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Using the R Commander A Point-and-Click Interface for R

Using the R Commander A Point-and-Click Interface for R

This book provides a general introduction to the R Commander graphical user interface (GUI) to R for readers who are unfamiliar with R. It is suitable for use as a supplementary text in a basic or intermediate-level statistics course. It is not intended to replace a basic or other statistics text but rather to complement it although it does promote sound statistical practice in the examples. The book should also be useful to individual casual or occasional users of R for whom the standard command-line interface is an obstacle. tinyurl. com/RcmdrBookThe site includes data files used in the book and an errata list. http://socserv. mcmaster. ca/jfox/Books/RCommander/Writing-Rcmdr-Plugins. pdf Writing R Commander Plug-in Packages | Using the R Commander A Point-and-Click Interface for R

GBP 175.00
1

Computational Methods for Numerical Analysis with R

Computational Methods for Numerical Analysis with R

Computational Methods for Numerical Analysis with R is an overview of traditional numerical analysis topics presented using R. This guide shows how common functions from linear algebra interpolation numerical integration optimization and differential equations can be implemented in pure R code. Every algorithm described is given with a complete function implementation in R along with examples to demonstrate the function and its use. Computational Methods for Numerical Analysis with R is intended for those who already know R but are interested in learning more about how the underlying algorithms work. As such it is suitable for statisticians economists and engineers and others with a computational and numerical background.

GBP 48.99
1

AI by Design A Plan for Living with Artificial Intelligence

AI by Design A Plan for Living with Artificial Intelligence

This book introduces the reader to Artificial Intelligence and its importance to our future. Campbell uses behavioural psychology explores technology economics real-life and historical examples to predict five future scenarios with AI. Illustrating through speculative fiction she describes possible futures after AI exceeds human capabilities. We are at a tipping point in history and must plan to ensure a successful co-existence with artificial intelligence. This book explains how to design for a future with AI so that rather than herald our downfall it helps us achieve a new renaissance. | AI by Design A Plan for Living with Artificial Intelligence

GBP 24.99
1

Foundations of Statistics for Data Scientists With R and Python

Foundations of Statistics for Data Scientists With R and Python

Foundations of Statistics for Data Scientists: With R and Python is designed as a textbook for a one- or two-term introduction to mathematical statistics for students training to become data scientists. It is an in-depth presentation of the topics in statistical science with which any data scientist should be familiar including probability distributions descriptive and inferential statistical methods and linear modeling. The book assumes knowledge of basic calculus so the presentation can focus on why it works as well as how to do it. Compared to traditional mathematical statistics textbooks however the book has less emphasis on probability theory and more emphasis on using software to implement statistical methods and to conduct simulations to illustrate key concepts. All statistical analyses in the book use R software with an appendix showing the same analyses with Python. Key Features: Shows the elements of statistical science that are important for students who plan to become data scientists. Includes Bayesian and regularized fitting of models (e. g. showing an example using the lasso) classification and clustering and implementing methods with modern software (R and Python). Contains nearly 500 exercises. The book also introduces modern topics that do not normally appear in mathematical statistics texts but are highly relevant for data scientists such as Bayesian inference generalized linear models for non-normal responses (e. g. logistic regression and Poisson loglinear models) and regularized model fitting. The nearly 500 exercises are grouped into Data Analysis and Applications and Methods and Concepts. Appendices introduce R and Python and contain solutions for odd-numbered exercises. The book's website (http://stat4ds. rwth-aachen. de/) has expanded R Python and Matlab appendices and all data sets from the examples and exercises. | Foundations of Statistics for Data Scientists With R and Python

GBP 82.99
1

Basics of Matrix Algebra for Statistics with R

Basics of Matrix Algebra for Statistics with R

A Thorough Guide to Elementary Matrix Algebra and Implementation in RBasics of Matrix Algebra for Statistics with R provides a guide to elementary matrix algebra sufficient for undertaking specialized courses such as multivariate data analysis and linear models. It also covers advanced topics such as generalized inverses of singular and rectangular matrices and manipulation of partitioned matrices for those who want to delve deeper into the subject. The book introduces the definition of a matrix and the basic rules of addition subtraction multiplication and inversion. Later topics include determinants calculation of eigenvectors and eigenvalues and differentiation of linear and quadratic forms with respect to vectors. The text explores how these concepts arise in statistical techniques including principal component analysis canonical correlation analysis and linear modeling. In addition to the algebraic manipulation of matrices the book presents numerical examples that illustrate how to perform calculations by hand and using R. Many theoretical and numerical exercises of varying levels of difficulty aid readers in assessing their knowledge of the material. Outline solutions at the back of the book enable readers to verify the techniques required and obtain numerical answers. Avoiding vector spaces and other advanced mathematics this book shows how to manipulate matrices and perform numerical calculations in R. It prepares readers for higher-level and specialized studies in statistics.

