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Equivalence and Noninferiority Tests for Quality Manufacturing and Test Engineers

Equivalence and Noninferiority Tests for Quality Manufacturing and Test Engineers

In engineering and quality control various situations including process validation and design verification require equivalence and noninferiority tests. Equivalence and Noninferiority Tests for Quality Manufacturing and Test Engineers presents methods for using validation and verification test data to demonstrate equivalence and noninferiority in engineering and applied science. The book covers numerous tests drawn from the author’s more than 30 years of work in a range of industrial settings. It provides computational formulas for the tests methods to determine or justify sample sizes and formulas to calculate power and operating characteristic curves. The methods are accessible using standard statistical software and do not require complicated programming. The book also includes computer code and screen shots for SAS R and JMP. This book provides you with a guide to performing validation and verification tests that demonstrate the adequacy of your process system or product. It will help you choose the best test for your application.

GBP 59.99
1

Nonparametric Statistical Tests A Computational Approach

Nonparametric Statistical Tests A Computational Approach

Nonparametric Statistical Tests: A Computational Approach describes classical nonparametric tests as well as novel and little-known methods such as the Baumgartner-Weiss-Schindler and the Cucconi tests. The book presents SAS and R programs allowing readers to carry out the different statistical methods such as permutation and bootstrap tests. The author considers example data sets in each chapter to illustrate methods. Numerous real-life data from various areas including the bible and their analyses provide for greatly diversified reading. The book covers: Nonparametric two-sample tests for the location-shift model specifically the Fisher-Pitman permutation test the Wilcoxon rank sum test and the Baumgartner-Weiss-Schindler test Permutation tests location-scale tests tests for the nonparametric Behrens-Fisher problem and tests for a difference in variability Tests for the general alternative including the (Kolmogorov-)Smirnov test ordered categorical and discrete numerical data Well-known one-sample tests such as the sign test and Wilcoxon’s signed rank test a modification suggested by Pratt (1959) a permutation test with original observations and a one-sample bootstrap test are presented. Tests for more than two groups the following tests are described in detail: the Kruskal-Wallis test the permutation F test the Jonckheere-Terpstra trend test tests for umbrella alternatives and the Friedman and Page tests for multiple dependent groups The concepts of independence and correlation and stratified tests such as the van Elteren test and combination tests The applicability of computer-intensive methods such as bootstrap and permutation tests for non-standard situations and complex designs Although the major development of nonparametric methods came to a certain end in the 1970s their importance undoubtedly persists. What is still needed is a computer assisted evaluation of their main properties. This book closes that gap. | Nonparametric Statistical Tests A Computational Approach

GBP 69.99
1

Statistical Inference Based on Divergence Measures

Statistical Inference Based on Divergence Measures

The idea of using functionals of Information Theory such as entropies or divergences in statistical inference is not new. However in spite of the fact that divergence statistics have become a very good alternative to the classical likelihood ratio test and the Pearson-type statistic in discrete models many statisticians remain unaware of this powerful approach. Statistical Inference Based on Divergence Measures explores classical problems of statistical inference such as estimation and hypothesis testing on the basis of measures of entropy and divergence. The first two chapters form an overview from a statistical perspective of the most important measures of entropy and divergence and study their properties. The author then examines the statistical analysis of discrete multivariate data with emphasis is on problems in contingency tables and loglinear models using phi-divergence test statistics as well as minimum phi-divergence estimators. The final chapter looks at testing in general populations presenting the interesting possibility of introducing alternative test statistics to classical ones like Wald Rao and likelihood ratio. Each chapter concludes with exercises that clarify the theoretical results and present additional results that complement the main discussions. Clear comprehensive and logically developed this book offers a unique opportunity to gain not only a new perspective on some standard statistics problems but the tools to put it into practice.

