Probability and statistics Books

Book SynopsisExam board: Cambridge Assessment International EducationLevel: A-levelSubject: MathematicsFirst teaching: September 2018First exams: Summer 2020Endorsed by Cambridge Assessment International Education to provide full support for Paper 6 of the syllabus for examination from 2020.Take mathematical understanding to the next level with this accessible series, written by experienced authors, examiners and teachers.- Improve confidence as a mathematician with clear explanations, worked examples, diverse activities and engaging discussion points. - Advance problem-solving, interpretation and communication skills through a wealth of questions that promote higher-order thinking. - Prepare for further study or life beyond the classroom by applying mathematics to other subjects and modelling real-world situations.- Reinforce learning with opportunities for digital practice via links to the Mathematics in Education and Industry's (MEI) Integral platform in the eBook.**To have full access to the eBook and Integral resources you must be subscribed to both Boost and Integral. To trial our eBooks and/or subscribe to Boost, visit: www.hoddereducation.com/Boost; to view samples of the Integral resources and/or subscribe to Integral, visit integralmaths.org/internationalPlease note that the Integral resources have not been through the Cambridge International endorsement process. This book covers the syllabus content for Probability and Statistics 2, including the Poisson distribution, linear combinations of random variables, continuous random variables, sampling and estimation and hypothesis tests.

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Book SynopsisLimitations of Linear Regression Applied on Ecological Data.- Things are not Always Linear; Additive Modelling.- Dealing with Heterogeneity.- Mixed Effects Modelling for Nested Data.- Violation of Independence Part I.- Violation of Independence Part II.- Meet the Exponential Family.- GLM and GAM for Count Data.- GLM and GAM for AbsencePresence and Proportional Data.- Zero-Truncated and Zero-Inflated Models for Count Data.- Generalised Estimation Equations.- GLMM and GAMM.- Estimating Trends for Antarctic Birds in Relation to Climate Change.- Large-Scale Impacts of Land-Use Change in a Scottish Farming Catchment.- Negative Binomial GAM and GAMM to Analyse Amphibian Roadkills.- Additive Mixed Modelling Applied on Deep-Sea Pelagic Bioluminescent Organisms.- Additive Mixed Modelling Applied on Phytoplankton Time Series Data.- Mixed Effects Modelling Applied on American Foulbrood Affecting Honey Bees Larvae.- Three-Way Nested Data for Age Determination Techniques Applied to Cetaceans.- GLTrade ReviewFrom the reviews:"For many people dealing with statistics is like jumping into ice-cold water. This metaphor is depicted by the cover of this book … . full of excellent example code and for most graphs and analyses the code is printed and explained in detail. … Each example finishes with … valuable information for a person new to a technique. In summary, I highly recommend the book to anyone who is familiar with basic statistics … who wants to expand his/her statistical knowledge to analyse ecological data." (Bernd Gruber, Basic and Applied Ecology, Vol. 10, 2009)"This book is written in a very approachable conversational style. The additional focus on the heuristics of the process rather than just a rote recital of theory and equations is commendable. This type of approach helps the reader get behind the ‘why’ of what’s being done rather than blindly follow a simple list of rules.… In short, this text is good for researchers with at least a little familiarity with the basic concepts of modeling and who want some solid stop-by-stop guidance with examples on how common ecological modeling tasks are accomplished using R." (Aaron Christ, Journal of Statistical Software, November 2009, Vol. 32)"The authors succeed in explaining complex extensions of regression in largely nonmathematical terms and clearly present appropriate R code for each analysis. A major strength of the text is that instead of relying on idealized datasets … the authors use data from consulting projects or dissertation research to expose issues associated with ‘real’ data. … The book is well written and accessible … . the volume should be a useful reference for advanced graduate students, postdoctoral researchers, and experienced professionals working in the biological sciences." (Paul E. Bourdeau, The Quarterly Review of Biology, Vol. 84, December, 2009)“This is a companion volume to Analyzing Ecology Data by the same authors. …It extends the previous work by looking at more complex general and generalized linear models involving mixed effects or heterogeneity in variances. It is aimed at statistically sophisticated readers who have a good understanding of multiple regression models… .The pedagogical style is informal… . The authors are pragmatists—they use combinations of informal graphical approaches, formal hypothesis testing, and information-theoretical model selection methods when analyzing data. …Advanced graduate students in ecology or ecologists with several years of experience with ‘messy’ data would find this book useful. …Statisticians would find this book interesting for the nice explorations of many of the issues with messy data. This book would be (very) suitable for a graduate course on statistical consulting—indeed, students would learn a great deal about the use of sophisticated statistical models in ecology! …I very much liked this book (and also the previous volume). I enjoyed the nontechnical presentations of the complex ideas and their emphasis that a good analysis uses ‘simple statistical methods wherever possible, but doesn’t use them simplistically.’” (Biometrics, Summer 2009, 65, 992–993)“This book is a great introduction to a wide variety of regression models. … This text examines how to fit many alternative models using the statistical package R. … The text is a valuable reference … . A large number of real datasets are used as examples. Discussion on which model to use and the large number of recent references make the book useful for self study … .” (David J. Olive, Technometrics, Vol. 52 (4), November, 2010)Table of ContentsLimitations of linear regression applied on ecological data. - Things are not always linear; additive modelling. - Dealing with hetergeneity. - Mixed modelling for nested data. - Violation of independence - temporal data. - Violation of independence; spatial data. - Generalised linear modelling and generalised additive modelling. - Generalised estimation equations. - GLMM and GAMM. - Estimating trends for Antarctic birds in relation to climate change. - Large-scale impacts of land-use change in a Scottish farming catchment. - Negative binomial GAM and GAMM to analyse amphibian road killings. - Additive mixed modelling applied on deep-sea plagic bioluminescent organisms. - Additive mixed modelling applied on phyoplankton time series data. - Mixed modelling applied on American Fouldbrood affecting honey bees larvae. - Three-way nested data for age determination techniques applied to small cetaceans. - GLMM applied on the spatial distribution of koalas in a fragmented landscape. - GEE and GLMM applied on binomial Badger activity data.

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Book SynopsisFay and Brittain present statistical hypothesis testing and compatible confidence intervals, focusing on application and proper interpretation. The emphasis is on equipping applied statisticians with enough tools - and advice on choosing among them - to find reasonable methods for almost any problem and enough theory to tackle new problems by modifying existing methods. After covering the basic mathematical theory and scientific principles, tests and confidence intervals are developed for specific types of data. Essential methods for applications are covered, such as general procedures for creating tests (e.g., likelihood ratio, bootstrap, permutation, testing from models), adjustments for multiple testing, clustering, stratification, causality, censoring, missing data, group sequential tests, and non-inferiority tests. New methods developed by the authors are included throughout, such as melded confidence intervals for comparing two samples and confidence intervals associated with WilTrade Review'A necessary book for the applied statistician seeking to understand the theoretical underpinnings of statistical methods and for graduate students knowledgeable about statistical theory but lacking experience in application. The book is chock full of challenging examples that point to the complexities of choice of method. A particularly valuable feature of the book is the authors' description of competing methods coupled with their clarity in explaining and justifying why they prefer one method over others. Fay and Brittain should sit on every statistician's bookshelf.' Janet Wittes, WCG Statistics Collaborative'Good statistical hypothesis testing and confidence interval construction involves mathematical aspects of finding a good test given a probability model and scientific aspects of determining the appropriateness of a probability model for answering a scientific question. This book provides a lucid discussion of both these mathematical and scientific aspects with compelling scientific examples. I most highly recommend this book.' Dylan Small, University of Pennsylvania'Congratulations to Fay and Brittain for this wonderful reference book that does what its somewhat unusual title suggests: puts hypothesis testing in the context of science. The vast coverage of topics, extensive bibliography and notes, and easy to understand explanations make 'Statistical Hypothesis Testing in Context: Reproducibility, Inference, and Science' an indispensable tool in the arsenal of any applied or theoretical statistician or biostatistician. I enthusiastically recommend buying the book!' Michael A. Proschan, National Institute of Allergy and Infectious DiseasesTable of Contents1. Introduction; 2. Theory of tests, p-values, and confidence intervals; 3. From scientific theory to statistical hypothesis test; 4. One sample studies with binary responses; 5. One sample studies with ordinal or numeric responses; 6. Paired data; 7. Two sample studies with binary responses; 8. Assumptions and hypothesis tests; 9. Two sample studies with ordinal or numeric responses; 10. General methods for creating decision rules; 11. K-Sample studies and trend tests; 12. Clustering and stratification; 13. Multiplicity in testing; 14. Testing from models; 15. Causality; 16. Censoring; 17. Missing data; 18. Group sequential and related adaptive methods; 19. Testing fit, equivalence, and non-inferiority; 20. Power and sample size.