GBP 44.99
1

Machine Learning Toolbox for Social Scientists Applied Predictive Analytics with R

Machine Learning Toolbox for Social Scientists Applied Predictive Analytics with R

Machine Learning Toolbox for Social Scientists covers predictive methods with complementary statistical tools that make it mostly self-contained. The inferential statistics is the traditional framework for most data analytics courses in social science and business fields especially in Economics and Finance. The new organization that this book offers goes beyond standard machine learning code applications providing intuitive backgrounds for new predictive methods that social science and business students can follow. The book also adds many other modern statistical tools complementary to predictive methods that cannot be easily found in econometrics textbooks: nonparametric methods data exploration with predictive models penalized regressions model selection with sparsity dimension reduction methods nonparametric time-series predictions graphical network analysis algorithmic optimization methods classification with imbalanced data and many others. This book is targeted at students and researchers who have no advanced statistical background but instead coming from the tradition of inferential statistics. The modern statistical methods the book provides allows it to be effectively used in teaching in the social science and business fields. Key Features: The book is structured for those who have been trained in a traditional statistics curriculum. There is one long initial section that covers the differences in estimation and prediction for people trained for causal analysis. The book develops a background framework for Machine learning applications from Nonparametric methods. SVM and NN simple enough without too much detail. It’s self-sufficient. Nonparametric time-series predictions are new and covered in a separate section. Additional sections are added: Penalized Regressions Dimension Reduction Methods and Graphical Methods have been increasing in their popularity in social sciences. | Machine Learning Toolbox for Social Scientists Applied Predictive Analytics with R

GBP 74.99
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Integration of IoT with Cloud Computing for Smart Applications

Integration of IoT with Cloud Computing for Smart Applications

Integration of IoT with Cloud Computing for Smart Applications provides an integrative overview of the Internet of Things (IoT) and cloud computing to be used for the various futuristic and intelligent applications. The aim of this book is to integrate IoT and cloud computing to translate ordinary resources into smart things. Discussions in this book include a broad and integrated perspective on the collaboration security growth of cloud infrastructure and real-time data monitoring. Features: Presents an integrated approach to solve the problems related to security reliability and energy consumption. Explains a unique approach to discuss the research challenges and opportunities in the field of IoT and cloud computing. Discusses a novel approach for smart agriculture smart healthcare systems smart cities and many other modern systems based on machine learning artificial intelligence and big data etc. Information presented in a simplified way for students researchers academicians and scientists business innovators and entrepreneurs management professionals and practitioners. This book can be great reference for graduate and postgraduate students researchers and academicians working in the field of computer science cloud computing artificial intelligence etc.

GBP 99.99
1

Introduction to Bioinformatics with R A Practical Guide for Biologists

Introduction to Bioinformatics with R A Practical Guide for Biologists

In biological research the amount of data available to researchers has increased so much over recent years it is becoming increasingly difficult to understand the current state of the art without some experience and understanding of data analytics and bioinformatics. An Introduction to Bioinformatics with R: A Practical Guide for Biologists leads the reader through the basics of computational analysis of data encountered in modern biological research. With no previous experience with statistics or programming required readers will develop the ability to plan suitable analyses of biological datasets and to use the R programming environment to perform these analyses. This is achieved through a series of case studies using R to answer research questions using molecular biology datasets. Broadly applicable statistical methods are explained including linear and rank-based correlation distance metrics and hierarchical clustering hypothesis testing using linear regression proportional hazards regression for survival data and principal component analysis. These methods are then applied as appropriate throughout the case studies illustrating how they can be used to answer research questions. Key Features: · Provides a practical course in computational data analysis suitable for students or researchers with no previous exposure to computer programming. · Describes in detail the theoretical basis for statistical analysis techniques used throughout the textbook from basic principles · Presents walk-throughs of data analysis tasks using R and example datasets. All R commands are presented and explained in order to enable the reader to carry out these tasks themselves. · Uses outputs from a large range of molecular biology platforms including DNA methylation and genotyping microarrays; RNA-seq genome sequencing ChIP-seq and bisulphite sequencing; and high-throughput phenotypic screens. · Gives worked-out examples geared towards problems encountered in cancer research which can also be applied across many areas of molecular biology and medical research. This book has been developed over years of training biological scientists and clinicians to analyse the large datasets available in their cancer research projects. It is appropriate for use as a textbook or as a practical book for biological scientists looking to gain bioinformatics skills. | Introduction to Bioinformatics with R A Practical Guide for Biologists

