Bioinformatics and Computational Biology Solutions Using R and Bioconductor (Statistics for Biology and Health)

Bioinformatics and Computational Biology Solutions Using R and Bioconductor (Statistics for Biology and Health) Review

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Bioinformatics and Computational Biology Solutions Using R and Bioconductor (Statistics for Biology and Health) Feature

Full four-color book.

Some of the editors created the Bioconductor project and Robert Gentleman is one of the two originators of R.

All methods are illustrated with publicly available data, and a major section of the book is devoted to fully worked case studies.

Code underlying all of the computations that are shown is made available on a companion website, and readers can reproduce every number, figure, and table on their own computers.

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The Statistical Analysis of Recurrent Events (Statistics for Biology and Health)

The Statistical Analysis of Recurrent Events (Statistics for Biology and Health) Review

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The Statistical Analysis of Recurrent Events (Statistics for Biology and Health) Feature

This book presents models and statistical methods for the analysis of recurrent event data. The authors provide broad, detailed coverage of the major approaches to analysis, while emphasizing the modeling assumptions that they are based on. More general intensity-based models are also considered, as well as simpler models that focus on rate or mean functions. Parametric, nonparametric and semiparametric methodologies are all covered, with procedures for estimation, testing and model checking.

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Nondetects and Data Analysis: Statistics for Censored Environmental Data (Statistics in Practice)

Nondetects and Data Analysis: Statistics for Censored Environmental Data (Statistics in Practice) Review

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STATISTICS IN PRACTICE

Statistical methods for interpreting and analyzing censored environmental data

Nondetects And Data Analysis: Statistics for Censored Environmental Data provides solutions for environmental scientists and professionals who need to interpret and analyze data that fall below the laboratory detection limit. Adapting survival analysis methods that have been successfully used in medical and industrial research, the author demonstrates, for the first time, their practical applications for studies of trace chemicals in air, water, soils, and biota. Readers quickly become proficient in these methods through the use of real-world examples that are solved using MINITAB® Release 14, a popular statistical software package, as well as other commonly used software packages.

Everything needed to master these innovative statistical methods is provided, including:

• Accompanying Web site featuring answers to book exercises and datasets, as well as MINITAB® macros to perform methods, which are not available in the commercial version
• Methods for data with multiple detection limits
• Solutions for research studies in which all data are below detection limits
• Techniques for constructing confidence, prediction, and tolerance intervals for data with nond-tects
• Methods for data with multiple detection limits

Chapters are organized by objective, such as computing intervals, comparing groups, and correlations, which enables readers to more easily apply the text to their particular research and goals. Extensive references to the literature for more in-depth research are provided; however, the text itself avoids complex math and calculus making it accessible to anyone in the environmental sciences. Environmental scientists and professionals will find the hands-on guidance and practical examples invaluable.

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Recent Advances in Reliability Theory: Methodology, Practice and Inference (Statistics for Industry and Technology)

Recent Advances in Reliability Theory: Methodology, Practice and Inference (Statistics for Industry and Technology) Review

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Recent Advances in Reliability Theory: Methodology, Practice and Inference (Statistics for Industry and Technology) Feature

This book presents thirty-one extensive and carefully edited chapters providing an up-to-date survey of new models and methods for reliability analysis and applications in science, engineering, and technology. The chapters contain broad coverage of the latest developments and innovative techniques in a wide range of theoretical and numerical issues in the field of statistical and probabilistic methods in reliability.

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Bayesian Networks: A Practical Guide to Applications (Statistics in Practice)

Bayesian Networks: A Practical Guide to Applications (Statistics in Practice) Review

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Bayesian Networks: A Practical Guide to Applications (Statistics in Practice) Feature

Bayesian Networks, the result of the convergence of artificial intelligence with statistics, are growing in popularity. Their versatility and modelling power is now employed across a variety of fields for the purposes of analysis, simulation, prediction and diagnosis.

This book provides a general introduction to Bayesian networks, defining and illustrating the basic concepts with pedagogical examples and twenty real-life case studies drawn from a range of fields including medicine, computing, natural sciences and engineering.

Designed to help analysts, engineers, scientists and professionals taking part in complex decision processes to successfully implement Bayesian networks, this book equips readers with proven methods to generate, calibrate, evaluate and validate Bayesian networks.

The book:

• Provides the tools to overcome common practical challenges such as the treatment of missing input data, interaction with experts and decision makers, determination of the optimal granularity and size of the model. 

• Highlights the strengths of Bayesian networks whilst also presenting a discussion of their limitations.

• Compares Bayesian networks with other modelling techniques such as neural networks, fuzzy logic and fault trees.

• Describes, for ease of comparison, the main features of the major Bayesian network software packages: Netica, Hugin, Elvira and Discoverer, from the point of view of the user.

• Offers a historical perspective on the subject and analyses future directions for research.

Written by leading experts with practical experience of applying Bayesian networks in finance, banking, medicine, robotics, civil engineering, geology, geography, genetics, forensic science, ecology, and industry, the book has much to offer both practitioners and researchers involved in statistical analysis or modelling in any of these fields.

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Six Sigma Statistics with EXCEL and MINITAB

Six Sigma Statistics with EXCEL and MINITAB Review

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Master the Statistical Techniques for Six Sigma Operations, While Boosting Your Excel and Minitab Skills!

Now with the help of this “one-stop” resource, operations and production managers can learn all the powerful statistical techniques for Six Sigma operations, while becoming proficient at Excel and Minitab at the same time.

Six Sigma Statistics with Excel and Minitab offers a complete guide to Six Sigma statistical methods, plus expert coverage of Excel and Minitab, two of today's most popular programs for statistical analysis and data visualization.