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Book SynopsisThis new edition to the classic book by ggplot2 creator Hadley Wickham highlights compatibility with knitr and RStudio. ggplot2 is a data visualization package for R that helps users create data graphics, including those that are multi-layered, with ease. With ggplot2, it's easy to: produce handsome, publication-quality plots with automatic legends created from the plot specification superimpose multiple layers (points, lines, maps, tiles, box plots) from different data sources with automatically adjusted common scales add customizable smoothers that use powerful modeling capabilities of R, such as loess, linear models, generalized additive models, and robust regression save any ggplot2 plot (or part thereof) for later modification or reuse create custom themes that capture in-house or journal style requirements and that can easily be applied to multiple plots approach a graph from a visual perspective, thinking about how each component of the data is represented on the final plot This book will be useful to everyone who has struggled with displaying data in an informative and attractive way. Some basic knowledge of R is necessary (e.g., importing data into R). ggplot2 is a mini-language specifically tailored for producing graphics, and you'll learn everything you need in the book. After reading this book you'll be able to produce graphics customized precisely for your problems, and you'll find it easy to get graphics out of your head and on to the screen or page.Trade Review“The versatility and efficiency of ggplot have led to the development of ggplot2 and this book which overviews the standard use and presentation secrets of functions developed in the last 5 years. … The book is written in an accessible manner and it is suitable for undergraduates, postgraduates and researchers with some R experience. All theoretical concepts are accompanied by code making it easy to learn by reproducing the examples.” (Irina Ioana Mohorianu, zbMATH 1397.62006, 2018)“The book is an excellent and very comprehensive manual of … one of the most popular R packages. It is currently the only book describing ggplot2 in such depth. The book contains many examples and is very nicely illustrated, demonstrating the strength of the package.” (Klaus Galensa, Computing Reviews, May, 2017)Table of ContentsIntroduction.- Getting Started with ggplot2.- Toolbox.- Mastering the Grammar.- Building a Plot Layer by Layer.- Scales, Axes and Legends.- Positioning.- Themes.- Data Analysis.- Data Transformation.- Modelling for Visualisation.- Programming with ggplot2.- Index.- R Code Index.

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Book SynopsisMoreover, the text is useful for mathematicians, physicists, and engineers who want to learn finance via an approach that builds their financial intuition and is explicit about model building, as well as business school students who want a treatment of finance that is deeper but not overly theoretical.Trade Review“The book is thick with rich with applications and solid justifications of all concepts. … If you are a financial mathematics instructor, this book is for you. It progresses through easier topics to more advanced topics very well and is practical, meaningful, and more importantly, relevant to the 21st century financial student. … can be used as the one standard book for your class but can also be a springboard to research projects with your more advanced and curious students.” (Peter T. Olszewski, MAA Reviews, August, 2017)“This self-contained book is well organized and covers a broad range of classical topics of financial mathematics. A rich source of examples and exercises, the book is an ideal textbook for both undergraduate and graduate students that are interested in mathematical models in finance. The book can also be used for mathematically trained students and individuals in actuarial science to prepare for professional exams.” (Zhuo Jin, Mathematical Reviews, May, 2017)“The book is an undergraduate textbook in mathematical finance with applications. … The textbook is aimed at advanced undergraduates, and also at master's degree students who want a more rigorous treatment of the mathematical models in finance. This text will be a very good textbook for a year-long course on introductory mathematical finance.” (Anatoliy Swishchuk, zbMATH 1348.91002, 2016)Table of ContentsPreface.- 1. Preliminaries and Financial Markets.- 2. The Time Value of Money.- 3. Markowitz Portfolio Theory.- 4. Capital Market Theory and Portfolio Risk Measures.- 5. Binomial Trees and Security Pricing Modeling.- 6. Stochastic Calculus and Geometric Brownian Motion Model.- 7. Derivatives: Forwards, Futures, Swaps and Options.- 8. The BSM Model and European Option Pricing.- Index.

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Book SynopsisStarting around the late 1950s, several research communities began relating the geometry of graphs to stochastic processes on these graphs. This book, twenty years in the making, ties together research in the field, encompassing work on percolation, isoperimetric inequalities, eigenvalues, transition probabilities, and random walks. Written by two leading researchers, the text emphasizes intuition, while giving complete proofs and more than 850 exercises. Many recent developments, in which the authors have played a leading role, are discussed, including percolation on trees and Cayley graphs, uniform spanning forests, the mass-transport technique, and connections on random walks on graphs to embedding in Hilbert space. This state-of-the-art account of probability on networks will be indispensable for graduate students and researchers alike.Trade Review'This long-awaited work focuses on one of the most interesting and important parts of probability theory. Half a century ago, most work on models such as random walks, Ising, percolation and interacting particle systems concentrated on processes defined on the d-dimensional Euclidean lattice. In the intervening years, interest has broadened dramatically to include processes on more general graphs, with trees being a particularly important case. This led to new problems and richer behavior, and as a result, to the development of new techniques. The authors are two of the major developers of this area; their expertise is evident throughout.' Thomas M. Liggett, University of California, Los Angeles'Masterly, beautiful, encyclopaedic, and yet browsable - this great achievement is obligatory reading for anyone working near the conjunction of probability and network theory.' Geoffrey Grimmett, University of Cambridge'For the last ten years, I have not let a doctoral student graduate without reading this [work]. Sadly, the earliest of those students are missing a considerable amount of material that the bound and published edition contains. Not only are the classical topics of random walks, electrical theory, and uniform spanning trees covered in more coherent fashion than in any other source, but this book is also the best place to learn about a number of topics for which the other choices for textual material are limited. These include mass transport, random walk boundaries, and dimension and capacity in the context of Markov processes.' Robin Pemantle, University of Pennsylvania'Lyons and Peres have done an amazing job of motivating their material and of explaining it in a conversational and accessible fashion. Even though the book emphasizes probability on infinite graphs, it is one of my favorite references for probability on finite graphs. If you want to understand random walks, isoperimetry, random trees, or percolation, this is where you should start.' Daniel Spielman, Yale University, Connecticut'This long-awaited book offers a splendid account of several major areas of discrete probability. Both authors have made outstanding contributions to the subject, and the exceptional quality of the book is largely due to their high level of mastery of the field. Although the only prerequisites are basic probability theory and elementary Markov chains, the book succeeds in providing an elegant presentation of the most beautiful and deepest results in the various areas of probability on graphs. The powerful techniques that made these results available, such as the use of isoperimetric inequalities or the mass-transport principle, are also presented in a detailed and self-contained manner. This book will be indispensable to any researcher working in probability on graphs and related topics, and it will also be a must for anybody interested in the recent developments of probability theory.' Jean-François Le Gall, Université Paris-Sud'This is a very timely book about a circle of actively developing subjects in discrete probability. No wonder that it became very popular two decades before publication, while still in development. Not only a comprehensive reference source, but also a good textbook to learn the subject, it will be useful for specialists and newcomers alike.' Stanislav Smirnov, Université of Genève'A glorious labor of love, compiled over more than two decades of work, that brilliantly surveys the deep and expansive relationships between random trees and other areas of mathematics. Rarely does one encounter a text so exquisitely well written or enjoyable to read. One cannot take more than a few steps in modern probability without encountering one of the topics surveyed here. A truly essential resource.' Scott Sheffield, Massachusetts Institute of Technology'There is much to be learned from studying this book. Many of the ideas and tools are useful in a wide variety of different contexts … Geoff Grimmett's quote on the cover calls the book 'Masterly, beautiful, encyclopedic and yet browsable.' I totally agree. Even though it is freely available on the web, you should buy a copy of the book.' Richard Durrett, Mathematical Association of America Reviews (www.maa.org)'This is a monumental book covering a lot of interesting problems in discrete probability, written by two experts in the field … The authors have done a great job of providing full proofs of all main results, hence creating a self-contained reference in this area.' Abbas Mehrabian, Zentralblatt MATH'This long-awaited book, a project that started in 1993, is bound to be the main reference in the fascinating field of probability on trees and weighted graphs. The authors are the leading experts behind the tremendous developments experienced in the subject in recent decades, where the underlying networks evolved from classical lattices to general graphs … This pedagogically written book is a marvelous support for several courses on topics from combinatorics, Markov chains, geometric group theory, etc., as well as on their inspiring relationships. The wealth of exercises (with comments provided at the end of the book) will enable students and researchers to check their understanding of this fascinating mathematics.' Laurent Miclo, MathSciNetTable of Contents1. Some highlights; 2. Random walks and electric networks; 3. Special networks; 4. Uniform spanning trees; 5. Branching processes, second moments, and percolation; 6. Isoperimetric inequalities; 7. Percolation on transitive graphs; 8. The mass-transport technique and percolation; 9. Infinite electrical networks and Dirichlet functions; 10. Uniform spanning forests; 11. Minimal spanning forests; 12. Limit theorems for Galton–Watson processes; 13. Escape rate of random walks and embeddings; 14. Random walks on groups and Poisson boundaries; 15. Hausdorff dimension; 16. Capacity and stochastic processes; 17. Random walks on Galton–Watson trees.

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Book SynopsisA comprehensive survey of undergraduate mathematics, compressing four years of study into one robust overview.In The Math You Need, Thomas Mack provides a singular, comprehensive survey of undergraduate mathematics, compressing four years of math curricula into one volume. Without sacrificing rigor, this book provides a go-to resource for the essentials that any academic or professional needs. Each chapter is followed by numerous exercises to provide the reader an opportunity to practice what they learned. The Math You Need is distinguished in its use of the Bourbaki style—the gold standard for concision and an approach that mathematicians will find of particular interest. As ambitious as it is compact, this text embraces mathematical abstraction throughout, avoiding ad hoc computations in favor of general results.Covering nine areas—group theory, commutative algebra, linear algebra, topology, real analysis, complex analysis, number theo

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Book SynopsisVaclav Smil is my favorite author… Numbers Don't Lie takes everything that makes his writing great and boils it down into an easy-to-read format. I unabashedly recommend this book to anyone who loves learning.--Bill Gates, GatesNotesFrom the author of How the World Really Works, an essential guide to understanding how numbers reveal the true state of our world--exploring a wide range of topics including energy, the environment, technology, transportation, and food production.Vaclav Smil's mission is to make facts matter. An environmental scientist, policy analyst, and a hugely prolific author, he is Bill Gates' go-to guy for making sense of our world. In Numbers Don't Lie, Smil answers questions such as: What's worse for the environment--your car or your phone? How much do the world's cows weigh (and what does it matter)? And what makes people happy? From data about our societies and populations, through measures of the