GBP 52.99
1

Statistical Analytics for Health Data Science with SAS and R

Statistical Analytics for Health Data Science with SAS and R

This book aims to compile typical fundamental-to-advanced statistical methods to be used for health data sciences. Although the book promotes applications to health and health-related data the models in the book can be used to analyze any kind of data. The data are analyzed with the commonly used statistical software of R/SAS (with online supplementary on SPSS/Stata). The data and computing programs will be available to facilitate readers’ learning experience. There has been considerable attention to making statistical methods and analytics available to health data science researchers and students. This book brings it all together to provide a concise point-of-reference for the most commonly used statistical methods from the fundamental level to the advanced level. We envisage this book will contribute to the rapid development in health data science. We provide straightforward explanations of the collected statistical theory and models compilations of a variety of publicly available data and illustrations of data analytics using commonly used statistical software of SAS/R. We will have the data and computer programs available for readers to replicate and implement the new methods. The primary readers would be applied data scientists and practitioners in any field of data science applied statistical analysts and scientists in public health academic researchers and graduate students in statistics and biostatistics. The secondary readers would be R&D professionals/practitioners in industry and governmental agencies. This book can be used for both teaching and applied research. | Statistical Analytics for Health Data Science with SAS and R

GBP 74.99
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Probability and Statistics for Engineering and the Sciences with Modeling using R

Probability and Statistics for Engineering and the Sciences with Modeling using R

Probability and statistics courses are more popular than ever. Regardless of your major or your profession you will most likely use concepts from probability and statistics often in your career. The primary goal behind this book is offering the flexibility for instructors to build most undergraduate courses upon it. This book is designed for either a one-semester course in either introductory probability and statistics (not calculus-based) and/or a one-semester course in a calculus-based probability and statistics course. The book focuses on engineering examples and applications while also including social sciences and more examples. Depending on the chapter flows a course can be tailored for students at all levels and background. Over many years of teaching this course the authors created problems based on real data student projects and labs. Students have suggested these enhance their experience and learning. The authors hope to share projects and labs with other instructors and students to make the course more interesting for both. R is an excellent platform to use. This book uses R with real data sets. The labs can be used for group work in class or for self-directed study. These project labs have been class-tested for many years with good results and encourage students to apply the key concepts and use of technology to analyze and present results. | Probability and Statistics for Engineering and the Sciences with Modeling using R

GBP 89.99
1

Reproducible Finance with R Code Flows and Shiny Apps for Portfolio Analysis

Reproducible Finance with R Code Flows and Shiny Apps for Portfolio Analysis

Reproducible Finance with R: Code Flows and Shiny Apps for Portfolio Analysis is a unique introduction to data science for investment management that explores the three major R/finance coding paradigms emphasizes data visualization and explains how to build a cohesive suite of functioning Shiny applications. The full source code asset price data and live Shiny applications are available at reproduciblefinance. com. The ideal reader works in finance or wants to work in finance and has a desire to learn R code and Shiny through simple yet practical real-world examples. The book begins with the first step in data science: importing and wrangling data which in the investment context means importing asset prices converting to returns and constructing a portfolio. The next section covers risk and tackles descriptive statistics such as standard deviation skewness kurtosis and their rolling histories. The third section focuses on portfolio theory analyzing the Sharpe Ratio CAPM and Fama French models. The book concludes with applications for finding individual asset contribution to risk and for running Monte Carlo simulations. For each of these tasks the three major coding paradigms are explored and the work is wrapped into interactive Shiny dashboards. | Reproducible Finance with R Code Flows and Shiny Apps for Portfolio Analysis

