details of the algorithmic processes along with an analysis of the processes performance over real-world microarray image data Covers the strengths and weaknesses of each technique Includes background material on microarray variants, basic transformations, clustering, gene expression data mining, and more Summary To harness the high-throughput potential of DNA microarray technology, it is crucial that the analysis stages of the process are decoupled from the requirements of operator assistance. Microarray Image Analysis: An Algorithmic Approach presents an automatic system for microarray image processing to make this decoupling a reality. The proposed system integrates and extends traditional analytical-based methods and custom-designed novel algorithms. The book first explores a new technique that takes advantage of a multiview approach to image analysis and addresses the challenges of applying powerful traditional techniques, such as clustering, to full-scale microarray experiments. It then presents an effective feature identification approach, an innovative technique that renders highly detailed surface models, a new approach to subgrid detection, a novel technique for the background removal process, and a useful technique for removing "noise." The authors also develop an expectation–maximization (EM) algorithm for modeling gene regulatory networks from gene expression time series data. The final chapter describes the overall benefits of these techniques in the biological and computer sciences and reviews future research topics. This book systematically brings together the fields of image processing, data analysis, and molecular biology to advance the state of the art in this important area. Although the text focuses on improving the processes involved in the analysis of microarray image data, the methods discussed can be applied to a broad range of medical and computer vision analysis areas. Contents :- Introduction Overview Current state of art Experimental approach Key issues Contribution to knowledge Structure of the book Background Introduction Molecular biology Microarray technology Microarray analysis Copasetic microarray analysis framework overview Summary Data Services Introduction Image transformation engine Evaluation Summary Structure Extrapolation I Introduction Pyramidic contextual clustering Evaluation Summary Structure Extrapolation II Introduction Image layout—master blocks Image structure—meta-blocks Summary Feature Identification I Introduction Spatial binding Evaluation of feature identification Evaluation of copasetic microarray analysis framework Summary Feature Identification II Background Proposed approach—subgrid detection Experimental results Conclusions Chained Fourier Background Reconstruction Introduction Existing techniques A new technique Experiments and results Conclusions Graph-Cutting for Improving Microarray Gene Expression Reconstructions Introduction Existing techniques Proposed technique Experiments and results Conclusions Stochastic Dynamic Modeling of Short Gene Expression Time Series Data Introduction Stochastic dynamic model for gene expression data An EM algorithm for parameter identification Simulation results Discussions Conclusions and future work Conclusions Introduction Achievements Contributions to microarray biology domain Contributions to computer science domain Future research topics Appendix A: Microarray Variants Appendix B: Basic Transformations Appendix C: Clustering Appendix D: A Glance on Mining Gene Expression Data Appendix E: Autocorrelation and GHT References About the Authors :- Karl Fraser is a research fellow in the Centre for Intelligent Data Analysis at Brunel University. Zidong Wang is a professor of dynamical systems and computing in the Department of Information Systems and Computing at Brunel University. Xiaohu Liu is a professor of computing and head of the Centre for Intelligent Data Analysis at Brunel University.

This book is for people who want to learn probability and statistics quickly. It brings together many of the main ideas in modern statistics in one place. The book is suitable for students and researchers in statistics, computer science, data mining and machine learning. This book covers a much wider range of topics than a typical introductory text on mathematical statistics. It includes modern topics like nonparametric curve estimation, bootstrapping and classification, topics that are usually relegated to follow-up courses. The reader is assumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. The text can be used at the advanced undergraduate and graduate level. Larry Wasserman is Professor of Statistics at Carnegie Mellon University. He is also a member of the Center for Automated Learning and Discovery in the School of Computer Science. His research areas include nonparametric inference, asymptotic theory, causality, and applications to astrophysics, bioinformatics, and genetics. He is the 1999 winner of the Committee of Presidents of Statistical Societies Presidents' Award and the 2002 winner of the Centre de recherches mathematiques de Montreal–Statistical Society of Canada Prize in Statistics. He is Associate Editor of The Journal of the American Statistical Association and The Annals of Statistics. He is a fellow of the American Statistical Association and of the Institute of Mathematical Statistics. CONTENTS Probability.- Random Variables.- Expectation.- Inequalities.- Convergence of Random Variables.- Models, Statistical Inference and Learning.- Estimating the CDF and Statistical Functionals.- The Bootstrap.- Parametric Inference.- Hypothesis Testing and p-values.- Bayesian Inference.- Statistical Decision Theory.- Linear and Logistic Regression.- Multivariate Models.- Inference about Independence.- Causal Inference.- Directed Graphs and Conditional Independence.- Undirected Graphs.- Loglinear Models.- Nonparametric Curve Estimation.- Smoothing Using Orthogonal Functions.- Classification.- Probability Redux: Stochastic Processes.- Simulation Methods.Index.