GBP 44.99
1

Handbook of Educational Measurement and Psychometrics Using R

Handbook of Educational Measurement and Psychometrics Using R

Currently there are many introductory textbooks on educational measurement and psychometrics as well as R. However there is no single book that covers important topics in measurement and psychometrics as well as their applications in R. The Handbook of Educational Measurement and Psychometrics Using R covers a variety of topics including classical test theory; generalizability theory; the factor analytic approach in measurement; unidimensional multidimensional and explanatory item response modeling; test equating; visualizing measurement models; measurement invariance; and differential item functioning. This handbook is intended for undergraduate and graduate students researchers and practitioners as a complementary book to a theory-based introductory or advanced textbook in measurement. Practitioners and researchers who are familiar with the measurement models but need to refresh their memory and learn how to apply the measurement models in R would find this handbook quite fulfilling. Students taking a course on measurement and psychometrics will find this handbook helpful in applying the methods they are learning in class. In addition instructors teaching educational measurement and psychometrics will find our handbook as a useful supplement for their course.

GBP 44.99
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Basic Statistics and Pharmaceutical Statistical Applications

Basic Statistics and Pharmaceutical Statistical Applications

Building on its best-selling predecessors Basic Statistics and Pharmaceutical Statistical Applications Third Edition covers statistical topics most relevant to those in the pharmaceutical industry and pharmacy practice. It focuses on the fundamentals required to understand descriptive and inferential statistics for problem solving. Incorporating new material in virtually every chapter this third edition now provides information on software applications to assist with evaluating data. New to the Third EditionUse of Excel® and Minitab® for performing statistical analysisDiscussions of nonprobability sampling procedures determining if data is normally distributed evaluation of covariances and testing for precision equivalenceExpanded sections on regression analysis chi square tests tests for trends with ordinal data and tests related to survival statistics Additional nonparametric procedures including the one-sided sign test Wilcoxon signed-ranks test and Mood’s median testWith the help of flow charts and tables the author dispels some of the anxiety associated with using basic statistical tests in the pharmacy profession and helps readers correctly interpret their results using statistical software. Through the text’s worked-out examples readers better understand how the mathematics works the logic behind many of the equations and the tests’ outcomes.

GBP 44.99
1

Theory of Statistical Inference

Theory of Statistical Inference

Theory of Statistical Inference is designed as a reference on statistical inference for researchers and students at the graduate or advanced undergraduate level. It presents a unified treatment of the foundational ideas of modern statistical inference and would be suitable for a core course in a graduate program in statistics or biostatistics. The emphasis is on the application of mathematical theory to the problem of inference leading to an optimization theory allowing the choice of those statistical methods yielding the most efficient use of data. The book shows how a small number of key concepts such as sufficiency invariance stochastic ordering decision theory and vector space algebra play a recurring and unifying role. The volume can be divided into four sections. Part I provides a review of the required distribution theory. Part II introduces the problem of statistical inference. This includes the definitions of the exponential family invariant and Bayesian models. Basic concepts of estimation confidence intervals and hypothesis testing are introduced here. Part III constitutes the core of the volume presenting a formal theory of statistical inference. Beginning with decision theory this section then covers uniformly minimum variance unbiased (UMVU) estimation minimum risk equivariant (MRE) estimation and the Neyman-Pearson test. Finally Part IV introduces large sample theory. This section begins with stochastic limit theorems the δ-method the Bahadur representation theorem for sample quantiles large sample U-estimation the Cramér-Rao lower bound and asymptotic efficiency. A separate chapter is then devoted to estimating equation methods. The volume ends with a detailed development of large sample hypothesis testing based on the likelihood ratio test (LRT) Rao score test and the Wald test. Features This volume includes treatment of linear and nonlinear regression models ANOVA models generalized linear models (GLM) and generalized estimating equations (GEE). An introduction to decision theory (including risk admissibility classification Bayes and minimax decision rules) is presented. The importance of this sometimes overlooked topic to statistical methodology is emphasized. The volume emphasizes throughout the important role that can be played by group theory and invariance in statistical inference. Nonparametric (rank-based) methods are derived by the same principles used for parametric models and are therefore presented as solutions to well-defined mathematical problems rather than as robust heuristic alternatives to parametric methods. Each chapter ends with a set of theoretical and applied exercises integrated with the main text. Problems involving R programming are included. Appendices summarize the necessary background in analysis matrix algebra and group theory.