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Book SynopsisEveryone who's had to get to grips with chance knows how tricky even its simplest manifestations can be. Its workings are a constant challenge to common sense: a run of luck goes bad just when you trust it; expert predictions of everything from the weather to elections prove hopelessly unreliable; proven health advice turns out to be anything but. Award-winning scientist and writer Robert Matthews shows us how we can cut through the conundrums of chance. He gives us access to some of the most potent intellectual tools ever developed, and explains how we can use them to guide our judgements and decisions. By the end of the book you'll know: -The secret to predicting coincidences; -The golden rule of professional gamblers; -How to tell when insurance is a waste of money; -When to heed health and diet warnings - and when to ignore them; -How to tell when forecasts are worth taking seriously; -How to make better choices in the face of uncertainty. Using a host of real-life examples, this groundbreaking book shows how the laws of probability can sharpen your decisions, make the most of your luck - and quite possibly transform your life.Trade ReviewIt takes an extraordinary writer to animate this driest of subjects for a general audience. That writer is Matthews ... At a time when mathematics needs charismatic ambassadors more than ever, Matthews has written a book of great significance. -- Oliver Moody * Times *Beguiling ... Matthews has the knack of explaining things clearly for the nonspecialist, leavening the formulae with intriguing snippets of history and biography ... his enthusiasm contributes to a lively and fascinating narrative. -- Ian Critchley * Sunday Times *Praise for Why Don't Spiders Stick to Their Webs: "Matthews gives us his wisdom like a beneficent and well-read uncle, entertaining his guests at the dinner table." -- Brian Clegg * Popular Science Books *Praise for 25 Big Ideas: "Robert Matthews has a gift for finding the simple, fascinating stories at the heart of concepts transforming the modern world" -- John Rennie, former Editor * Scientific American *

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Book SynopsisQuantitative genetics is the study of continuously varying traits which make up the majority of biological attributes of evolutionary and commercial interest. This book provides a much-needed up-to-date, in-depth yet accessible text for the field. In lucid language, the author guides readers through the main concepts of population and quantitative genetics and their applications. It is written to be approachable to even those without a strong mathematical background, including applied examples, a glossary of key terms, and problems and solutions to support students in grasping important theoretical developments and their relevance to real-world biology. An engaging, must-have textbook for advanced undergraduate and postgraduate students. Given its applied focus, it also equips researchers in genetics, genomics, evolutionary biology, animal and plant breeding, and conservation genetics with the understanding and tools for genetic improvement, comprehension of the genetic basis of human Trade Review'Quantitative genetics as a scientific discipline isn't dead just yet, despite predictions of its demise over many decades. In fact, it is very much alive in the genomics era, across a wide range of disciplines, including plant and animal breeding, evolutionary genetics and human (medical) genetics. Armando Caballero's timely textbook, a translation and update from his Spanish version, combines a description of the theory and methods underlying quantitative trait variation in populations with data examples and applications from modern genome technologies. It is an excellent introduction to the field, and demonstrates once again how population and quantitative genetics theory has stood the test of time and is highly relevant today.' Peter M. Visscher, University of Queensland'Armando Caballero's work is a masterful tour through both evolutionary and applied quantitative genetics. It provides a fruitful and unusual blend of population and quantitative genetics, and it will be extremely useful for anyone who wants to learn more about either of these fields.' Michael Whitlock, University of British Columbia'As the field within genetics having arguably the deepest history, quantitative genetics continues as a lively endeavour advancing understanding of the inheritance and change of traits that are continuous in their distributions and complex in the genetic and environmental influences on them. I welcome Caballero's text for new generations of students coming up to speed in this important and challenging field. The problems and questions concluding each chapter will especially aid them in testing their growing understanding. This text will also serve as a valuable resource for established practitioners of quantitative genetics.' Ruth G. Shaw, University of MinnesotaTable of ContentsPreface; Preface to the Spanish version; 1. Continuous variation; 2. Forces of change in the allele frequencies; 3. Components of phenotypic values and variances; 4. Inbreeding and coancestry; 5. Effective population size; 6. Estimation of genetic values, variances and covariances; 7. Mutation; 8. Consequences of inbreeding; 9. Artificial selection; 10. Natural selection; 11. Genomic analysis of quantitative traits; Solution to the problems and self-assessment questions; Glossary; References; Index.

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Book SynopsisThe Bellman function, a powerful tool originating in control theory, can be used successfully in a large class of difficult harmonic analysis problems and has produced some notable results over the last thirty years. This book by two leading experts is the first devoted to the Bellman function method and its applications to various topics in probability and harmonic analysis. Beginning with basic concepts, the theory is introduced step-by-step starting with many examples of gradually increasing sophistication, culminating with CalderónZygmund operators and end-point estimates. All necessary techniques are explained in generality, making this book accessible to readers without specialized training in non-linear PDEs or stochastic optimal control. Graduate students and researchers in harmonic analysis, PDEs, functional analysis, and probability will find this to be an incisive reference, and can use it as the basis of a graduate course.Trade Review'I first encountered Bellman functions about 35 years ago when advising engineers striving to minimize the expenditure of diamond chips in silicon grinding. Fifteen years later I was amused to learn that Nazarov, Treil, and Volberg successfully applied similar ideas to a variety of problems in harmonic analysis. Together with Vasyunin (and other analysts), they developed these techniques into a powerful tool which is carefully explained in the present book. The book is written on a level accessible to graduate students and I recommend it to everyone who wishes to join the Bellman functions club.' Mikhail Sodin, Tel Aviv UniversityTable of ContentsIntroduction; 1. Examples of Bellman functions; 2. What you always wanted to know about Stochastic Optimal Control, but were afraid to ask; 3. Conformal martingales models. Stochastic and classical Ahlfors-Beurling operators; 4. Dyadic models. Application of Bellman technique to upper estimates of singular integrals; 5. Application of Bellman technique to the end-point estimates of singular integrals.

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Book SynopsisMeasurement is a fundamental concept that underpins almost every aspect of the modern world. It is central to the sciences, social sciences, medicine, and economics, but it affects everyday life. We measure everything - from the distance of far-off galaxies to the temperature of the air, levels of risk, political majorities, taxes, blood pressure, IQ, and weight. The history of measurement goes back to the ancient world, and its story has been one of gradual standardization. Today there are different types of measurement, levels of accuracy, and systems of units, applied in different contexts. Measurement involves notions of variability, accuracy, reliability, and error, and challenges such as the measurement of extreme values.In this Very Short Introduction, David Hand explains the common mathematical framework underlying all measurement, the main approaches to measurement, and the challenges involved. Following a brief historical account of measurement, he discusses measurement as used in the physical sciences and engineering, the life sciences and medicine, the social and behavioural sciences, economics, business, and public policy.ABOUT THE SERIES: The Very Short Introductions series from Oxford University Press contains hundreds of titles in almost every subject area. These pocket-sized books are the perfect way to get ahead in a new subject quickly. Our expert authors combine facts, analysis, perspective, new ideas, and enthusiasm to make interesting and challenging topics highly readable.Table of ContentsREFERENCES; FURTHER READING; INDEX

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Book SynopsisThis text provides a state-of-the-art treatment of distributional regression, accompanied by real-world examples from diverse areas of application. Maximum likelihood, Bayesian and machine learning approaches are covered in-depth and contrasted, providing an integrated perspective on GAMLSS for researchers in statistics and other data-rich fields.

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Book SynopsisEvery business area relies on an understanding of statistics to succeed. Statistics for Business and Economics by Carlos Cortinhas and Ken Black shows students that the proper application of statistics in the business world goes hand-in-hand with good decision making. Every statistical tool presented in this book has a business application set in a global context and the many learning features and easy to use structure will engage and reassure each business statistic student.Table of ContentsUnit I Introduction 1 Introduction to Statistics 3 2 Charts and Graphs 19 3 Descriptive Statistics 51 4 Probability 102 Unit II Distributions and Sampling 5 Discrete Distributions 149 6 Continuous Distributions 194 7 Sampling and Sampling Distributions 234 Unit III Making Inferences About Population Parameters 8 Statistical Inference: Estimation for Single Populations 271 9 Statistical Inference: Hypothesis Testing for Single Populations 310 10 Statistical Inferences About Two Populations 363 11 Analysis of Variance and Design of Experiments 422 Unit IV Regression Analysis and Forecasting 12 Simple Regression Analysis and Correlation 487 13 Multiple Regression Analysis 545 14 Building Multiple Regression Models 578 15 Time-Series Forecasting and Index Numbers 625 Unit V Categorical Data and Non-parametric Statistics 16 Analysis of Categorical Data 685 17 Non-parametric Statistics 713 Appendices A Tables 765 B Answers to Selected Odd-Numbered Quantitative Problems 807 Glossary 819 Index 829 Website Materials 18 Statistical Quality Control 19 Decision Analysis

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Book SynopsisHow do I create a good research hypothesis?How do I know when my literature review is finished?What is the difference between a sample and a population?What is power and why is it important?In an increasingly data-driven world, it is more important than ever for students as well as professionals to better understand the process of research. This invaluable guide answers the essential questions that students ask about research methods in a concise and accessible way. Trade Review"This is a concise text that has good coverage of the basic concepts and elementary principles of research methods. It picks up where many traditional research methods texts stop and provides additional discussion on some of the hardest to understand concepts." -- University of Central Florida"I think it’s a great idea for a text (or series), and I have no doubt that the majority of students would find it helpful. The material is presented clearly, and it is easy to read and understand. My favorite example from those provided is on p. 7 where the author provides an actual checklist for evaluating the merit of a study. This is a great tool for students and would provide an excellent “practice” approach to learning this skill. Over time students wouldn’t need a checklist, but I think it would be invaluable for those students with little to no research experience." -- University of DenverTable of ContentsPart 1. Understanding the Research Process and Getting Started Part 2. Reviewing and Writing About Your Research Question Part 3. Introductory Ideas About Ethics Part 4. Research Methods: Knowing the Language, Knowing the Ideas Part 5. Sampling Ideas and Issues Part 6. Describing Data Using Descriptive Techniques Part 7. All About Testing and Measuring Part 8. Understanding Different Research Methods Part 9. All About Inference and Significance

