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Introduction to Linear Algebra

Introduction to Linear Algebra

Linear algebra provides the essential mathematical tools to tackle all the problems in Science. Introduction to Linear Algebra is primarily aimed at students in applied fields (e. g. Computer Science and Engineering) providing them with a concrete rigorous approach to face and solve various types of problems for the applications of their interest. This book offers a straightforward introduction to linear algebra that requires a minimal mathematical background to read and engage with. Features Presented in a brief informative and engaging style Suitable for a wide broad range of undergraduates Contains many worked examples and exercises

GBP 44.99
1

Introduction to Number Theory

Introduction to Number Theory

Introduction to Number Theory covers the essential content of an introductory number theory course including divisibility and prime factorization congruences and quadratic reciprocity. The instructor may also choose from a collection of additional topics. Aligning with the trend toward smaller essential texts in mathematics the author strives for clarity of exposition. Proof techniques and proofs are presented slowly and clearly. The book employs a versatile approach to the use of algebraic ideas. Instructors who wish to put this material into a broader context may do so though the author introduces these concepts in a non-essential way. A final chapter discusses algebraic systems (like the Gaussian integers) presuming no previous exposure to abstract algebra. Studying general systems helps students to realize unique factorization into primes is a more subtle idea than may at first appear; students will find this chapter interesting fun and quite accessible. Applications of number theory include several sections on cryptography and other applications to further interest instructors and students alike.

GBP 38.99
1

An Introduction to Analysis

An Introduction to Analysis

The third edition of this widely popular textbook is authored by a master teacher. This book provides a mathematically rigorous introduction to analysis of real­valued functions of one variable. This intuitive student-friendly text is written in a manner that will help to ease the transition from primarily computational to primarily theoretical mathematics. The material is presented clearly and as intuitive as possible while maintaining mathematical integrity. The author supplies the ideas of the proof and leaves the write-up as an exercise. The text also states why a step in a proof is the reasonable thing to do and which techniques are recurrent. Examples while no substitute for a proof are a valuable tool in helping to develop intuition and are an important feature of this text. Examples can also provide a vivid reminder that what one hopes might be true is not always true. Features of the Third Edition: Begins with a discussion of the axioms of the real number system. The limit is introduced via sequences. Examples motivate what is to come highlight the need for hypothesis in a theorem and make abstract ideas more concrete. A new section on the Cantor set and the Cantor function. Additional material on connectedness. Exercises range in difficulty from the routine getting your feet wet types of problems to the moderately challenging problems. Topology of the real number system is developed to obtain the familiar properties of continuous functions. Some exercises are devoted to the construction of counterexamples. The author presents the material to make the subject understandable and perhaps exciting to those who are beginning their study of abstract mathematics. Table of Contents Preface Introduction The Real Number System Sequences of Real Numbers Topology of the Real Numbers Continuous Functions Differentiation Integration Series of Real Numbers Sequences and Series of Functions Fourier Series Bibliography Hints and Answers to Selected Exercises Index Biography James R. Kirkwood holds a Ph. D. from University of Virginia. He has authored fifteen published mathematics textbooks on various topics including calculus real analysis mathematical biology and mathematical physics. His original research was in mathematical physics and he co-authored the seminal paper in a topic now called Kirkwood-Thomas Theory in mathematical physics. During the summer he teaches real analysis to entering graduate students at the University of Virginia. He has been awarded several National Science Foundation grants. His texts Elementary Linear Algebra Linear Algebra and Markov Processes are also published by CRC Press. | An Introduction to Analysis

GBP 82.99
1

Introduction to Mathematical Modeling

Introduction to Mathematical Modeling

Introduction to Mathematical Modeling helps students master the processes used by scientists and engineers to model real-world problems including the challenges posed by space exploration climate change energy sustainability chaotic dynamical systems and random processes. Primarily intended for students with a working knowledge of calculus but minimal training in computer programming in a first course on modeling the more advanced topics in the book are also useful for advanced undergraduate and graduate students seeking to get to grips with the analytical numerical and visual aspects of mathematical modeling as well as the approximations and abstractions needed for the creation of a viable model.