GBP 99.99
1

Linux The Textbook Second Edition

Linux The Textbook Second Edition

Choosen by BookAuthority as one of BookAuthority's Best Linux Mint Books of All TimeLinux: The Textbook Second Edition provides comprehensive coverage of the contemporary use of the Linux operating system for every level of student or practitioner from beginners to advanced users. The text clearly illustrates system-specific commands and features using Debian-family Debian Ubuntu and Linux Mint and RHEL-family CentOS and stresses universal commands and features that are critical to all Linux distributions. The second edition of the book includes extensive updates and new chapters on system administration for desktop stand-alone PCs and server-class computers; API for system programming including thread programming with pthreads; virtualization methodologies; and an extensive tutorial on systemd service management. Brand new online content on the CRC Press website includes an instructor’s workbook test bank and In-Chapter exercise solutions as well as full downloadable chapters on Python Version 3. 5 programming ZFS TC shell programming advanced system programming and more. An author-hosted GitHub website also features updates further references and errata. Features New or updated coverage of file system sorting regular expressions directory and file searching file compression and encryption shell scripting system programming client-server–based network programming thread programming with pthreads and system administration Extensive in-text pedagogy including chapter objectives student projects and basic and advanced student exercises for every chapter Expansive electronic downloads offer advanced content on Python ZFS TC shell scripting advanced system programming internetworking with Linux TCP/IP and many more topics all featured on the CRC Press website Downloadable test bank work book and solutions available for instructors on the CRC Press website Author-maintained GitHub repository provides other resources such as live links to further references updates and errata | Linux The Textbook Second Edition

GBP 38.99
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Grid Computing Techniques and Applications

Grid Computing Techniques and Applications

Designed for senior undergraduate and first-year graduate students Grid Computing: Techniques and Applications shows professors how to teach this subject in a practical way. Extensively classroom-tested it covers job submission and scheduling Grid security Grid computing services and software tools graphical user interfaces workflow editors and Grid-enabling applications. The book begins with an introduction that discusses the use of a Grid computing Web-based portal. It then examines the underlying action of job submission using a command-line interface and the use of a job scheduler. After describing both general Internet security techniques and specific security mechanisms developed for Grid computing the author focuses on Web services technologies and how they are adopted for Grid computing. He also discusses the advantages of using a graphical user interface over a command-line interface and presents a graphical workflow editor that enables users to compose sequences of computational tasks visually using a simple drag-and-drop interface. The final chapter explains how to deploy applications on a Grid. The Grid computing platform offers much more than simply running an application at a remote site. It also enables multiple geographically distributed computers to collectively obtain increased speed and fault tolerance. Illustrating this kind of resource discovery this practical text encompasses the varied and interconnected aspects of Grid computing including how to design a system infrastructure and Grid portal. Supplemental Web ResourcesThe author’s Web site offers various instructional resources including slides and links to software for programming assignments. Many of these assignments do not require access to a Grid platform. Instead the author provides step-by-step instructions for installing open-source software to deploy and test Web and Grid services a Grid computing workflow editor to design and test workflows and a Grid computing portal to deploy portlets. | Grid Computing Techniques and Applications

GBP 69.99
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Basic Matrix Algebra with Algorithms and Applications

Basic Matrix Algebra with Algorithms and Applications

Clear prose tight organization and a wealth of examples and computational techniques make Basic Matrix Algebra with Algorithms and Applications an outstanding introduction to linear algebra. The author designed this treatment specifically for freshman majors in mathematical subjects and upper-level students in natural resources the social sciences business or any discipline that eventually requires an understanding of linear models. With extreme pedagogical clarity that avoids abstraction wherever possible the author emphasizes minimal polynomials and their computation using a Krylov algorithm. The presentation is highly visual and relies heavily on work with a graphing calculator to allow readers to focus on concepts and techniques rather than on tedious arithmetic. Supporting materials including test preparation Maple worksheets are available for download from the Internet. This unassuming but insightful and remarkably original treatment is organized into bite-sized clearly stated objectives. It goes well beyond the LACSG recommendations for a first course while still implementing their philosophy and core material. Classroom tested with great success it prepares readers well for the more advanced studies their fields ultimately will require.