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Book SynopsisThis well-respected text is designed for the first course in probability and statistics taken by students majoring in Engineering and the Computing Sciences. The prerequisite is one year of calculus. The text offers a balanced presentation of applications and theory. The authors take care to develop the theoretical foundations for the statistical methods presented at a level that is accessible to students with only a calculus background. They explore the practical implications of the formal results to problem-solving so students gain an understanding of the logic behind the techniques as well as practice in using them. The examples, exercises, and applications were chosen specifically for students in engineering and computer science and include opportunities for real data analysis.Table of ContentsChapter 1 - Introduction to Probability and Counting1.1 Interpreting Probabilities1.2 Sample Spaces and Events1.3 Permutations and CombinationsChapter SummaryExercisesReview ExercisesChapter 2 - Some Probability Laws2.1 Axioms of Probability2.2 Conditional Probability2.3 Independence and the Multiplication Rule2.4 Bayes' TheoremChapter SummaryExercisesReview ExercisesChapter 3 - Discrete Distributions3.1 Random Variables3.2 Discrete Probablility Densities3.3 Expectation and Distribution Parameters3.4 Geometric Distribution and the Moment Generating Function3.5 Binomial Distribution3.6 Negative Binomial Distribution3.7 Hypergeometric Distribution3.8 Poisson DistributionChapter SummaryExercisesReview ExercisesChapter 4 - Continuous Distributions4.1 Continuous Densities4.2 Expectation and Distribution Parameters4.3 Gamma, Exponential, and Chi-Squared Distributions4.4 Normal Distribution4.5 Normal Probability Rule and Chebyshev's Inequality4.6 Normal Approximation to the Binomial Distribution4.7 Weibull Distribution and Reliability4.8 Transformation of Variables4.9 Simulating a Continuous DistributionChapter SummaryExercisesReview ExercisesChapter 5 - Joint Distributions5.1 Joint Densities and Independence5.2 Expectation and Covariance5.3 Correlation5.4 Conditional Densities and Regression5.5 Transformation of VariablesChapter SummaryExercisesReview ExercisesChapter 6 - Descriptive Statistics6.1 Random Sampling6.2 Picturing the Distribution6.3 Sample Statistics6.4 BoxplotsChapter SummaryExercisesReview ExercisesChapter 7 - Estimation7.1 Point Estimation7.2 The Method of Moments and Maximum Likelihood7.3 Functions of Random Variables--Distribution of X7.4 Interval Estimation and the Central Limit TheoremChapter SummaryExercisesReview ExercisesChapter 8 - Inferences on the Mean and Variance of a Distribution8.1 Interval Estimation of Variability8.2 Estimating the Mean and the Student-t Distribution8.3 Hypothesis Testing8.4 Significance Testing8.5 Hypothesis and Significance Tests on the Mean8.6 Hypothesis Test on the Variance8.7 Alternative Nonparametric MethodsChapter SummaryExercisesReview ExercisesChapter 9 - Inferences on Proportions9.1 Estimating Proportions9.2 Testing Hypothesis on a Proportion9.3 Comparing Two Proportions Estimation9.4 Coparing Two Proportions: Hypothesis TestingChapter SummaryExercisesReview ExercisesChapter 10 - Comparing Two Means and Two Variances10.1 Point Estimation: Independent Samples10.2 Comparing Variances: The F Distribution10.3 Comparing Means: Variances Equal (Pooled Test)10.4 Comparing Means: Variances Unequal10.5 Compairing Means: Paried Data10.6 Alternative Nonparametric Methods10.7 A Note on TechnologyChapter SummaryExercisesReview ExercisesChapter 11 - Sample Linear Regression and Correlation11.1 Model and Parameter Estimation11.2 Properties of Least-Squares Estimators11.3 Confidence Interval Estimation and Hypothesis Testing11.4 Repeated Measurements and Lack of Fit11.5 Residual Analysis11.6 CorrelationChapter SummaryExercisesReview ExercisesChapter 12 - Multiple Linear Regression Models12.1 Least-Squares Procedures for Model Fitting12.2 A Matrix Approach to Least Squares12.3 Properties of the Least-Squares Estimators12.4 Interval Estimation12.5 Testing Hypothesis about Model Parameters12.6 Use of Indicator or "Dummy" Variables (Optional)12.7 Criteria for Variable Selection12.8 Model Transformation and Concluding RemarksChapter SummaryExercisesReview ExercisesChapter 13 - Analysis of Variance13.1 One-Way Classification Fixed-Effects Model13.2 Comparing Variances13.3 Pairwise Comparison13.4 Testing Contrasts13.5 Randomized Complete Block Design13.6 Latin Squares13.7 Random-Effects Models13.8 Design Models in Matrix Form13.9 Alternative Nonparametirc MethodsChapter SummaryExercisesReview ExercisesChapter 14 - Factorial Experiments14.1 Two-Factor Analysis of Variance14.2 Extension to Three Factors14.3 Random and Mixed Model Factorial Experiments14.4 2k Factorial Experiments 14.5 2k Factorial Experiments in an Incomplete Block Design 14.6 Fractional Factorial ExperimentsChapter SummaryExercisesReview ExercisesChapter 15 - Categorical Data15.1 Multinomial Distribution15.2 Chi-Squared Goodness of Fit Tests15.3 Testing for Independence15.4 Comparing ProportionsChapter SummaryExercisesReview ExercisesChapter 16 - Statistical Quality Control16.1 Properties of Control Charts16.2 Shewart Control Charts for Measurements16.3 Shewart Control Charts for Attributes16.4 Tolerance Limits16.5 Acceptance Sampling16.6 Two-Stage Acceptance Sampling16.7 Extensions in Quality ControlChapter SummaryExercisesReview ExerciesAppendix A - Statistical TablesAppendix B - Answers to Selected ProblemsAppendix C - Selected Derivations

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a huge range and FREE tracked UK delivery on ALL orders.

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Book SynopsisIn Measuring and Reasoning, Fred L. Bookstein examines the way ordinary arithmetic and numerical patterns are translated into scientific understanding, showing how the process relies on two carefully managed forms of argument: Abduction: the generation of new hypotheses to accord with findings that were surprising on previous hypotheses, and Consilience: the confirmation of numerical pattern claims by analogous findings at other levels of measurement. These profound principles include an understanding of the role of arithmetic and, more importantly, of how numerical patterns found in one study can relate to numbers found in others. More than 200 figures and diagrams illuminate the text. The book can be read with profit by any student of the empirical nature or social sciences and by anyone concerned with how scientists persuade those of us who are not scientists why we should credit the most important claims about scientific facts or theories.Table of ContentsPart I. The Basic Structure of a Numerical Inference: 1. Getting started; 2. Consilience as a rhetorical strategy; 3. Abduction and strong inference; Part II. A Sampler of Strategies: 4. The undergraduate course; Part III. Numerical Inference for General Systems: 5. Abduction and consilience in more complicated systems; 6. The singular value decomposition: a family of pattern engines for organized systems; 7. Morphometrics, and other examples; Part IV. What Is to Be Done?: 8. Retrospect and prospect.

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Book SynopsisSpectral analysis is widely used to interpret time series collected in diverse areas. This book covers the statistical theory behind spectral analysis and provides data analysts with the tools needed to transition theory into practice. Actual time series from oceanography, metrology, atmospheric science and other areas are used in running examples throughout, to allow clear comparison of how the various methods address questions of interest. All major nonparametric and parametric spectral analysis techniques are discussed, with emphasis on the multitaper method, both in its original formulation involving Slepian tapers and in a popular alternative using sinusoidal tapers. The authors take a unified approach to quantifying the bandwidth of different nonparametric spectral estimates. An extensive set of exercises allows readers to test their understanding of theory and practical analysis. The time series used as examples and R language code for recreating the analyses of the series are aTrade Review'Percival and Walden have written an excellent text for anyone who analyzes or wants to learn how to analyze time series data in the frequency domain. The aims and scope of the text are broad and require the skills that one would acquire in a basic course on mathematical statistics. The authors take a data analysis approach and relegate theoretical material to special sections or problems, and give ample references to the more theoretical details. The authors give philosophical as well as practical guidance in applying spectral techniques to time series data. This book is one of the best texts on the topic and would be useful as a reference for researchers. In addition, the book would be great as a textbook for a one semester/quarter course on the spectral analysis of time series.' David Stoffer, University of Pittsburgh'I used Spectral Analysis for Physical Applications several times for my spectral analysis courses. It was an excellent addition to the literature. This new book, considerably enlarged, will certainly have the same impact. The authors should be congratulated for a most valuable book.' Pedro A. Morettin, Universidade de São Paulo'Spectral Analysis for Univariate Time Series is an excellent step-by-step introduction to using Fourier methods in the statistical analysis of time series. The in-depth material, extensive exercises, practical advice, and illustrative data analyses provide valuable insights to readers of varied backgrounds.' Peter F. Craigmile, Ohio State University'This book will serve scientists and engineers in many fields with a general toolbox for spectral analysis. The fundamentals of non-parametric and parametric methods are presented, together with convincing examples and exercises. I especially appreciate the extensive chapter on combining direct spectral estimators, as todays standard toolbox definitely should include multitaper based spectral analysis.' Maria Sandsten, Lunds universitet'The book constitutes a lot more than an update of the authors' 1993 book Spectral Analysis for Physical Applications. The stand-out features are still the examples and exercises, but all data analysis has been done in R and considerable effort has gone into explanation, and how the methods fit in with alternatives. There is also a new chapter on simulation. The book is suitable not just as a reference for statisticians, engineers and physicists, but also as a graduate level text, particularly because of the chapter summaries and the thought-provoking comments at the section ends.' Barry Quinn, Macquarie University, Sydney'The excellent new textbook by Percival and Walden is an important source of information for anyone interested in time series analysis. Theoretical rigour combined with practical analysis of interesting real world data gives the reader a pedagogical journey into the world of spectral analysis and time series analysis. Highly recommended!' Alfred Hanssen, Universitetet i Tromsø – Norges arktiske universitetTable of Contents1. Introduction to spectral analysis; 2. Stationary stochastic processes; 3. Deterministic spectral analysis; 4. Foundations for stochastic spectral analysis; 5. Linear time-invariant filters; 6. Periodogram and other direct spectral estimators; 7. Lag window estimators; 8. Combining direct spectral estimators; 9. Parametric spectral estimators; 10. Harmonic analysis; 11. Simulation of time series.

