
Introduction to Data Technologies  Paul Murrell 

Cover Price : Rs 2,995.00

Imprint : CRC Press ISBN : 9781498797740 YOP : 2016

Binding : Hardback Total Pages : 418 CD : No


DESCRIPTION
Contains a collection of diverse, computerrelated topics, with an emphasis on research
Connects varied topics through numerous, realworld 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, behindthescenes 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.
Onestop 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. 



Design and Modeling for Computer Experiments  KaiTai Fang, Runze Li, Agus Sudjianto 
Author 
KaiTai Fang Runze Li Agus Sudjianto


Cover Price : Rs 2,995.00

Imprint : CRC Press ISBN : 9781498797795 YOP : 2016

Binding : Hardback Total Pages : 304 CD : No


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 realworld 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 spacefilling 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
SpaceFilling 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 wellorganized 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 



Business Statistics, 3rd Edn  B.M. Aggarwal & Sukhvir Singh 
Author 
B.M. Aggarwal Sukhvir Singh


Cover Price : Rs 595.00

Imprint : Ane Books Pvt. Ltd. ISBN : 9789385462382 YOP : 2016

Binding : Paperback Size : 7.25" X 9.50" Total Pages : 836 CD : No


About the Book
The book meets the requirements of the undergraduate 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. 



ALL OF STATISTICS: A CONCISE COURSE IN STATISTICAL INFERENCE  LARRY WASSERMAN (EX) 

Cover Price : Rs 995.00

Imprint : Springer ISBN : 9788132213963 YOP : 2013

Binding : Paperback Total Pages : 462 CD : No


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 followup 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 pvalues. 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.




Microarray Image Analysis: An Algorithmic Approach  Karl Fraser, Zidong Wang, Xiaohui Liu 
Author 
Karl Fraser Zidong Wang Xiaohui Liu


Cover Price : Rs 2,995.00

Imprint : CRC Press ISBN : 9781498797757 YOP : 2016

Binding : Hardback Total Pages : 335 CD : No


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 graphcutting techniques
Presents the details of the algorithmic processes along with an analysis of the processes performance over realworld 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 highthroughput 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 analyticalbased methods and customdesigned 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 fullscale 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—metablocks
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
GraphCutting 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. 



Statistical Design of Experiments With Engineering Applications, Indian Reprint  Kamel Rekab (EX) 
Author 
Kamel Rekab Muzaffar Shaikh


Cover Price : Rs 4,995.00

Imprint : CRC / Lewis ISBN : 9781574446258 YOP : 2014

Binding : Hardback Total Pages : 268 CD : No


"provides easy to understand and fastlearning 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 readymade, quick, and easytolearn 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, stepwise 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
 THREELEVEL FACTORIAL DESIGNS
 SECONDORDER ANALYSIS
APPENDICES
Appendix 1: TwoLevel Fractional Factorial Designs
Appendix 2: PlackettBurman 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 BoxBehken Designs
Appendix 8: Matrix Algebra
Index




Elementary Statistical Quality Control 2nd Ed,Indian Reprint  John T.Burr (EX) 

Cover Price : Rs 4,995.00

Imprint : CRC / Lewis ISBN : 9780824790523 YOP : 2014

Binding : Hardback Total Pages : 468 CD : No


Maintaining the readerfriendly 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 realworld 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 OLDNUMBERED 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. 