GBP 175.00
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Introduction to Biological Networks

Introduction to Biological Networks

The new research area of genomics-inspired network biology lacks an introductory book that enables both physical/computational scientists and biologists to obtain a general yet sufficiently rigorous perspective of current thinking. Filling this gap Introduction to Biological Networks provides a thorough introduction to genomics-inspired network biology for physical scientists and biologists involved in interdisciplinary research. The book focuses on the concept of molecular and genetic interaction networks as a paradigm for interpreting the complexity of molecular biology at a genomic scale. The authors describe the experimental methods used to discover and test networks of interaction among biological molecules. They also present computational methods for predicting the interaction networks discuss general mechanisms of network formation and evolution and explore the application of network approaches to important problems in biology and medicine. With many examples throughout and clear explanations of key concepts this book is the first to offer a broad treatment of genomics-inspired network biology with sufficient mathematical and biological rigor. It gives readers a conceptual understanding of this burgeoning scientific field.

GBP 59.99
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Linear Regression Models Applications in R

Linear Regression Models Applications in R

Research in social and behavioral sciences has benefited from linear regression models (LRMs) for decades to identify and understand the associations among a set of explanatory variables and an outcome variable. Linear Regression Models: Applications in R provides you with a comprehensive treatment of these models and indispensable guidance about how to estimate them using the R software environment. After furnishing some background material the author explains how to estimate simple and multiple LRMs in R including how to interpret their coefficients and understand their assumptions. Several chapters thoroughly describe these assumptions and explain how to determine whether they are satisfied and how to modify the regression model if they are not. The book also includes chapters on specifying the correct model adjusting for measurement error understanding the effects of influential observations and using the model with multilevel data. The concluding chapter presents an alternative model—logistic regression—designed for binary or two-category outcome variables. The book includes appendices that discuss data management and missing data and provides simulations in R to test model assumptions. Features Furnishes a thorough introduction and detailed information about the linear regression model including how to understand and interpret its results test assumptions and adapt the model when assumptions are not satisfied. Uses numerous graphs in R to illustrate the model’s results assumptions and other features. Does not assume a background in calculus or linear algebra rather an introductory statistics course and familiarity with elementary algebra are sufficient. Provides many examples using real-world datasets relevant to various academic disciplines. Fully integrates the R software environment in its numerous examples. The book is aimed primarily at advanced undergraduate and graduate students in social behavioral health sciences and related disciplines taking a first course in linear regression. It could also be used for self-study and would make an excellent reference for any researcher in these fields. The R code and detailed examples provided throughout the book equip the reader with an excellent set of tools for conducting research on numerous social and behavioral phenomena. John P. Hoffmann is a professor of sociology at Brigham Young University where he teaches research methods and applied statistics courses and conducts research on substance use and criminal behavior. | Linear Regression Models Applications in R

GBP 66.99
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A Criminologist's Guide to R Crime by the Numbers

A Criminologist's Guide to R Crime by the Numbers

A Criminologist's Guide to R: Crime by the Numbers introduces the programming language R and covers the necessary skills to conduct quantitative research in criminology. By the end of this book a person without any prior programming experience can take raw crime data be able to clean it visualize the data present it using R Markdown and change it to a format ready for analysis. A Criminologist's Guide to R focuses on skills specifically for criminology such as spatial joins mapping and scraping data from PDFs however any social scientist looking for an introduction to R for data analysis will find this useful. Key Features: Introduction to RStudio including how to change user preference settings. Basic data exploration and cleaning – subsetting loading data regular expressions aggregating data. Graphing with ggplot2. How to make maps (hotspot maps choropleth maps interactive maps). Webscraping and PDF scraping. Project management – how to prepare for a project how to decide which projects to do best ways to collaborate with people how to store your code (using git) and how to test your code. | A Criminologist's Guide to R Crime by the Numbers