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Book SynopsisThis modern and comprehensive guide to long-range dependence and self-similarity starts with rigorous coverage of the basics, then moves on to cover more specialized, up-to-date topics central to current research. These topics concern, but are not limited to, physical models that give rise to long-range dependence and self-similarity; central and non-central limit theorems for long-range dependent series, and the limiting Hermite processes; fractional Brownian motion and its stochastic calculus; several celebrated decompositions of fractional Brownian motion; multidimensional models for long-range dependence and self-similarity; and maximum likelihood estimation methods for long-range dependent time series. Designed for graduate students and researchers, each chapter of the book is supplemented by numerous exercises, some designed to test the reader''s understanding, while others invite the reader to consider some of the open research problems in the field today.Trade Review'This is a marvelous book that brings together both classical background material and the latest research results on long-range dependence. The book is written so that it can be used as a main source by a graduate student, including all the essential proofs. I highly recommend this book.' Mark M. Meerschaert, Michigan State University'This volume lays a rock-solid foundation for the subjects of long-range dependence and self-similarity. It also provides an up-to-date survey of more specialized topics at the center of this research area. The text is very readable and suitable for graduate courses, as it is self-contained and does not require more than an introductory course on stochastic calculus and time series. It is also written with the necessary level of mathematical detail to make it suitable for self-study. I particularly enjoyed the very nice introduction to fractional Brownian motion, its different representations, its stochastic calculus, and the connection to fractional calculus. I strongly recommend this book, which is a welcome addition to the literature and useful for a large audience.' Eric Moulines, Centre de Mathématiques Appliquées, École Polytechnique, Paris'This book provides a modern, rigorous introduction to long-range dependence and self-similarity. The authors write with wonderful clarity, covering fundamental as well as selected specialized topics. The book can be highly recommended to anybody interested in mathematical foundations of long memory and self-similar processes.' Jan Beran, University of Konstanz, Germany'This is the most readable and lucid account I have seen on long-range dependence and self-similarity. Pipiras and Taqqu present a time-series-centric view of this subject that should appeal to both practitioners and researchers in stochastic processes and statistics. I was especially enamored by the insightful comments on the history of the subject that conclude each chapter. This alone is worth the price of the book!' Richard Davis, Columbia University, New YorkTable of ContentsList of abbreviations; Notation; Preface; 1. A brief overview of times series and stochastic processes; 2. Basics of long-range dependence and self-similarity; 3. Physical models for long-range dependence and self-similarity; 4. Hermite processes; 5. Non-central and central limit theorems; 6. Fractional calculus and integration of deterministic functions with respect to FBM; 7. Stochastic integration with respect to fractional Brownian motion; 8. Series representations of fractional Brownian motion; 9. Multidimensional models; 10. Maximum likelihood estimation methods; Appendix A. Auxiliary notions and results; Appendix B. Integrals with respect to random measures; Appendix C. Basics of Malliavin calculus; Appendix D. Other notes and topics; Bibliography; Index.

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Book SynopsisBased on a course taught by the author, this book combines the theoretical underpinnings of statistics with the practical analysis of Earth sciences data using MATLAB. The book is organized to introduce the underlying concepts, and then extends these to the data, covering methods that are most applicable to Earth sciences. Topics include classical parametric estimation and hypothesis testing, and more advanced least squares-based, nonparametric, and resampling estimators. Multivariate data analysis, not often encountered in introductory texts, is presented later in the book, and compositional data is treated at the end. Datasets and bespoke MATLAB scripts used in the book are available online, as well as additional datasets and suggested questions for use by instructors. Aimed at entering graduate students and practicing researchers in the Earth and ocean sciences, this book is ideal for those who want to learn how to analyse data using MATLAB in a statistically-rigorous manner.Trade Review'One of the main strengths of this book is the combination of mathematical rigor with extensive examples, allowing readers to work through case studies to better understand the concepts presented. The tool used for this purpose is MATLAB, which is widely used in the earth science community. Examples are drawn from geophysics, astrophysics, and anthropology (among others). Both the scripts and the data examples used in the book are available for download from the publisher's website. … This book is an ideal guide for graduate students seeking a comprehensive and rigorous understanding of statistical methods in earth sciences. For the more mature earth scientist (and I include myself in that number), it provides a useful reference to widely used statistical concepts that many of us regularly encounter.' Lucy MacGregor, The Leading Edge'… this book will be a welcome and invaluable addition to any earth scientist's library.' Sven Treitel, The Leading EdgeTable of ContentsPreface; 1. Probability concepts; 2. Statistical concepts; 3. Statistical distributions; 4. Characterization of data; 5. Point, interval and ratio estimators; 6. Hypothesis testing; 7. Nonparametric methods; 8. Resampling methods; 9. Linear regression; 10. Multivariate statistics; 11. Compositional data; Appendix: MATLAB functions to produce ternary diagrams; References; Index.

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Book SynopsisThe primary data driver behind US drug policy is the National Survey on Drug Use and Health. This insider history traces the evolution of the survey and how the survey has interacted with the political and social climate of the country, from its origins during the Vietnam War to its role in the war on drugs. The book includes first-hand accounts that explain how the data was used and misused by political leaders, why changes were made in the survey design, and what challenges researchers faced in communicating statistical principles to policymakers and leaders. It also makes recommendations for managing survey data collection and reporting in the context of political pressures and technological advances. Survey research students and practitioners will learn practical lessons about questionnaire design, mode effects, sampling, nonresponse, weighting, editing, imputation, statistical significance, and confidentiality. The book also includes common-language explanations of key terms and pTrade Review'This book is a first of a kind 'tell all' about data. Not just any data, but the very data that courted the national public policy machine into decades of debate about how to solve the very problem it defined: America's addiction to drugs. When she would not cooperate with our wishes and say what we desperately wanted to hear - America is drug free - we tried to change her. As this book documents so well, silly us. The data are the data; what we do with it reflects our own vices. This book is a must read for anyone who wants a thorough understanding of the nexus between data systems and public policy.' John Carnevale, Carnevale Associates, LLC'Sound methodology is a sine qua non of quality measurements. It doesn't happen magically, as Joseph Gfroerer expertly shows us. Data scientists will benefit from the details of the National Survey on Drug Use and Health data generating process; however, the book will also be an invaluable source for policy makers too as it illustrates and informs though fascinating examples of the interplay between political decision making and survey statistics.' Frauke Kreuter, University of Maryland, University of Mannheim and Institute for Employment Research'Rare is it to find a comprehensive methodological and political history of an important social and epidemiological resource such as the NSDUH. Gfroerer's careful documentation of the evolution of this ongoing national survey make for a fascinating case study of real world applied research.' Timothy P. Johnson, University of Illinois, ChicagoTable of ContentsIntroduction; 1. President Nixon launches the war on drugs; 2. The survey continues, as illicit drug use peaks; 3. Cocaine and new directions for the survey; 4. The White House needs data and a bigger survey; 5. Criticism, correction, and communication; 6. The survey moves to SAMHSA; 7. Rising drug use in the 1990s; 8. Better sample, better analysis, but not always; 9. A perfect redesign storm; 10. Continuing survey design improvements; 11. Analytic bankruptcy, reorganization, recovery, and resilience; 12. How to redesign an ongoing survey, or not; 13. Lessons learned and future challenges.

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Book SynopsisThis book provides a rigorous yet accessible introduction to the scientific study of sociology and other social sciences. It is designed to provide students with the basic tools needed to be both critical consumers and beginning producers of scholarly social science research.Trade Review'Kellstedt, Whitten, and Tuch provide an accessible, sophisticated text. With well-chosen examples, they show why a full understanding of research design, theory construction, and causal inference is essential for effective use of our modern toolkit of data analysis techniques.' Stephen L. Morgan, Johns Hopkins University'A comprehensive and well-written introduction to the techniques and logic of social research. The logic and application of a wide range of methodological techniques are explained eloquently and clearly, and the examples used cut across social science disciplines. This book should be widely used in methods courses across the social sciences.' George Wilson, University of Miami'This valuable textbook is unique for two reasons: first, it seamlessly integrates theory, research design, and data analysis, providing students with the foundation required to develop empirically grounded research projects that can make theoretical progress in social science. Second, it is accessible and engaging, drawing students in and showing them how stimulating and exciting social research can be.' Michael Hughes, Virginia TechTable of ContentsList of Figures; List of Tables; Preface; Acknowledgments; 1. The Scientific Study of Society; 2. The Art of Theory Building; 3. Evaluating Causal Relationships; 4. Research Design; 5. Survey Research; 6. Measuring Concepts of Interest; 7. Getting to Know Your Data; 8. Probability and Statistical Inference; 9. Bivariate Hypothesis Testing; 10. Two-Variable Regression Models; 11. Multiple Regression; 12. Putting it all Together to Produce Effective Research; Appendix A. Critical Values of Chi-square; Appendix B. Critical Values of t; Appendix C. The Λ Link Function for Binomial Logit Models; Appendix D. The Φ Link Function for Binomial Probit Models; References; Index.