GBP 84.99
1

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
1

Field Guide to Compelling Analytics

Field Guide to Compelling Analytics

Field Guide to Compelling Analytics is written for Analytics Professionals (APs) who want to increase their probability of success in implementing analytical solutions. In the past soft skills such as presentation and persuasive writing techniques have been the extent of teaching junior APs how to effectively communicate the value of analytical products. However there are other aspects to success such as trust and experience that may play a more important role in convincing fellow APs clients advisors and leadership groups that their analytic solutions will work. This book introduces the formula ‘Analytics + Trust + Communication + Experience > Convince Them’ to illustrate an AP’s ability to convince a stakeholder. The ‘Convince Me’ stakeholders might be an analytics team member team lead decision-maker or senior leader that are either internal or external to the AP’s organization. Whoever they are this formula represents a concise digestible and above all practical means to increase the likelihood that you will be able to persuade them of the value of your analytical product. Features Includes insight questions to support class discussion Written in broadly non-mathematical terms designed to be accessible to any level of student or practicing AP to read understand and implement the concepts Each section introduces the ideas through real-life case studies

GBP 48.99
1

Introduction to Real Analysis

Introduction to Real Analysis

This classic textbook has been used successfully by instructors and students for nearly three decades. This timely new edition offers minimal yet notable changes while retaining all the elements presentation and accessible exposition of previous editions. A list of updates is found in the Preface to this edition. This text is based on the author’s experience in teaching graduate courses and the minimal requirements for successful graduate study. The text is understandable to the typical student enrolled in the course taking into consideration the variations in abilities background and motivation. Chapters one through six have been written to be accessible to the average student w hile at the same time challenging the more talented student through the exercises. Chapters seven through ten assume the students have achieved some level of expertise in the subject. In these chapters the theorems examples and exercises require greater sophistication and mathematical maturity for full understanding. In addition to the standard topics the text includes topics that are not always included in comparable texts. Chapter 6 contains a section on the Riemann-Stieltjes integral and a proof of Lebesgue’s t heorem providing necessary and sufficient conditions for Riemann integrability. Chapter 7 also includes a section on square summable sequences and a brief introduction to normed linear spaces. C hapter 8 contains a proof of the Weierstrass approximation theorem using the method of aapproximate identities. The inclusion of Fourier series in the text allows the student to gain some exposure to this important subject. The final chapter includes a detailed treatment of Lebesgue measure and the Lebesgue integral using inner and outer measure. The exercises at the end of each section reinforce the concepts. Notes provide historical comments or discuss additional topics. | Introduction to Real Analysis

GBP 46.99
1

Introduction to Probability Second Edition

Introduction to Probability Second Edition

Developed from celebrated Harvard statistics lectures Introduction to Probability provides essential language and tools for understanding statistics randomness and uncertainty. The book explores a wide variety of applications and examples ranging from coincidences and paradoxes to Google PageRank and Markov chain Monte Carlo (MCMC). Additional application areas explored include genetics medicine computer science and information theory. The authors present the material in an accessible style and motivate concepts using real-world examples. Throughout they use stories to uncover connections between the fundamental distributions in statistics and conditioning to reduce complicated problems to manageable pieces. The book includes many intuitive explanations diagrams and practice problems. Each chapter ends with a section showing how to perform relevant simulations and calculations in R a free statistical software environment. The second edition adds many new examples exercises and explanations to deepen understanding of the ideas clarify subtle concepts and respond to feedback from many students and readers. New supplementary online resources have been developed including animations and interactive visualizations and the book has been updated to dovetail with these resources. Supplementary material is available on Joseph Blitzstein’s website www. stat110. net. The supplements include:Solutions to selected exercisesAdditional practice problemsHandouts including review material and sample exams Animations and interactive visualizations created in connection with the edX online version of Stat 110. Links to lecture videos available on ITunes U and YouTube There is also a complete instructor's solutions manual available to instructors who require the book for a course. | Introduction to Probability Second Edition

GBP 66.99
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Introduction to Software Engineering

Introduction to Software Engineering

Practical Guidance on the Efficient Development of High-Quality SoftwareIntroduction to Software Engineering Second Edition equips students with the fundamentals to prepare them for satisfying careers as software engineers regardless of future changes in the field even if the changes are unpredictable or disruptive in nature. Retaining the same organization as its predecessor this second edition adds considerable material on open source and agile development models. The text helps students understand software development techniques and processes at a reasonably sophisticated level. Students acquire practical experience through team software projects. Throughout much of the book a relatively large project is used to teach about the requirements design and coding of software. In addition a continuing case study of an agile software development project offers a complete picture of how a successful agile project can work. The book covers each major phase of the software development life cycle from developing software requirements to software maintenance. It also discusses project management and explains how to read software engineering literature. Three appendices describe software patents command-line arguments and flowcharts.