GBP 74.99
1

Polynomial Completeness in Algebraic Systems

Polynomial Completeness in Algebraic Systems

Boolean algebras have historically played a special role in the development of the theory of general or universal algebraic systems providing important links between algebra and analysis set theory mathematical logic and computer science. It is not surprising then that focusing on specific properties of Boolean algebras has lead to new directions in universal algebra. In the first unified study of polynomial completeness Polynomial Completeness in Algebraic Systems focuses on and systematically extends another specific property of Boolean algebras: the property of affine completeness. The authors present full proof that all affine complete varieties are congruence distributive and that they are finitely generated if and only if they can be presented using only a finite number of basic operations. In addition to these important findings the authors describe the different relationships between the properties of lattices of equivalence relations and the systems of functions compatible with them. An introductory chapter surveys the appropriate background material exercises in each chapter allow readers to test their understanding and open problems offer new research possibilities. Thus Polynomial Completeness in Algebraic Systems constitutes an accessible coherent presentation of this rich topic valuable to both researchers and graduate students in general algebraic systems.

GBP 59.99
1

Statistics in Toxicology Using R

Statistics in Toxicology Using R

The apparent contradiction between statistical significance and biological relevance has diminished the value of statistical methods as a whole in toxicology. Moreover recommendations for statistical analysis are imprecise in most toxicological guidelines. Addressing these dilemmas Statistics in Toxicology Using R explains the statistical analysis of selected experimental data in toxicology and presents assay-specific suggestions such as for the in vitro micronucleus assay. Mostly focusing on hypothesis testing the book covers standardized bioassays for chemicals drugs and environmental pollutants. It is organized according to selected toxicological assays including: Short-term repeated toxicity studiesLong-term carcinogenicity assays Studies on reproductive toxicity Mutagenicity assays Toxicokinetic studies The book also discusses proof of safety (particularly in ecotoxicological assays) toxicogenomics the analysis of interlaboratory studies and the modeling of dose-response relationships for risk assessment. For each toxicological problem the author describes the statistics involved matching data example R code and outcomes and their interpretation. This approach allows you to select a certain bioassay identify the specific data structure run the R code with the data example understand the test outcome and interpretation and replace the data set with your own data and run again.

GBP 44.99
1

Introduction to Hierarchical Bayesian Modeling for Ecological Data

Introduction to Hierarchical Bayesian Modeling for Ecological Data

Making statistical modeling and inference more accessible to ecologists and related scientists Introduction to Hierarchical Bayesian Modeling for Ecological Data gives readers a flexible and effective framework to learn about complex ecological processes from various sources of data. It also helps readers get started on building their own statistical models. The text begins with simple models that progressively become more complex and realistic through explanatory covariates and intermediate hidden states variables. When fitting the models to data the authors gradually present the concepts and techniques of the Bayesian paradigm from a practical point of view using real case studies. They emphasize how hierarchical Bayesian modeling supports multidimensional models involving complex interactions between parameters and latent variables. Data sets exercises and R and WinBUGS codes are available on the authors’ website. This book shows how Bayesian statistical modeling provides an intuitive way to organize data test ideas investigate competing hypotheses and assess degrees of confidence of predictions. It also illustrates how conditional reasoning can dismantle a complex reality into more understandable pieces. As conditional reasoning is intimately linked with Bayesian thinking considering hierarchical models within the Bayesian setting offers a unified and coherent framework for modeling estimation and prediction.