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Book SynopsisThanks to the driving forces of the Ità calculus and the Malliavin calculus, stochastic analysis has expanded into numerous fields including partial differential equations, physics, and mathematical finance. This book is a compact, graduate-level text that develops the two calculi in tandem, laying out a balanced toolbox for researchers and students in mathematics and mathematical finance. The book explores foundations and applications of the two calculi, including stochastic integrals and differential equations, and the distribution theory on Wiener space developed by the Japanese school of probability. Uniquely, the book then delves into the possibilities that arise by using the two flavors of calculus together. Taking a distinctive, path-space-oriented approach, this book crystallizes modern day stochastic analysis into a single volume.Trade Review'This book is a comprehensive guide to stochastic analysis related to Brownian motion. It contains the basis of the Itô calculus and the Malliavin calculus, which are the heart of the modern analysis of Brownian motion. The book is self-contained and it is accessible for graduate students and researchers who wish to learn about stochastic differential equations.' Hiroshi Kunita'A very readable text on stochastic integrals and differential equations for novices to the area, including a substantial chapter on analysis on Wiener space and Malliavin calculus. The many examples and applications included, such as Schilder's theorem, Ramer's theorem, semi-classical limits, quadratic Wiener functionals, and rough paths, give additional value.' David Elworthy, University of Warwick'This book develops stochastic analysis from the path space point of view, with an emphasis on the connection between Brownian motion and partial differential equations. A detailed treatment of Malliavin calculus and important applications in finance and physics make this monograph an innovative and useful reference in the field.' David Nualart, University of KansasTable of ContentsPreface; Frequently used notation; 1. Fundamentals of continuous stochastic processes; 2. Stochastic integrals and Itô's formula; 3. Brownian motion and Laplacian; 4. Stochastic differential equations; 5. Malliavin calculus; 6. Black-Scholes model; 7. Semiclassical limit; Appendix; References; Subject index.

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Book SynopsisFrom Measures to Itô Integrals gives a clear account of measure theory, leading via L2-theory to Brownian motion, Itô integrals and a brief look at martingale calculus. Modern probability theory and the applications of stochastic processes rely heavily on an understanding of basic measure theory. This text is ideal preparation for graduate-level courses in mathematical finance and perfect for any reader seeking a basic understanding of the mathematics underpinning the various applications of Itô calculus.Table of ContentsPreface; 1. Probability and measure; 2. Measures and distribution functions; 3. Measurable functions/random variables; 4. Integration and expectation; 5. Lp-spaces and conditional expectation; 6. Discrete-time martingales; 7. Brownian motion; 8. Stochastic integrals; Bibliography; Index.

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Book SynopsisThis textbook provides an introduction to the scientific study of sociology and other social sciences. It offers the basic tools necessary for readers to become both critical consumers and beginning producers of scientific research on society. The authors present an integrated approach to research design and empirical analyses in which researchers can develop and test causal theories. They use examples from social science research that students will find engaging and inspiring and that will help them to understand key concepts. The book makes technical materials accessible to students who might otherwise be intimidated by mathematical examples. This new text, with the addition of sociologist Steven A. Tuch to the author team, follows the successful format, approach, and pedagogical features in Paul M. Kellstedt and Guy D. Whitten''s bestselling text, The Fundamentals of Political Science Research, now in its third edition. Workbooks in Stata, SPSS, and R, three of the most popular statTrade Review'Kellstedt, Whitten, and Tuch provide an accessible, sophisticated text. With well-chosen examples, they show why a full understanding of research design, theory construction, and causal inference is essential for effective use of our modern toolkit of data analysis techniques.' Stephen L. Morgan, Johns Hopkins University'A comprehensive and well-written introduction to the techniques and logic of social research. The logic and application of a wide range of methodological techniques are explained eloquently and clearly, and the examples used cut across social science disciplines. This book should be widely used in methods courses across the social sciences.' George Wilson, University of Miami'This valuable textbook is unique for two reasons: first, it seamlessly integrates theory, research design, and data analysis, providing students with the foundation required to develop empirically grounded research projects that can make theoretical progress in social science. Second, it is accessible and engaging, drawing students in and showing them how stimulating and exciting social research can be.' Michael Hughes, Virginia TechTable of ContentsList of Figures; List of Tables; Preface; Acknowledgments; 1. The Scientific Study of Society; 2. The Art of Theory Building; 3. Evaluating Causal Relationships; 4. Research Design; 5. Survey Research; 6. Measuring Concepts of Interest; 7. Getting to Know Your Data; 8. Probability and Statistical Inference; 9. Bivariate Hypothesis Testing; 10. Two-Variable Regression Models; 11. Multiple Regression; 12. Putting it all Together to Produce Effective Research; Appendix A. Critical Values of Chi-square; Appendix B. Critical Values of t; Appendix C. The Λ Link Function for Binomial Logit Models; Appendix D. The Φ Link Function for Binomial Probit Models; References; Index.

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Book SynopsisThis concise introduction covers all of the measure theory and probability most useful for statisticians. Originating from the authors' own graduate course, it is perfect for a two-term course or for self-study. It is especially useful to graduate students in related fields who want to shore up their mathematical foundation.Table of ContentsPreface; Acknowledgements; 1. Point sets and certain classes of sets; 2. Measures: general properties and extension; 3. Measurable functions and transformations; 4. The integral; 5. Absolute continuity and related topics; 6. Convergence of measurable functions, Lp-spaces; 7. Product spaces; 8. Integrating complex functions, Fourier theory and related topics; 9. Foundations of probability; 10. Independence; 11. Convergence and related topics; 12. Characteristic functions and central limit theorems; 13. Conditioning; 14. Martingales; 15. Basic structure of stochastic processes; References; Index.

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Book SynopsisProbability has applications in many areas of modern science, not to mention in our daily life. Its importance as a mathematical discipline cannot be overrated, and it is a fascinating and surprising topic in its own right. This engaging textbook with its easy-to-follow writing style provides a comprehensive yet concise introduction to the subject. It covers all of the standard material for undergraduate and first-year-graduate-level courses as well as many topics that are usually not found in standard texts, such as Bayesian inference, Markov chain Monte Carlo simulation, and Chernoff bounds.Trade Review'This is an attractive textbook for an introductory probability course at the upper undergraduate level. It covers not only the standard material for such a course (discrete probability, the axioms of probability, conditional probability, discrete and continuous random variables, jointly distributed random variables, limit theorems, Markov chains, etc.) but also some topics that might be considered more unusual, such as Kelly betting, renewal-reward stochastic processes, and the law of iterated logarithms. Topics from statistics (confidence intervals, Student-t distribution, Baysian inference, etc.) also appear. The book is quite well-written, nicely motivated, demonstrates considerable enthusiasm for the material, and gives lots of examples of the usefulness of probability. Mark Hunacek, MAA ReviewsAs with its predecessor, Probability: A Lively Introduction has an engaging and sympathetic tone which will be welcomed by those wrestling with this endlessly fascinating but tricky subject. Robert A. J. Matthews, Significance'This text serves as an excellent introduction to probability theory. Tijms has achieved the difficult feat of writing a book that is useful as both a textbook and a reference resource. As he wisely points out in the introduction, a key step in attracting students' attention to this field is providing clear, natural examples. In this book, every chapter is full of such examples. Besides covering the topics expected in an entry-level book, the author also covers multivariate normal distributions and the chi-square test, generating functions, and Markov chains (both the discrete time and the continuous time cases). Many students will appreciate the four appendixes at the end of the book. The first three contain the necessary background in enumerative combinatorics, set theory, and calculus, and make the book even more widely accessible in doing so. The fourth appendix introduces a more advanced concept, Monte Carlo simulations. There are plenty of excellent exercises in each chapter, half of which come with detailed solutions (not just numerical answers).' M. Bona, Choice'In this book, Henk Tijms aims at sharing his passion and enthusiasm for the fascinating world of probability with his readers. I can only say that he convincingly succeeded to do so!' Ivo Adan, European Journal of Operational Research'A most interesting aspect of this text is its exposition. The text relies heavily on a narrative approach: graphics and lengthy displayed calculations are infrequent. Happily, the author writes well, with an obvious enthusiasm (the 'liveliness' of the title is correct) and a gift for choosing appropriate and revealing examples. Often these examples provide jumping-off points for further discussion or exploration. The material is also presented accurately and at an appropriate level of rigor. One oddity is that theorems are not presented in the classic boxed-off fashion followed by a clearly marked proof. Instead, theorems (which the author calls 'rules') are stated and proofs or sketches of proofs are given within the narrative. The text also features an abundance of interesting exercises, ranging from elementary to challenging. Full solutions to the odd-numbered problems appear at the end of the book, and the publisher offers a password-protected site with solutions to all the exercises in the text.' Thomas Polaski, Mathematical Reviews'This is indeed a lively introduction to probability theory. The book is addressed basically to undergraduate students and their teachers. All traditional notions, results, ideas and techniques are included and discussed in detail. This is done smoothly and gradually in a master style.' Jordan M. Stoyanov, ZB Math ReviewsTable of Contents1. Foundations of probability theory; 2. Conditional probability; 3. Discrete random variables; 4. Continuous random variables; 5. Jointly distributed random variables; 6. Multivariate normal distribution; 7. Conditioning by random variables; 8. Generating functions; 9. Additional topics in probability; 10. Discrete-time Markov chains; 11. Continuous-time Markov chains.