Written by a seasoned Six Sigma Master Black Belt, the book explains how to create and interpret dot plots, histograms, and box plots using Minitab…decide on sampling strategies, sample size, and confidence intervals…apply hypothesis tests to compare variance, means, and proportions…conduct a regression and residual analysis…design and analyze an experiment…and much more.

Filled with clear, concise accounts of the theory for each statistical method presented, Six Sigma Statistics with Excel and Minitab features:

• Easy-to-follow explanations of powerful Six Sigma tools
• A wealth of exercises and case studies
• 200 graphical illustrations for Excel and Minitab

Essential for achieving Six Sigma goals in any organization, Six Sigma Statistics with Excel and Minitab is a unique, skills-building toolkit for mastering a wide range of vital statistical techniques, and for capitalizing on the potential of Excel and Minitab.

Six Sigma Statistical with Excel and Minitab offers operations and production managers a complete guide to Six Sigma statistical techniques, together with expert coverage of Excel and Minitab, two of today's most popular programs for statistical analysis and data visualization.

Written by Issa Bass, a Six Sigma Master Black Belt with years of hands-on experience in industry, this on-target resource takes readers through the application of each Six Sigma statistical tool, while presenting a straightforward tutorial for effectively utilizing Excel and Minitab. With the help of this essential reference, managers can:

• Acquire the basic tools for data collection, organization, and description
• Learn the fundamental principles of probability
• Create and interpret dot plots, histograms, and box plots using Minitab
• Decide on sampling strategies, sample size, and confidence intervals
• Apply hypothesis tests to compare variance, means, and proportions
• Stay on top of production processes with statistical process control
• Use process capability analysis to ensure that processes meet customers' expectations
• Employ analysis of variance to make inferences about more than two population means
• Conduct a regression and residual analysis
• Design and analyze an experiment

In addition, Six Sigma Statistics with Excel and Minitab enables you to develop a better understanding of the Taguchi Method…use measurement system analysis to find out if measurement processes are accurate…discover how to test ordinal or nominal data with nonparametric statistics…and apply the full range of basic quality tools.

Filled with step-by-step exercises, graphical illustrations, and screen shots for performing Six Sigma techniques on Excel and Minitab, the book also provides clear, concise explanations of the theory for each of the statistical tools presented.

Authoritative and comprehensive, Six Sigma Statistics with Excel and Minitab is a valuable skills-building resource for mastering all the statistical techniques for Six Sigma operations, while harnessing the power of Excel and Minitab.

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Application and Program Performance Analysis Using PEX Statistics on IBM I5/Os

Application and Program Performance Analysis Using PEX Statistics on IBM I5/Os Review

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Operational Risk Management: A Practical Approach to Intelligent Data Analysis (Statistics in Practice)

Operational Risk Management: A Practical Approach to Intelligent Data Analysis (Statistics in Practice) Review

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Models and methods for operational risks assessment and mitigation are gaining importance in financial institutions, healthcare organizations, industry, businesses and organisations in general. This book introduces modern Operational Risk Management and describes how various data sources of different types, both numeric and semantic sources such as text can be integrated and analyzed. The book also demonstrates how Operational Risk Management is synergetic to other risk management activities such as Financial Risk Management and Safety Management.

Operational Risk Management: a practical approach to intelligent data analysis provides practical and tested methodologies for combining structured and unstructured, semantic-based data, and numeric data, in Operational Risk Management (OpR) data analysis.

Key Features:

• The book is presented in four parts: 1) Introduction to OpR Management, 2) Data for OpR Management, 3) OpR Analytics and 4) OpR Applications and its Integration with other Disciplines.
• Explores integration of semantic, unstructured textual data, in Operational Risk Management.
• Provides novel techniques for combining qualitative and quantitative information to assess risks and design mitigation strategies.
• Presents a comprehensive treatment of "near-misses" data and incidents in Operational Risk Management.
• Looks at case studies in the financial and industrial sector.
• Discusses application of ontology engineering to model knowledge used in Operational Risk Management.

Many real life examples are presented, mostly based on the MUSING project co-funded by the EU FP6 Information Society Technology Programme. It provides a unique multidisciplinary perspective on the important and evolving topic of Operational Risk Management. The book will be useful to operational risk practitioners, risk managers in banks, hospitals and industry looking for modern approaches to risk management that combine an analysis of structured and unstructured data. The book will also benefit academics interested in research in this field, looking for techniques developed in response to real world problems.

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Survival and Event History Analysis: A Process Point of View (Statistics for Biology and Health)

Survival and Event History Analysis: A Process Point of View (Statistics for Biology and Health) Review

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Survival and Event History Analysis: A Process Point of View (Statistics for Biology and Health) Feature

The aim of this book is to bridge the gap between standard textbook models and a range of models where the dynamic structure of the data manifests itself fully. The common denominator of such models is stochastic processes. The authors show how counting processes, martingales, and stochastic integrals fit very nicely with censored data. Beginning with standard analyses such as Kaplan-Meier plots and Cox regression, the presentation progresses to the additive hazard model and recurrent event data. Stochastic processes are also used as natural models for individual frailty; they allow sensible interpretations of a number of surprising artifacts seen in population data. The stochastic process framework is naturally connected to causality. The authors show how dynamic path analyses can incorporate many modern causality ideas in a framework that takes the time aspect seriously. To make the material accessible to the reader, a large number of practical examples, mainly from medicine, are developed in detail. Stochastic processes are introduced in an intuitive and non-technical manner. The book is aimed at investigators who use event history methods and want a better understanding of the statistical concepts. It is suitable as a textbook for graduate courses in statistics and biostatistics.

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