GBP 44.99
1

An Advanced Course in Probability and Stochastic Processes

An Advanced Course in Probability and Stochastic Processes

An Advanced Course in Probability and Stochastic Processes provides a modern and rigorous treatment of probability theory and stochastic processes at an upper undergraduate and graduate level. Starting with the foundations of measure theory this book introduces the key concepts of probability theory in an accessible way providing full proofs and extensive examples and illustrations. Fundamental stochastic processes such as Gaussian processes Poisson random measures Lévy processes Markov processes and Itô processes are presented and explored in considerable depth showcasing their many interconnections. Special attention is paid to martingales and the Wiener process and their central role in the treatment of stochastic integrals and stochastic calculus. This book includes many exercises designed to test and challenge the reader and expand their skillset. An Advanced Course in Probability and Stochastic Processes is meant for students and researchers who have a solid mathematical background and who have had prior exposure to elementary probability and stochastic processes. Key Features: Focus on mathematical understanding Rigorous and self-contained Accessible and comprehensive High-quality illustrations Includes essential simulation algorithms Extensive list of exercises and worked-out examples Elegant and consistent notation

GBP 89.99
1

Basketball Data Science With Applications in R

Basketball Data Science With Applications in R

Using data from one season of NBA games Basketball Data Science: With Applications in R is the perfect book for anyone interested in learning and applying data analytics in basketball. Whether assessing the spatial performance of an NBA player's shots or doing an analysis of the impact of high pressure game situations on the probability of scoring this book discusses a variety of case studies and hands-on examples using a custom R package. The codes are supplied so readers can reproduce the analyses themselves or create their own. Assuming a basic statistical knowledge Basketball Data Science with R is suitable for students technicians coaches data analysts and applied researchers. Features: One of the first books to provide statistical and data mining methods for the growing field of analytics in basketball Presents tools for modelling graphs and figures to visualize the data Includes real world case studies and examples such as estimations of scoring probability using the Golden State Warriors as a test case Provides the source code and data so readers can do their own analyses on NBA teams and players | Basketball Data Science With Applications in R

GBP 48.99
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Design and Analysis of Experiments Classical and Regression Approaches with SAS

Design and Analysis of Experiments Classical and Regression Approaches with SAS

Unlike other books on the modeling and analysis of experimental data Design and Analysis of Experiments: Classical and Regression Approaches with SAS not only covers classical experimental design theory it also explores regression approaches. Capitalizing on the availability of cutting-edge software the author uses both manual methods and SAS programs to carry out analyses. The book presents most of the different designs covered in a typical experimental design course. It discusses the requirements for good experimentation the completely randomized design the use of orthogonal contrast to test hypotheses and the model adequacy check. With an emphasis on two-factor factorial experiments the author analyzes repeated measures as well as fixed random and mixed effects models. He also describes designs with randomization restrictions before delving into the special cases of the 2k and 3k factorial designs including fractional replication and confounding. In addition the book covers response surfaces balanced incomplete block and hierarchical designs ANOVA ANCOVA and MANOVA. Fortifying the theory and computations with practical exercises and supplemental material this distinctive text provides a modern comprehensive treatment of experimental design and analysis. | Design and Analysis of Experiments Classical and Regression Approaches with SAS

GBP 59.99
1

Mobile Crowdsensing

Mobile Crowdsensing

Mobile crowdsensing is a technology that allows large scale cost-effective sensing of the physical world. In mobile crowdsensing mobile personal devices such as smart phones or smart watches come equipped with a variety of sensors that can be leveraged to collect data related to environment transportation healthcare safety and so on. This book presents the first extensive coverage of mobile crowdsensing with examples and insights drawn from the authors’ extensive research on this topic as well as from the research and development of a growing community of researchers and practitioners working in this emerging field. Throughout the text the authors provide the reader with various examples of crowdsensing applications and the building blocks to creating the necessary infrastructure explore the related concepts of mobile sensing and crowdsourcing and examine security and privacy issues introduced by mobile crowdsensing platforms. Provides a comprehensive description of mobile crowdsensing a one-stop shop for all relevant issues pertaining to mobile crowdsensing including motivation applications design and implementation incentive mechanisms and reliability and privacy. Describes the design and implementations of mobile crowdsensing platforms of great interest for the readers working in research and industry to quickly implement and test their systems. Identifies potential issues in building such mobile crowdsensing applications to ensure their usability in real life and presents future directions in mobile crowdsensing by emphasizing the open problems that have to be addressed.