GBP 44.99
1

Introduction to Python Programming

Introduction to Python Programming

Introduction to Python Programming is written for students who are beginners in the field of computer programming. This book presents an intuitive approach to the concepts of Python Programming for students. This book differs from traditional texts not only in its philosophy but also in its overall focus level of activities development of topics and attention to programming details. The contents of the book are chosen with utmost care after analyzing the syllabus for Python course prescribed by various top universities in USA Europe and Asia. Since the prerequisite know-how varies significantly from student to student the book’s overall overture addresses the challenges of teaching and learning of students which is fine-tuned by the authors’ experience with large sections of students. This book uses natural language expressions instead of the traditional shortened words of the programming world. This book has been written with the goal to provide students with a textbook that can be easily understood and to make a connection between what students are learning and how they may apply that knowledge. Features of this book This book does not assume any previous programming experience although of course any exposure to other programming languages is useful This book introduces all of the key concepts of Python programming language with helpful illustrations Programming examples are presented in a clear and consistent manner Each line of code is numbered and explained in detail Use of f-strings throughout the book Hundreds of real-world examples are included and they come from fields such as entertainment sports music and environmental studies Students can periodically check their progress with in-chapter quizzes that appear in all chapters

GBP 160.00
1

Introduction to Math Olympiad Problems

Introduction to Math Olympiad Problems

Introduction to Math Olympiad Problems aims to introduce high school students to all the necessary topics that frequently emerge in international Math Olympiad competitions. In addition to introducing the topics the book will also provide several repetitive-type guided problems to help develop vital techniques in solving problems correctly and efficiently. The techniques employed in the book will help prepare students for the topics they will typically face in an Olympiad-style event but also for future college mathematics courses in Discrete Mathematics Graph Theory Differential Equations Number Theory and Abstract Algebra. Features: Numerous problems designed to embed good practice in readers and build underlying reasoning analysis and problem-solving skills Suitable for advanced high school students preparing for Math Olympiad competitions

GBP 24.99
1

Introduction to Probability with Mathematica

Introduction to Probability with Mathematica

Updated to conform to Mathematica® 7. 0 Introduction to Probability with Mathematica® Second Edition continues to show students how to easily create simulations from templates and solve problems using Mathematica. It provides a real understanding of probabilistic modeling and the analysis of data and encourages the application of these ideas to practical problems. The accompanyingdownloadable resources offer instructors the option of creating class notes demonstrations and projects. New to the Second EditionExpanded section on Markov chains that includes a study of absorbing chainsNew sections on order statistics transformations of multivariate normal random variables and Brownian motionMore example data of the normal distribution More attention on conditional expectation which has become significant in financial mathematicsAdditional problems from Actuarial Exam PNew appendix that gives a basic introduction to MathematicaNew examples exercises and data sets particularly on the bivariate normal distributionNew visualization and animation features from Mathematica 7. 0Updated Mathematica notebooks on the downloadable resources. After covering topics in discrete probability the text presents a fairly standard treatment of common discrete distributions. It then transitions to continuous probability and continuous distributions including normal bivariate normal gamma and chi-square distributions. The author goes on to examine the history of probability the laws of large numbers and the central limit theorem. The final chapter explores stochastic processes and applications ideal for students in operations research and finance.

GBP 59.99
1

Practical Guide to Logistic Regression

Practical Guide to Logistic Regression

Practical Guide to Logistic Regression covers the key points of the basic logistic regression model and illustrates how to use it properly to model a binary response variable. This powerful methodology can be used to analyze data from various fields including medical and health outcomes research business analytics and data science ecology fisheries astronomy transportation insurance economics recreation and sports. By harnessing the capabilities of the logistic model analysts can better understand their data make appropriate predictions and classifications and determine the odds of one value of a predictor compared to another. Drawing on his many years of teaching logistic regression using logistic-based models in research and writing about the subject Professor Hilbe focuses on the most important features of the logistic model. Serving as a guide between the author and readers the book explains how to construct a logistic model interpret coefficients and odds ratios predict probabilities and their standard errors based on the model and evaluate the model as to its fit. Using a variety of real data examples mostly from health outcomes the author offers a basic step-by-step guide to developing and interpreting observation and grouped logistic models as well as penalized and exact logistic regression. He also gives a step-by-step guide to modeling Bayesian logistic regression. R statistical software is used throughout the book to display the statistical models while SAS and Stata codes for all examples are included at the end of each chapter. The example code can be adapted to readers own analyses. All the code is available on the author‘s website.