"provides easy to understand and fast-learning approaches to applying experimental design methods to solving variety of problems that occur in practice. Statistical design principles and complicated concepts are explained very nicely (with minimal mathematical details) using simple examples drawn from engineering and related fields. Inclusion of several chapters on location and dispersion optimization techniques makes this book more useful than other traditional textbooks on experimental design. The authors emphasize more on design techniques than on estimation and analyses which is what gives new dimension. In all, this book is a welcome addition and one that will prove highly successful." -Prof. Ibrahim A. Ahmad, Dept. of Statistics & Actuarial Science, University of Central Florida, Orlando, USA Giving you a ready-made, quick, and easy-to-learn approach for applying design of experiments techniques to problems. Statistical Design of Experiments with Engineering Applications uses quality as the main theme to explain various design of experiments concepts. The authors examine the entire product lifecycle and the tools and techniques necessary to measure quality at each stage. They explain topics such as optimization, Taguchi's method, variance reduction, and graphical applications based on statistical techniques. Wherever applicable the book supplies practical rules of thumb, step-wise procedures that allow you to grasp concepts quickly and apply them appropriately, and examples that demonstrate how to apply techniques. Emphasizing the importance of quality to products and services, the authors include concepts from the field of Quality Engineering. Written with an emphasis on application and not on bogging you down with the theoretical underpinnings, the book enables you to solve 80% of design problems without worrying about the derivation of mathematical formulas. Contents - INTRODUCTION - DESIGNING AND CONDUCTING THE EXPERIMENTS - OPTIMIZATION OF THE LOCATION PARAMETER - MINIMIZATION OF THE DISPERSION - TAGUCHI'S APPROACH TO THE DESIGN OF EXPERIMENTS - STATISTICAL OPTIMIZATION OF THE LOCATION PARAMETER - STATISTICAL MINIMIZATION OF THE DISPERSION PARAMETER - VALIDITY OF THE PREDICTION EQUATION - THREE-LEVEL FACTORIAL DESIGNS - SECOND-ORDER ANALYSIS APPENDICES Appendix 1: Two-Level Fractional Factorial Designs Appendix 2: Plackett-Burman Designs Appendix 3: Taguchi Designs Appendix 4: Standardized Normal Distribution Appendix 5: Percentiles of t Distribution Appendix 6: Percentiles of the F Distribution Appendix 7: Some Useful Box-Behken Designs Appendix 8: Matrix Algebra Index

Maintaining the reader-friendly features of its popular predecessor, the Second Edition illustrates fundamental principles and practices in statistical quality control for improved quality, reliability, and productivity in the management of production processes and industrial and business operations. Presenting key concepts of statistical quality control in a simple and straightforward manner, this reference will provide a solid foundation in statistical quality control theory, background, and applications. Reflecting upon classic examples and case studies, as well as recent industrial data, this reference will prove invaluable in the development of solution to real-world dilemmas in the manufacturing and service industries. Moving from elementary topics to sampling by variables, sound tolerancing, and relationships between variable, this reference contains new and updated discussions on process capability, methods to establish realistic specifications, acceptance sampling, and procedures for statistical control charting…. Supplies a large number of practice problems and examples in each chapter…. considers management models currently utilized in industry including the TQC, TQM, and MBNQA ….helps quality control professionals obtain maximum performance from production processes and meet the demands of heightened competition and requirements set by national and international standards… and discusses key Six Sigma and Lean Manufacturing principles and concepts. Contents 1. WHY STATISTICS? 2. CHARACTERISTICS OF DATA AND HOW TO DESCRIBE THEM 3. SIMPLE PROBABILITY AND PROBABILITY DISTRIBUTIONS 4. CONTROL CHARTS IN GENERAL 5. CONTROL CHARTS FOR ATTRIBUTES 6. CONTROL CHARTS FOR MEASUREMENTS: PROCESS CONTROL 7. PROCESS CAPABILITY 8. FURTHER TOPICS IN CONTROL CHARTS AND APPLICATIONS 9. ACCEPTANCE SAMPLING FOR ATTRIBUTES 10. SOME STANDARD SAMPLING PLANS FOR ATTRIBUTES 11. SAMPLING BY VARIABLES 12. TOLERANCES FOR MATING PARTS AND ASSEMBLIES 13.STUDYING RELATIONSHIPS BETWEEN VARIABLES BY LINEAR CORRELATION AND REGRESSION 14. A FEW RELIABILITY CONCEPTS APPENDIX ANSWERS TO OLD-NUMBERED PROBLEMS INDEX About the Author John T. Burr is the founder of the consulting firm Rochester Quality Associates, and previously served as an Assistant Professor, Center for Quality and Applies Statistics, Rochester Institute of Technology, New York. Burr also worked at Eastman Kodak for 24 years and conducted extensive quality training programs, as well as consultation on the ISO 9000 standards, auditing, quality engineering, quality management, and statistical process control. He received the B.S. degree in chemistry from Grinnell College, lowa, and the Ph.D. degree in analytical chemistry from Purdue University, West Lafayette, Indiana.