GBP 44.99
1

Python Packages

Python Packages

Python Packages introduces Python packaging at an introductory and practical level that’s suitable for those with no previous packaging experience. Despite this the text builds up to advanced topics such as automated testing creating documentation versioning and updating a package and implementing continuous integration and deployment. Covering the entire Python packaging life cycle this essential guide takes readers from package creation all the way to effective maintenance and updating. Python Packages focuses on the use of current and best-practice packaging tools and services like poetry cookiecutter pytest sphinx GitHub and GitHub Actions. Features: The book’s source code is available online as a GitHub repository where it is collaborated on automatically tested and built in real time as changes are made; demonstrating the use of good reproducible and clear project workflows. Covers not just the process of creating a package but also how to document it test it publish it to the Python Package Index (PyPI) and how to properly version and update it. All concepts in the book are demonstrated using examples. Readers can follow along creating their own Python packages using the reproducible code provided in the text. Focuses on a modern approach to Python packaging with emphasis on automating and streamlining the packaging process using new and emerging tools such as poetry and GitHub Actions.

GBP 35.99
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Elementary Number Theory

Elementary Number Theory

Elementary Number Theory Gove Effinger Gary L. Mullen This text is intended to be used as an undergraduate introduction to the theory of numbers. The authors have been immersed in this area of mathematics for many years and hope that this text will inspire students (and instructors) to study understand and come to love this truly beautiful subject. Each chapter after an introduction develops a new topic clearly broken out in sections which include theoretical material together with numerous examples each worked out in considerable detail. At the end of each chapter after a summary of the topic there are a number of solved problems also worked out in detail followed by a set of supplementary problems. These latter problems give students a chance to test their own understanding of the material; solutions to some but not all of them complete the chapter. The first eight chapters discuss some standard material in elementary number theory. The remaining chapters discuss topics which might be considered a bit more advanced. The text closes with a chapter on Open Problems in Number Theory. Students (and of course instructors) are strongly encouraged to study this chapter carefully and fully realize that not all mathematical issues and problems have been resolved! There is still much to be learned and many questions to be answered in mathematics in general and in number theory in particular.

GBP 48.99
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Principles of Biostatistics

Principles of Biostatistics

Principles of Biostatistics Third Edition is a concepts-based introduction to statistical procedures that prepares public health medical and life sciences students to conduct and evaluate research. With an engaging writing style and helpful graphics the emphasis is on concepts over formulas or rote memorization. Throughout the book the authors use practical interesting examples with real data to bring the material to life. Thoroughly revised and updated this third edition includes a new chapter introducing the basic principles of Study Design as well as new sections on sample size calculations for two-sample tests on means and proportions the Kruskal-Wallis test and the Cox proportional hazards model. Key Features: Includes a new chapter on the basic principles of study design. Additional review exercises have been added to each chapter. Datasets and Stata and R code are available on the book’s website. The book is divided into three parts. The first five chapters deal with collections of numbers and ways in which to summarize explore and explain them. The next two chapters focus on probability and introduce the tools needed for the subsequent investigation of uncertainty. It is only in the eighth chapter and thereafter that the authors distinguish between populations and samples and begin to investigate the inherent variability introduced by sampling thus progressing to inference. Postponing the slightly more difficult concepts until a solid foundation has been established makes it easier for the reader to comprehend them.