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Book SynopsisMarkov chains and hidden Markov chains have applications in many areas of engineering and genomics. This book provides a basic introduction to the subject by first developing the theory of Markov processes in an elementary discrete time, finite state framework suitable for senior undergraduates and graduates. The authors then introduce semi-Markov chains and hidden semi-Markov chains, before developing related estimation and filtering results. Genomics applications are modelled by discrete observations of these hidden semi-Markov chains. This book contains new results and previously unpublished material not available elsewhere. The approach is rigorous and focused on applications.Trade Review'… this book is of interest to researchers attracted by hidden Markov and semi-Markov models. It covers probabilistic and statistical treatments of the considered topics, and introduces the reader … to possible applications, mainly in genomics. Hence, Ph.D. students and specialists in the area of hidden Markov processes are invited to consider this book as a reference in their activities.' Antonio Di Crescenzo, MathSciNet'… dedicated mostly to graduate students and providing a rigorous and rather complete mathematical introduction to the theory of hidden Markov models as well as hidden semi-Markov models under main assumption that the hidden process is a finite state Markov chain. The semi-Markov models appear when the assumption that the length of time the chain spends in any state is geometrically distributed is relaxed. The authors carefully construct these processes on the canonical probability space and then derive filters and smoother, as well as the Viterbi estimates. The central role plays the EM Algorithm.' Jerzy Ombach, ZB Math ReviewsTable of ContentsPreface; 1. Observed Markov chains; 2. Estimation of an observed Markov chain; 3. Hidden Markov models; 4. Filters and smoothers; 5. The Viterbi algorithm; 6. The EM algorithm; 7. A new Markov chain model; 8. Semi-Markov models; 9. Hidden semi-Markov models; 10. Filters for hidden semi-Markov models; Appendix A. Higher order chains; Appendix B. An example of a second order chain; Appendix C. A conditional Bayes theorem; Appendix D. On conditional expectations; Appendix E. Some molecular biology; Appendix F. Earlier applications of hidden Markov chain models; References; Index.

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Book SynopsisHumanity is confronted by and attracted to extremes. Extreme events shape our thinking, feeling, and actions; they echo in our politics, media, literature, and science. We often associate extremes with crises, disasters, and risks to be averted, yet extremes also have the potential to lead us towards new horizons. Featuring essays by leading intellectuals and public figures arising from the 2017 Darwin College Lectures, this volume explores ''extreme'' events, from the election of President Trump, the rise of populism, and the Brexit referendum, to the 2008 financial crisis, the Syrian war, and climate change. It also celebrates ''extreme'' achievements in the realms of health, exploration, and scientific discovery. A fascinating, engaging, and timely collection of essays by renowned scholars, journalists, and intellectuals, this volume challenges our understanding of what is normal and what is truly extreme, and sheds light on some of the issues facing humanity in the twenty-first cenTable of ContentsNotes on contributors; Acknowledgements; On the notion of 'extremes' Julius F. W. Weitzdörfer; 1. Dealing with extremism David Runciman; 2. Extreme weather Emily Shuckburgh; 3. Probability, risk, and extremes Nassim Nicholas Taleb; 4. Extreme rowing Roz Savage; 5. Extremes of war: stories of survival from Syria Lyse Doucet; 6. Extreme politics: the four waves of national populism in the West Matthew Goodwin; 7. Extreme longevity Sarah Harper; 8. Extremes of power in the universe Andrew C. Fabian; Index.

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Book SynopsisEach chapter of this book covers specific topics in statistical analysis, such as robust alternatives to t-tests or how to develop a questionnaire. They also address particular questions on these topics, which are commonly asked by human-computer interaction (HCI) researchers when planning or completing the analysis of their data. The book presents the current best practice in statistics, drawing on the state-of-the-art literature that is rarely presented in HCI. This is achieved by providing strong arguments that support good statistical analysis without relying on mathematical explanations. It additionally offers some philosophical underpinnings for statistics, so that readers can see how statistics fit with experimental design and the fundamental goal of discovering new HCI knowledge.Trade Review'If you, and your experiments, have been bruised by statistical misfortune, then this is the book for you. Paul Cairns' wise and pragmatic advice talks us through the practical use of statistics in Human-Computer Interaction, showing his own bruises when necessary. This should become the standard reference that the field needs.' Alan Blackwell, University of Cambridge'In Human-Computer Interaction, we gather data from experiment designs that are often more complex or messy than those presented as examples in a basic textbook on statistics. Cairns presents digestible information for an interdisciplinary audience with expertise and authority. I will be buying a copy of this book for my students, and also one for myself!' Regan Mandryk, University of Saskatchewan, Canada'This is a must-read for novice or well-established researchers alike, who are worried about whether they are conducting the correct statistical analyses of their data. Paul Cairns makes learning about statistics seem both fun and interesting. I'm confident that this book will positively impact the quality of future Human-Computer Interaction research.' Anna L. Cox, University College London Interaction CentreTable of ContentsGetting started; Part I. Why We Use Statistics: 1. How statistics support science; 2. Testing the null; 3. Constraining Bayes; 4. Effects: what tests test; Part II. How To Use Statistics: 5. Planning your statistical analysis; 6. A cautionary tail: why you should not do a one-tailed test; 7. Is this normal?; 8. Sorting out outliers; 9. Power and two types of error; 10. Using nonparametric tests; 11. A robust t-test; 12. The ANOVA family and friends; 13. Exploring, over-testing and fishing; 14. When is a correlation not a correlation?; 15. What makes a good Likert item?; 16. The meaning of factors; 17. Unreliable reliability: the problem of Cronbach's alpha; 18. Tests for questionnaires.

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Book SynopsisThis book, meant for graduate students and researchers, explores the connections between numerical approximation and statistical inference from a game and decision theoretic perspective, and illustrates these interplays by addressing problems related to numerical homogenization, operator adapted wavelets, and fast solvers.Trade Review'This is a terrific book. A hot new topic, first rate mathematics, real applications. It's an important contribution by marvelous scholars.' Persi Diaconis, Stanford University'This book does a masterful job of bringing together the two seemingly unrelated fields of numerical approximation and statistical inference to produce a general framework for developing solvers that are both provably accurate and scale to extremely large problem sizes. It seamlessly integrates concepts from numerical approximation, statistical inference, information-based complexity, and game theory to reveal a rich mathematical structure that forms a comprehensive foundation for solver development. Of tremendous value to the practitioner is a thorough analysis of solver accuracy and computational requirements. In addition to providing a comprehensive guide to solver development and analysis this book presents a unique perspective that provides numerous valuable insights into the solution of science and engineering problems.' Don Hush, University of New Mexico'This unique book provides a novel game-theoretic approach to Probabilistic Scientific Computing by exploring the interplay between numerical approximation and statistical inference, and exploits such links to develop new fast methods for solving partial differential equations. Gamblets are magic basis functions resulting from a clever adversarial zero sum game between two players and can be used in modeling multiscale problems with no scale separation in numerical homogenization. The book provides original exposition to many topics of the modern era of scientific computing, including sparse representation of Gaussian fields, probabilistic interpretation of numerical errors, linear complexity algorithms, and rigorous settings in the Sobolev and Banach spaces of these topics. It is appropriate for graduate-level courses and as a valuable reference for any scientist who is interested in rigorous understanding and use of modern numerical algorithms in problems where data and mathematical models co-exist.' George Karniadakis, Brown UniversityTable of Contents1. Introduction; 2. Sobolev space basics; 3. Optimal recovery splines; 4. Numerical homogenization; 5. Operator adapted wavelets; 6. Fast solvers; 7. Gaussian fields; 8. Optimal recovery games on $\mathcal{H}^{s}_{0}(\Omega)$; 9. Gamblets; 10. Hierarchical games; 11. Banach space basics; 12. Optimal recovery splines; 13. Gamblets; 14. Bounded condition numbers; 15. Exponential decay; 16. Fast Gamblet Transform; 17. Gaussian measures, cylinder measures, and fields on $\mathcal{B}$; 18. Recovery games on $\mathcal{B}$; 19. Game theoretic interpretation of Gamblets; 20. Survey of statistical numerical approximation; 21. Positive definite matrices; 22. Non-symmetric operators; 23. Time dependent operators; 24. Dense kernel matrices; 25. Fundamental concepts.

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Book SynopsisThis practical introduction covers mathematical methods for the analysis of stochastic models and their biological applications. Based on courses taught at the University of Oxford, the book can be used for self-study or as a supporting text for advanced undergraduate or beginning graduate-level courses in applied mathematics.Trade Review'The text can be used effectively for solitary study or as a textbook for a course offered at the boundary between undergraduate and beginning graduate study … This is a remarkable, even admirable, work that bears the mark of its Oxford origins. Its potential audience includes chemists and mathematicians as well as adventuresome biologists and physicists and perhaps even bright or intrepid general readers.' A. E. Viste, Choice'This textbook is an example-driven introduction to stochastic modeling in mathematical biology … Beyond serving as a course textbook, the book could serve as a good general introduction to the area of stochastic modeling in biology for researchers, particularly given the copious citations to more specialist texts.' Andrew Krause, MAA Reviews'Erban and Chapman's Stochastic Modelling of Reaction–Diffusion Processes will be valuable both as a reference for practitioners and as a textbook for a graduate course on stochastic modelling. Every chapter includes problems for the reader. The problems are well written and appropriate for most intended readers of the book. I hope that this book is widely adopted and that it becomes a standard textbook in the field.' Michael A. Salins, Mathematical Reviews/MathSciNet Review'This book is also available at a reduced price as an e-book on Kindle. Based on the sample I viewed, all the features of the printed book have been perfectly preserved, with no loss of clarity in the layout or the mathematical symbols or the graphs and diagrams.' David Hopkins, The Mathematical GazetteTable of Contents1. Stochastic simulation of chemical reactions; 2. Deterministic versus stochastic modelling; 3. Stochastic differential equations; 4. Diffusion; 5. Efficient stochastic modelling of chemical reactions; 6. Stochastic reaction-diffusion models; 7. SSAs for reaction-diffusion-advection processes; 8. Microscopic models of Brownian motion; 9. Multiscale and multi-resolution methods; Appendix A. Deterministic modelling of chemical reactions; Appendix B. Discrete probability distributions; Appendix C. Continuous probability distributions; References; Index.