GBP 59.99
1

Spatial Statistics for Data Science Theory and Practice with R

Spatial Statistics for Data Science Theory and Practice with R

Spatial data is crucial to improve decision-making in a wide range of fields including environment health ecology urban planning economy and society. Spatial Statistics for Data Science: Theory and Practice with R describes statistical methods modeling approaches and visualization techniques to analyze spatial data using R. The book provides a comprehensive overview of the varying types of spatial data and detailed explanations of the theoretical concepts of spatial statistics alongside fully reproducible examples which demonstrate how to simulate describe and analyze spatial data in various applications. Combining theory and practice the book includes real-world data science examples such as disease risk mapping air pollution prediction species distribution modeling crime mapping and real state analyses. The book utilizes publicly available data and offers clear explanations of the R code for importing manipulating analyzing and visualizing data as well as the interpretation of the results. This ensures contents are easily accessible and fully reproducible for students researchers and practitioners. Key Features: Describes R packages for retrieval manipulation and visualization of spatial data. Offers a comprehensive overview of spatial statistical methods including spatial autocorrelation clustering spatial interpolation model-based geostatistics and spatial point processes. Provides detailed explanations on how to fit and interpret Bayesian spatial models using the integrated nested Laplace approximation (INLA) and stochastic partial differential equation (SPDE) approaches. | Spatial Statistics for Data Science Theory and Practice with R

GBP 74.99
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Applied Linear Regression for Longitudinal Data With an Emphasis on Missing Observations

Applied Linear Regression for Longitudinal Data With an Emphasis on Missing Observations

This book introduces best practices in longitudinal data analysis at intermediate level with a minimum number of formulas without sacrificing depths. It meets the need to understand statistical concepts of longitudinal data analysis by visualizing important techniques instead of using abstract mathematical formulas. Different solutions such as multiple imputation are explained conceptually and consequences of missing observations are clarified using visualization techniques. Key features include the following: Provides datasets and examples online Gives state-of-the-art methods of dealing with missing observations in a non-technical way with a special focus on sensitivity analysis Conceptualises the analysis of comparative (experimental and observational) studies It is the ideal companion for researchers and students in epidemiological health and social and behavioral sciences working with longitudinal studies without a mathematical background. | Applied Linear Regression for Longitudinal Data With an Emphasis on Missing Observations

GBP 89.99
1

Combinatorial Nullstellensatz With Applications to Graph Colouring

Combinatorial Nullstellensatz With Applications to Graph Colouring

Combinatorial Nullstellensatz is a novel theorem in algebra introduced by Noga Alon to tackle combinatorial problems in diverse areas of mathematics. This book focuses on the applications of this theorem to graph colouring. A key step in the applications of Combinatorial Nullstellensatz is to show that the coefficient of a certain monomial in the expansion of a polynomial is nonzero. The major part of the book concentrates on three methods for calculating the coefficients: Alon-Tarsi orientation: The task is to show that a graph has an orientation with given maximum out-degree and for which the number of even Eulerian sub-digraphs is different from the number of odd Eulerian sub-digraphs. In particular this method is used to show that a graph whose edge set decomposes into a Hamilton cycle and vertex-disjoint triangles is 3-choosable and that every planar graph has a matching whose deletion results in a 4-choosable graph. Interpolation formula for the coefficient: This method is in particular used to show that toroidal grids of even order are 3-choosable r-edge colourable r-regular planar graphs are r-edge choosable and complete graphs of order p+1 where p is a prime are p-edge choosable. Coefficients as the permanents of matrices: This method is in particular used in the study of the list version of vertex-edge weighting and to show that every graph is (2 3)-choosable. It is suited as a reference book for a graduate course in mathematics. | Combinatorial Nullstellensatz With Applications to Graph Colouring

GBP 52.99
1

Understanding Statistics for the Social Sciences with IBM SPSS

Understanding Statistics for the Social Sciences with IBM SPSS

Modern statistical software provides the ability to compute statistics in a timely orderly fashion. This introductory statistics textbook presents clear explanations of basic statistical concepts and introduces students to the IBM SPSS program to demonstrate how to conduct statistical analyses via the popular point-and-click and the syntax file methods. The focal point is to show students how easy it is to analyse data using SPSS once they have learned the basics. Provides clear explanation of basic statistical concepts that provides the foundation for the beginner students’ statistical journey. Introduces the SPSS software program. Gives clear explanation of the purpose of specific statistical procedures (e. g. frequency distributions measures of central tendencies measures of variability etc. ). Avoids the conventional cookbook approach that contributes very little to students’ understanding of the rationale of how the correct results were obtained. The advantage of learning the IBM SPSS software package at the introductory class level is that most social sciences students will employ this program in their later years of study. This is because SPSS is one of the most popular of the many statistical packages currently available. Learning how to use this program at the very start not only familiarizes students with the utility of this program but also provides them with the experience to employ the program to conduct more complex analyses in their later years. | Understanding Statistics for the Social Sciences with IBM SPSS