GBP 74.99
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Deep Learning A Comprehensive Guide

Deep Learning A Comprehensive Guide

Deep Learning: A Comprehensive Guide provides comprehensive coverage of Deep Learning (DL) and Machine Learning (ML) concepts. DL and ML are the most sought-after domains requiring a deep understanding – and this book gives no less than that. This book enables the reader to build innovative and useful applications based on ML and DL. Starting with the basics of neural networks and continuing through the architecture of various types of CNNs RNNs LSTM and more till the end of the book each and every topic is given the utmost care and shaped professionally and comprehensively. Key Features Includes the smooth transition from ML concepts to DL concepts Line-by-line explanations have been provided for all the coding-based examples Includes a lot of real-time examples and interview questions that will prepare the reader to take up a job in ML/DL right away Even a person with a non-computer-science background can benefit from this book by following the theory examples case studies and code snippets Every chapter starts with the objective and ends with a set of quiz questions to test the reader’s understanding Includes references to the related YouTube videos that provide additional guidance AI is a domain for everyone. This book is targeted toward everyone irrespective of their field of specialization. Graduates and researchers in deep learning will find this book useful. | Deep Learning A Comprehensive Guide

GBP 130.00
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Exposure-Response Modeling Methods and Practical Implementation

Exposure-Response Modeling Methods and Practical Implementation

Discover the Latest Statistical Approaches for Modeling Exposure-Response RelationshipsWritten by an applied statistician with extensive practical experience in drug development Exposure-Response Modeling: Methods and Practical Implementation explores a wide range of topics in exposure-response modeling from traditional pharmacokinetic-pharmacodynamic (PKPD) modeling to other areas in drug development and beyond. It incorporates numerous examples and software programs for implementing novel methods. The book describes using measurement error models to treat sequential modeling fitting models with exposure and response driven by complex dynamics and survival analysis with dynamic exposure history. It also covers Bayesian analysis and model-based Bayesian decision analysis causal inference to eliminate confounding biases and exposure-response modeling with response-dependent dose/treatment adjustments (dynamic treatment regimes) for personalized medicine and treatment adaptation. Many examples illustrate the use of exposure-response modeling in experimental toxicology clinical pharmacology epidemiology and drug safety. Some examples demonstrate how to solve practical problems while others help with understanding concepts and evaluating the performance of new methods. The provided SAS and R codes enable readers to test the approaches in their own scenarios. Although application oriented this book also gives a systematic treatment of concepts and methodology. Applied statisticians and modelers can find details on how to implement new approaches. Researchers can find topics for or applications of their work. In addition students can see how complicated methodology and models are applied to practical situations. | Exposure-Response Modeling Methods and Practical Implementation

GBP 44.99
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Computational Aspects of Polynomial Identities Volume l Kemer's Theorems 2nd Edition

Computational Aspects of Polynomial Identities Volume l Kemer's Theorems 2nd Edition

Computational Aspects of Polynomial Identities: Volume l Kemer’s Theorems 2nd Edition presents the underlying ideas in recent polynomial identity (PI)-theory and demonstrates the validity of the proofs of PI-theorems. This edition gives all the details involved in Kemer’s proof of Specht’s conjecture for affine PI-algebras in characteristic 0. The book first discusses the theory needed for Kemer’s proof including the featured role of Grassmann algebra and the translation to superalgebras. The authors develop Kemer polynomials for arbitrary varieties as tools for proving diverse theorems. They also lay the groundwork for analogous theorems that have recently been proved for Lie algebras and alternative algebras. They then describe counterexamples to Specht’s conjecture in characteristic p as well as the underlying theory. The book also covers Noetherian PI-algebras Poincaré–Hilbert series Gelfand–Kirillov dimension the combinatoric theory of affine PI-algebras and homogeneous identities in terms of the representation theory of the general linear group GL. Through the theory of Kemer polynomials this edition shows that the techniques of finite dimensional algebras are available for all affine PI-algebras. It also emphasizes the Grassmann algebra as a recurring theme including in Rosset’s proof of the Amitsur–Levitzki theorem a simple example of a finitely based T-ideal the link between algebras and superalgebras and a test algebra for counterexamples in characteristic p. | Computational Aspects of Polynomial Identities Volume l Kemer's Theorems 2nd Edition

GBP 59.99
1