GBP 175.00
1

Introduction to Python for Humanists

Introduction to Python for Humanists

This book will introduce digital humanists at all levels of education to Python. It provides background and guidance on learning the Python computer programming language and as it presumes no knowledge on the part of the reader about computers or coding concepts allows the reader to gradually learn the more complex tasks that are currently popular in the field of digital humanities. This book will be aimed at undergraduates graduates and faculty who are interested in learning how to use Python as a tool within their workflow. An Introduction to Python for Digital Humanists will act as a primer for students who wish to use Python allowing them to engage with more advanced textbooks. This book fills a real need as it is first Python introduction to be aimed squarely at humanities students as other books currently available do not approach Python from a humanities perspective. It will be designed so that those experienced in Python can teach from it in addition to allowing those who are interested in being self-taught can use it for that purpose. Key Features: Data analysis Data science Computational humanities Digital humanities Python Natural language processing Social network analysis App development | Introduction to Python for Humanists

GBP 44.99
1

A Bridge to Higher Mathematics

A Bridge to Higher Mathematics

A Bridge to Higher Mathematics is more than simply another book to aid the transition to advanced mathematics. The authors intend to assist students in developing a deeper understanding of mathematics and mathematical thought. The only way to understand mathematics is by doing mathematics. The reader will learn the language of axioms and theorems and will write convincing and cogent proofs using quantifiers. Students will solve many puzzles and encounter some mysteries and challenging problems. The emphasis is on proof. To progress towards mathematical maturity it is necessary to be trained in two aspects: the ability to read and understand a proof and the ability to write a proof. The journey begins with elements of logic and techniques of proof then with elementary set theory relations and functions. Peano axioms for positive integers and for natural numbers follow in particular mathematical and other forms of induction. Next is the construction of integers including some elementary number theory. The notions of finite and infinite sets cardinality of counting techniques and combinatorics illustrate more techniques of proof. For more advanced readers the text concludes with sets of rational numbers the set of reals and the set of complex numbers. Topics like Zorn‘s lemma and the axiom of choice are included. More challenging problems are marked with a star. All these materials are optional depending on the instructor and the goals of the course.

GBP 175.00
1

Transition to Advanced Mathematics

Transition to Advanced Mathematics

This unique and contemporary text not only offers an introduction to proofs with a view towards algebra and analysis a standard fare for a transition course but also presents practical skills for upper-level mathematics coursework and exposes undergraduate students to the context and culture of contemporary mathematics. The authors implement the practice recommended by the Committee on the Undergraduate Program in Mathematics (CUPM) curriculum guide that a modern mathematics program should include cognitive goals and offer a broad perspective of the discipline. Part I offers: An introduction to logic and set theory. Proof methods as a vehicle leading to topics useful for analysis topology algebra and probability. Many illustrated examples often drawing on what students already know that minimize conversation about doing proofs. An appendix that provides an annotated rubric with feedback codes for assessing proof writing. Part II presents the context and culture aspects of the transition experience including: 21st century mathematics including the current mathematical culture vocations and careers. History and philosophical issues in mathematics. Approaching reading and learning from journal articles and other primary sources. Mathematical writing and typesetting in LaTeX. Together these Parts provide a complete introduction to modern mathematics both in content and practice. Table of Contents Part I - Introduction to Proofs Logic and Sets Arguments and Proofs Functions Properties of the Integers Counting and Combinatorial Arguments RelationsPart II - Culture History Reading and Writing Mathematical Culture Vocation and Careers History and Philosophy of Mathematics Reading and Researching Mathematics Writing and Presenting Mathematics Appendix A. Rubric for Assessing Proofs Appendix B. Index of Theorems and Definitions from Calculus and Linear Algebra Bibliography Index Biographies Danilo R. Diedrichs is an Associate Professor of Mathematics at Wheaton College in Illinois. Raised and educated in Switzerland he holds a PhD in applied mathematical and computational sciences from the University of Iowa as well as a master’s degree in civil engineering from the Ecole Polytechnique Fédérale in Lausanne Switzerland. His research interests are in dynamical systems modeling applied to biology ecology and epidemiology. Stephen Lovett is a Professor of Mathematics at Wheaton College in Illinois. He holds a PhD in representation theory from Northeastern University. His other books include Abstract Algebra: Structures and Applications (2015) Differential Geometry of Curves and Surfaces with Tom Banchoff (2016) and Differential Geometry of Manifolds (2019). | Transition to Advanced Mathematics