GBP 44.99
1

Bayesian Analysis with R for Drug Development Concepts Algorithms and Case Studies

Bayesian Analysis with R for Drug Development Concepts Algorithms and Case Studies

Drug development is an iterative process. The recent publications of regulatory guidelines further entail a lifecycle approach. Blending data from disparate sources the Bayesian approach provides a flexible framework for drug development. Despite its advantages the uptake of Bayesian methodologies is lagging behind in the field of pharmaceutical development. Written specifically for pharmaceutical practitioners Bayesian Analysis with R for Drug Development: Concepts Algorithms and Case Studies describes a wide range of Bayesian applications to problems throughout pre-clinical clinical and Chemistry Manufacturing and Control (CMC) development. Authored by two seasoned statisticians in the pharmaceutical industry the book provides detailed Bayesian solutions to a broad array of pharmaceutical problems. Features Provides a single source of information on Bayesian statistics for drug development Covers a wide spectrum of pre-clinical clinical and CMC topics Demonstrates proper Bayesian applications using real-life examples Includes easy-to-follow R code with Bayesian Markov Chain Monte Carlo performed in both JAGS and Stan Bayesian software platforms Offers sufficient background for each problem and detailed description of solutions suitable for practitioners with limited Bayesian knowledge Harry Yang Ph. D. is Senior Director and Head of Statistical Sciences at AstraZeneca. He has 24 years of experience across all aspects of drug research and development and extensive global regulatory experiences. He has published 6 statistical books 15 book chapters and over 90 peer-reviewed papers on diverse scientific and statistical subjects including 15 joint statistical works with Dr. Novick. He is a frequent invited speaker at national and international conferences. He also developed statistical courses and conducted training at the FDA and USP as well as Peking University. Steven Novick Ph. D. is Director of Statistical Sciences at AstraZeneca. He has extensively contributed statistical methods to the biopharmaceutical literature. Novick is a skilled Bayesian computer programmer and is frequently invited to speak at conferences having developed and taught courses in several areas including drug-combination analysis and Bayesian methods in clinical areas. Novick served on IPAC-RS and has chaired several national statistical conferences. | Bayesian Analysis with R for Drug Development Concepts Algorithms and Case Studies

GBP 38.99
1

Reinforcement Learning for Cyber-Physical Systems with Cybersecurity Case Studies

Reinforcement Learning for Cyber-Physical Systems with Cybersecurity Case Studies

Reinforcement Learning for Cyber-Physical Systems: with Cybersecurity Case Studies was inspired by recent developments in the fields of reinforcement learning (RL) and cyber-physical systems (CPSs). Rooted in behavioral psychology RL is one of the primary strands of machine learning. Different from other machine learning algorithms such as supervised learning and unsupervised learning the key feature of RL is its unique learning paradigm i. e. trial-and-error. Combined with the deep neural networks deep RL become so powerful that many complicated systems can be automatically managed by AI agents at a superhuman level. On the other hand CPSs are envisioned to revolutionize our society in the near future. Such examples include the emerging smart buildings intelligent transportation and electric grids. However the conventional hand-programming controller in CPSs could neither handle the increasing complexity of the system nor automatically adapt itself to new situations that it has never encountered before. The problem of how to apply the existing deep RL algorithms or develop new RL algorithms to enable the real-time adaptive CPSs remains open. This book aims to establish a linkage between the two domains by systematically introducing RL foundations and algorithms each supported by one or a few state-of-the-art CPS examples to help readers understand the intuition and usefulness of RL techniques. FeaturesIntroduces reinforcement learning including advanced topics in RL Applies reinforcement learning to cyber-physical systems and cybersecurity Contains state-of-the-art examples and exercises in each chapterProvides two cybersecurity case studies Reinforcement Learning for Cyber-Physical Systems with Cybersecurity Case Studies is an ideal text for graduate students or junior/senior undergraduates in the fields of science engineering computer science or applied mathematics. It would also prove useful to researchers and engineers interested in cybersecurity RL and CPS. The only background knowledge required to appreciate the book is a basic knowledge of calculus and probability theory.