GBP 82.99
1

An Introduction to Metric Spaces

An Introduction to Metric Spaces

This book serves as a textbook for an introductory course in metric spaces for undergraduate or graduate students. The goal is to present the basics of metric spaces in a natural and intuitive way and encourage students to think geometrically while actively participating in the learning of this subject. In this book the authors illustrated the strategy of the proofs of various theorems that motivate readers to complete them on their own. Bits of pertinent history are infused in the text including brief biographies of some of the central players in the development of metric spaces. The textbook is divided into seven chapters that contain the main materials on metric spaces; namely introductory concepts completeness compactness connectedness continuous functions and metric fixed point theorems with applications. Some of the noteworthy features of this book include · Diagrammatic illustrations that encourage readers to think geometrically · Focus on systematic strategy to generate ideas for the proofs of theorems · A wealth of remarks observations along with a variety of exercises · Historical notes and brief biographies appearing throughout the text | An Introduction to Metric Spaces

GBP 32.99
1

Introduction to Mathematical Oncology

Introduction to Mathematical Oncology

Introduction to Mathematical Oncology presents biologically well-motivated and mathematically tractable models that facilitate both a deep understanding of cancer biology and better cancer treatment designs. It covers the medical and biological background of the diseases modeling issues and existing methods and their limitations. The authors introduce mathematical and programming tools along with analytical and numerical studies of the models. They also develop new mathematical tools and look to future improvements on dynamical models. After introducing the general theory of medicine and exploring how mathematics can be essential in its understanding the text describes well-known practical and insightful mathematical models of avascular tumor growth and mathematically tractable treatment models based on ordinary differential equations. It continues the topic of avascular tumor growth in the context of partial differential equation models by incorporating the spatial structure and physiological structure such as cell size. The book then focuses on the recent active multi-scale modeling efforts on prostate cancer growth and treatment dynamics. It also examines more mechanistically formulated models including cell quota-based population growth models with applications to real tumors and validation using clinical data. The remainder of the text presents abundant additional historical biological and medical background materials for advanced and specific treatment modeling efforts. Extensively classroom-tested in undergraduate and graduate courses this self-contained book allows instructors to emphasize specific topics relevant to clinical cancer biology and treatment. It can be used in a variety of ways including a single-semester undergraduate course a more ambitious graduate course or a full-year sequence on mathematical oncology.

GBP 44.99
1

Concise Introduction to Linear Algebra

Concise Introduction to Linear Algebra

Concise Introduction to Linear Algebra deals with the subject of linear algebra covering vectors and linear systems vector spaces orthogonality determinants eigenvalues and eigenvectors singular value decomposition. It adopts an efficient approach to lead students from vectors matrices quickly into more advanced topics including LU decomposition orthogonal decomposition Least squares solutions Gram-Schmidt process eigenvalues and eigenvectors diagonalizability spectral decomposition positive definite matrix quadratic forms singular value decompositions and principal component analysis. This book is designed for onesemester teaching to undergraduate students.

GBP 44.99
1

Practitioner’s Guide to Data Science

Practitioner’s Guide to Data Science

This book aims to increase the visibility of data science in real-world which differs from what you learn from a typical textbook. Many aspects of day-to-day data science work are almost absent from conventional statistics machine learning and data science curriculum. Yet these activities account for a considerable share of the time and effort for data professionals in the industry. Based on industry experience this book outlines real-world scenarios and discusses pitfalls that data science practitioners should avoid. It also covers the big data cloud platform and the art of data science such as soft skills. The authors use R as the primary tool and provide code for both R and Python. This book is for readers who want to explore possible career paths and eventually become data scientists. This book comprehensively introduces various data science fields soft and programming skills in data science projects and potential career paths. Traditional data-related practitioners such as statisticians business analysts and data analysts will find this book helpful in expanding their skills for future data science careers. Undergraduate and graduate students from analytics-related areas will find this book beneficial to learn real-world data science applications. Non-mathematical readers will appreciate the reproducibility of the companion R and python codes. Key Features: • It covers both technical and soft skills. • It has a chapter dedicated to the big data cloud environment. For industry applications the practice of data science is often in such an environment. • It is hands-on. We provide the data and repeatable R and Python code in notebooks. Readers can repeat the analysis in the book using the data and code provided. We also suggest that readers modify the notebook to perform analyses with their data and problems if possible. The best way to learn data science is to do it! | Practitioner’s Guide to Data Science