GBP 44.99
1

Iterative Methods and Preconditioning for Large and Sparse Linear Systems with Applications

Iterative Methods and Preconditioning for Large and Sparse Linear Systems with Applications

This book describes in a basic way the most useful and effective iterative solvers and appropriate preconditioning techniques for some of the most important classes of large and sparse linear systems. The solution of large and sparse linear systems is the most time-consuming part for most of the scientific computing simulations. Indeed mathematical models become more and more accurate by including a greater volume of data but this requires the solution of larger and harder algebraic systems. In recent years research has focused on the efficient solution of large sparse and/or structured systems generated by the discretization of numerical models by using iterative solvers. | Iterative Methods and Preconditioning for Large and Sparse Linear Systems with Applications

GBP 150.00
1

Python for Beginners

Python for Beginners

Python is an amazing programming language. It can be applied to almost any programming task. It allows for rapid development and debugging. Getting started with Python is like learning any new skill: it’s important to find a resource you connect with to guide your learning. Luckily there’s no shortage of excellent books that can help you learn both the basic concepts of programming and the specifics of programming in Python. With the abundance of resources it can be difficult to identify which book would be best for your situation. Python for Beginners is a concise single point of reference for all material on python. Provides concise need-to-know information on Python types and statements special method names built-in functions and exceptions commonly used standard library modules and other prominent Python tools Offers practical advice for each major area of development with both Python 3. x and Python 2. x Based on the latest research in cognitive science and learning theory Helps the reader learn how to write effective idiomatic Python code by leveraging its best—and possibly most neglected—features This book focuses on enthusiastic research aspirants who work on scripting languages for automating the modules and tools development of web applications handling big data complex calculations workflow creation rapid prototyping and other software development purposes. It also targets graduates postgraduates in computer science information technology academicians practitioners and research scholars.

GBP 120.00
1

Introductory Mathematical Analysis for Quantitative Finance

Introductory Mathematical Analysis for Quantitative Finance

Introductory Mathematical Analysis for Quantitative Finance is a textbook designed to enable students with little knowledge of mathematical analysis to fully engage with modern quantitative finance. A basic understanding of dimensional Calculus and Linear Algebra is assumed. The exposition of the topics is as concise as possible since the chapters are intended to represent a preliminary contact with the mathematical concepts used in Quantitative Finance. The aim is that this book can be used as a basis for an intensive one-semester course. Features:Written with applications in mind and maintaining mathematical rigor. Suitable for undergraduate or master's level students with an Economics or Management background. Complemented with various solved examples and exercises to support the understanding of the subject.

GBP 39.99
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Fundamentals of Ramsey Theory

Fundamentals of Ramsey Theory

Ramsey theory is a fascinating topic. The author shares his view of the topic in this contemporary overview of Ramsey theory. He presents from several points of view adding intuition and detailed proofs in an accessible manner unique among most books on the topic. This book covers all of the main results in Ramsey theory along with results that have not appeared in a book before. The presentation is comprehensive and reader friendly. The book covers integer graph and Euclidean Ramsey theory with many proofs being combinatorial in nature. The author motivates topics and discussion rather than just a list of theorems and proofs. In order to engage the reader each chapter has a section of exercises. This up-to-date book introduces the field of Ramsey theory from several different viewpoints so that the reader can decide which flavor of Ramsey theory best suits them. Additionally the book offers: A chapter providing different approaches to Ramsey theory e. g. using topological dynamics ergodic systems and algebra in the Stone-Čech compactification of the integers. A chapter on the probabilistic method since it is quite central to Ramsey-type numbers. A unique chapter presenting some applications of Ramsey theory. Exercises in every chapter The intended audience consists of students and mathematicians desiring to learn about Ramsey theory. An undergraduate degree in mathematics (or its equivalent for advanced undergraduates) and a combinatorics course is assumed. TABLE OF CONENTS Preface List of Figures List of Tables Symbols 1. Introduction 2. Integer Ramsey Theory 3. Graph Ramsey Theory 4. Euclidean Ramsey Theory 5. Other Approaches to Ramsey Theory 6. The Probabilistic Method 7. Applications Bibliography Index Biography Aaron Robertson received his Ph. D. in mathematics from Temple University under the guidance of his advisor Doron Zeilberger. Upon finishing his Ph. D. he started at Colgate University in upstate New York where he is currently Professor of Mathematics. He also serves as Associate Managing editor of the journal Integers. After a brief detour into the world of permutation patterns he has focused most of his research on Ramsey theory. | Fundamentals of Ramsey Theory

GBP 82.99
1

Statistical Reasoning for Surgeons

Statistical Reasoning for Surgeons

Trying to read up on statistics can be like trying to decide where you want to start eating the elephant and what’s the most digestible way to get it down. This book is written to give bite-size nuggets of insight based on our experiences grappling with datasets large and small. It is intended to bridge the gap between the formal equations and the practicalities of generating a research manuscript. We won’t pretend reading it will answer all your questions but it will help explain what questions need to be asked for your study and how you can address them with both accuracy and clarity. The size detail and (ostensible) organization of this book allow for easy reading and can give a leg (or at least a half-step) up for those seeking more detailed study later. Features include: Excel sheets to allow exploration of topics raised Emphasis on intuitive explanations over formulas. Consideration of issues specific to clinical and surgical studies Our audience is someone who may or may not have enjoyed formal statistics education (that is you may have had it and not enjoyed it!) who may like seeing a more dressed-down presentation of the topics. Actual statisticians may pick this up at risk of a chuckle (with us or at us) and may find some useful ways to present topics to non-statisticians. | Statistical Reasoning for Surgeons

GBP 48.99
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Python for Bioinformatics

Python for Bioinformatics

In today's data driven biology programming knowledge is essential in turning ideas into testable hypothesis. Based on the author’s extensive experience Python for Bioinformatics Second Edition helps biologists get to grips with the basics of software development. Requiring no prior knowledge of programming-related concepts the book focuses on the easy-to-use yet powerful Python computer language. This new edition is updated throughout to Python 3 and is designed not just to help scientists master the basics but to do more in less time and in a reproducible way. New developments added in this edition include NoSQL databases the Anaconda Python distribution graphical libraries like Bokeh and the use of Github for collaborative development.

GBP 74.99
1

Geometry for the Artist

Geometry for the Artist

Geometry for the Artist is based on a course of the same name which started in the 1980s at Maharishi International University. It is aimed both at artists willing to dive deeper into geometry and at mathematicians open to learning about applications of mathematics in art. The book includes topics such as perspective symmetry topology fractals curves surfaces and more. A key part of the book’s approach is the analysis of art from a geometric point of view—looking at examples of how artists use each new topic. In addition exercises encourage students to experiment in their own work with the new ideas presented in each chapter. This book is an exceptional resource for students in a general-education mathematics course or teacher-education geometry course and since many assignments involve writing about art this text is ideal for a writing-intensive course. Moreover this book will be enjoyed by anyone with an interest in connections between mathematics and art. Features Abundant examples of artwork displayed in full color Suitable as a textbook for a general-education mathematics course or teacher-education geometry course Designed to be enjoyed by both artists and mathematicians

GBP 44.99
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Mathematics for Engineers and Scientists

Mathematics for Engineers and Scientists

Since its original publication in 1969 Mathematics for Engineers and Scientists has built a solid foundation in mathematics for legions of undergraduate science and engineering students. It continues to do so but as the influence of computers has grown and syllabi have evolved once again the time has come for a new edition. Thoroughly revised to meet the needs of today's curricula Mathematics for Engineers and Scientists Sixth Edition covers all of the topics typically introduced to first- or second-year engineering students from number systems functions and vectors to series differential equations and numerical analysis. Among the most significant revisions to this edition are:Simplified presentation of many topics and expanded explanations that further ease the comprehension of incoming engineering studentsA new chapter on double integralsMany more exercises applications and worked examplesA new chapter introducing the MATLAB and Maple software packagesAlthough designed as a textbook with problem sets in each chapter and selected answers at the end of the book Mathematics for Engineers and Scientists Sixth Edition serves equally well as a supplemental text and for self-study. The author strongly encourages readers to make use of computer algebra software to experiment with it and to learn more about mathematical functions and the operations that it can perform.

GBP 170.00
1