GBP 52.99
1

Introduction to Computational Proteomics

Introduction to Computational Proteomics

Introduction to Computational Proteomics introduces the field of computational biology through a focused approach that tackles the different steps and problems involved with protein analysis classification and meta-organization. The book starts with the analysis of individual entities and works its way through the analysis of more complex entities from protein families to interactions cellular pathways and gene networks. The first part of the book presents methods for identifying the building blocks of the protein space such as motifs and domains. It also describes algorithms for assessing similarity between proteins based on sequence and structure analysis as well as mathematical models such as hidden Markov models and support vector machines that are used to represent protein families and classify new instances. The second part covers methods that investigate higher order structure in the protein space through the application of unsupervised learning algorithms such as clustering and embedding. The book also explores the broader context of proteins. It discusses methods for analyzing gene expression data predicting protein-protein interactions elucidating cellular pathways and reconstructing gene networks. This book provides a coherent and thorough introduction to proteome analysis. It offers rigorous formal descriptions along with detailed algorithmic solutions and models. Each chapter includes problem sets from courses taught by the author at Cornell University and the Technion. Software downloads data sets and other material are available at biozon. org

GBP 59.99
1

C Programming Learn to Code

C Programming Learn to Code

The C programming language is a popular language in industries as well as academics. Since its invention and standardized as ANSI C several other standards known as C99 C11 and C17 were published with new features in subsequent years. This book covers all the traits of ANSI C and includes new features present in other standards. The content of this book helps a beginner to learn the fundamental concept of the C language. The book contains a step-by-step explanation of every program that allows a learner to understand the syntax and builds a foundation to write similar programs. The explanation clarity exercises and illustrations present in this book make it a complete textbook in all aspects. Features: Other than ANSI C the book explains the new C standards like C99 C11 and C17. Most basic and easy-to-follow programs are chosen to explain the concepts and their syntax. More emphasis is given to the topics like Functions Pointers and Structures. Recursion is emphasized with numerous programming examples and diagrams. A separate chapter on the command-line argument and preprocessors is included that concisely explains their usage. Several real-life figures are taken to explain the concepts of dynamic memory allocation file handling and the difference between structure and union. The book contains more than 260 illustrations more than 200 programs and exercises at the end of each chapter. This book serves as a textbook for UG/PG courses in science and engineering. The researcher postgraduate engineers and embedded software developers can also keep this book as reference material for their fundamental learning. | C Programming Learn to Code

GBP 105.00
1

Introduction to Functional Data Analysis

Introduction to Functional Data Analysis

Introduction to Functional Data Analysis provides a concise textbook introduction to the field. It explains how to analyze functional data both at exploratory and inferential levels. It also provides a systematic and accessible exposition of the methodology and the required mathematical framework. The book can be used as textbook for a semester-long course on FDA for advanced undergraduate or MS statistics majors as well as for MS and PhD students in other disciplines including applied mathematics environmental science public health medical research geophysical sciences and economics. It can also be used for self-study and as a reference for researchers in those fields who wish to acquire solid understanding of FDA methodology and practical guidance for its implementation. Each chapter contains plentiful examples of relevant R code and theoretical and data analytic problems. The material of the book can be roughly divided into four parts of approximately equal length: 1) basic concepts and techniques of FDA 2) functional regression models 3) sparse and dependent functional data and 4) introduction to the Hilbert space framework of FDA. The book assumes advanced undergraduate background in calculus linear algebra distributional probability theory foundations of statistical inference and some familiarity with R programming. Other required statistics background is provided in scalar settings before the related functional concepts are developed. Most chapters end with references to more advanced research for those who wish to gain a more in-depth understanding of a specific topic.

GBP 44.99
1