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Book SynopsisEach chapter of this book covers specific topics in statistical analysis, such as robust alternatives to t-tests or how to develop a questionnaire. They also address particular questions on these topics, which are commonly asked by human-computer interaction (HCI) researchers when planning or completing the analysis of their data. The book presents the current best practice in statistics, drawing on the state-of-the-art literature that is rarely presented in HCI. This is achieved by providing strong arguments that support good statistical analysis without relying on mathematical explanations. It additionally offers some philosophical underpinnings for statistics, so that readers can see how statistics fit with experimental design and the fundamental goal of discovering new HCI knowledge.Trade Review'If you, and your experiments, have been bruised by statistical misfortune, then this is the book for you. Paul Cairns' wise and pragmatic advice talks us through the practical use of statistics in Human-Computer Interaction, showing his own bruises when necessary. This should become the standard reference that the field needs.' Alan Blackwell, University of Cambridge'In Human-Computer Interaction, we gather data from experiment designs that are often more complex or messy than those presented as examples in a basic textbook on statistics. Cairns presents digestible information for an interdisciplinary audience with expertise and authority. I will be buying a copy of this book for my students, and also one for myself!' Regan Mandryk, University of Saskatchewan, Canada'This is a must-read for novice or well-established researchers alike, who are worried about whether they are conducting the correct statistical analyses of their data. Paul Cairns makes learning about statistics seem both fun and interesting. I'm confident that this book will positively impact the quality of future Human-Computer Interaction research.' Anna L. Cox, University College London Interaction CentreTable of ContentsGetting started; Part I. Why We Use Statistics: 1. How statistics support science; 2. Testing the null; 3. Constraining Bayes; 4. Effects: what tests test; Part II. How To Use Statistics: 5. Planning your statistical analysis; 6. A cautionary tail: why you should not do a one-tailed test; 7. Is this normal?; 8. Sorting out outliers; 9. Power and two types of error; 10. Using nonparametric tests; 11. A robust t-test; 12. The ANOVA family and friends; 13. Exploring, over-testing and fishing; 14. When is a correlation not a correlation?; 15. What makes a good Likert item?; 16. The meaning of factors; 17. Unreliable reliability: the problem of Cronbach's alpha; 18. Tests for questionnaires.

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Book SynopsisBased on the author''s university lecture courses, this book presents the many facets of one of the most important open problems in operator algebra theory. Central to this book is the proof of the equivalence of the various forms of the problem, including forms involving C*-algebra tensor products and free groups, ultraproducts of von Neumann algebras, and quantum information theory. The reader is guided through a number of results (some of them previously unpublished) revolving around tensor products of C*-algebras and operator spaces, which are reminiscent of Grothendieck''s famous Banach space theory work. The detailed style of the book and the inclusion of background information make it easily accessible for beginning researchers, Ph.D. students, and non-specialists alike.Trade Review'This is a very rich and detailed monograph on an enormously important subject. It is written in the crystal clear and elegant style that is the hallmark of its author, and it offers a lot of information to specialists and novices alike. The book will certainly become an authoritative guide.' Dirk Werner, London Mathematical Society Student Texts'This book is jam packed with information, and should be an invaluable guide to anyone interested in these ideas … For the complete picture and the recent advances, Pisier's book is the place to go.' Bulletin of the American Mathematical SocietyTable of ContentsIntroduction; 1. Completely bounded and completely positive maps: basics; 2. Completely bounded and completely positive maps: a tool kit; 3. C*-algebras of discrete groups; 4. C*-tensor products; 5. Multiplicative domains of c.p. maps; 6. Decomposable maps; 7. Tensorizing maps and functorial properties; 8. Biduals, injective von Neumann algebras and C*-norms; 9. Nuclear pairs, WEP, LLP and QWEP; 10. Exactness and nuclearity; 11. Traces and ultraproducts; 12. The Connes embedding problem; 13. Kirchberg's conjecture; 14. Equivalence of the two main questions; 15. Equivalence with finite representability conjecture; 16. Equivalence with Tsirelson's problem; 17. Property (T) and residually finite groups. Thom's example; 18. The WEP does not imply the LLP; 19. Other proofs that C(n) < n. Quantum expanders; 20. Local embeddability into ${\mathscr{C}}$ and non-separability of $(OS_n, d_{cb})$; 21. WEP as an extension property; 22. Complex interpolation and maximal tensor product; 23. Haagerup's characterizations of the WEP; 24. Full crossed products and failure of WEP for $\mathscr{B}\otimes_{\min}\mathscr{B}$; 25. Open problems; Appendix. Miscellaneous background; References; Index.

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Book SynopsisMany areas of mining engineering gather and use statistical information, provided by observing the actual operation of equipment, their systems, the development of mining works, surface subsidence that accompanies underground mining, displacement of rocks surrounding surface pits and underground drives and longwalls, amongst others. In addition, the actual modern machines used in surface mining are equipped with diagnostic systems that automatically trace all important machine parameters and send this information to the main producerâs computer. Such data not only provide information on the technical properties of the machine but they also have a statistical character. Furthermore, all information gathered during stand and lab investigations where parts, assemblies and whole devices are tested in order to prove their usefulness, have a stochastic character. All of these materials need to be developed statistically and, more importantly, based on these results mining engineers must make decisions whether to undertake actions, connected with the further operation of the machines, the further development of the works, etc. For these reasons, knowledge of modern statistics is necessary for mining engineers; not only as to how statistical analysis of data should be conducted and statistical synthesis should be done, but also as to understanding the results obtained and how to use them to make appropriate decisions in relation to the mining operation.This book on statistical analysis and synthesis starts with a short repetition of probability theory and also includes a special section on statistical prediction. The text is illustrated with many examples taken from mining practice; moreover the tables required to conduct statistical inference are included.Table of Contents1.Fundamentals 2.Some areas of application of mathematical statistics in mining 3.Analysis of data 4.Synthesis of data 5.Relationships between random variables 6.Synthesis of data—regression analysis 7.Special topic: Prediction 8.Explanations of some important terms 9.Statistical tables

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Book SynopsisLearn to evaluate and apply statistics in medicine, medical research, and all health-related fieldsA  Doody's Core Title for 2023!Basic & Clinical Biostatistics provides medical students, researchers, and practitioners with the knowledge needed to develop sound judgment about data applicable to clinical care. This fifth edition has been updated throughout to deliver a comprehensive, timely introduction to biostatistics and epidemiology as applied to medicine, clinical practice, and research. Particular emphasis is on study design and interpretation of results of research.  The book features âœPresenting Problemsâ drawn from studies published in the medical literature, end-of-chapter exercises, and a reorganization of content to reflect the way investigators ask research questions. To facilitate learning, each chapter contain a set of key concepts underscoring the important ideas discussed.  Features:Table of ContentsChapter 1. Introduction to Medical Research Chapter 2. Study Designs in Medical Research Chapter 3. Summarizing Data & PresentingData in Tables & Graphs Chapter 4. Probability & Related Topicsfor Making Inferences About Data Chapter 5. Research Questions About One GroupChapter 6. Research Questions About TwoSeparate or Independent Groups Chapter 7. Research Questions About Means inThree or More Groups Chapter 8. Research Questions AboutRelationships among Variables Chapter 9. Analyzing Research Questions AboutSurvival Chapter 10. Statistical Methods for MultipleVariables Chapter 11. Survey Research Chapter 12. Methods of Evidence-Based Medicineand Decision Analysis Chapter 13. Reading the Medical Literature Appendix A: Tables Appendix B: Answers to Exercises Appendix C: Flowcharts for Relating ResearchQuestions to Statistical Methods Glossary References

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Book SynopsisAvailable for the first time in McGraw-Hill''s Connect! Principles of Statistics for Engineers and Scientists emphasizes statistical methods and how they can be applied to problems in science and engineering. The book contains many examples that feature real, contemporary data sets, both to motivate students and to show connections to industry and scientific research. Because statistical analyses are done on computers, the book contains exercises and examples that involve interpreting, as well as generating, computer output. This book may be used effectively with any software package.Table of Contents1 Summarizing Univariate Data2 Summarizing Bivariate Data3 Probability4 Commonly Used Distributions5 Point and Interval Estimation for a Single Sample6 Hypothesis Tests for a Single Sample7 Inferences for Two Samples8 Inference in Linear Models9 Factorial Experiments10 Statistical Quality Control

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Book SynopsisStatistics problems can make the best students shudder as they near the classroom, but they need not worry any longer -- QuickStudy is here to help! A comprehensive, up-to-date collection of tips and tricks for understanding statistics/probability is contained in this 3-panel (6-page) guide, which is designed with easy-to-use icons to help students go right to the equations and problems they most need to learn, and also call out helpful tips to use and common pitfalls to avoid.

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Book SynopsisTable of ContentsIntroduction. Three stories that made me write this bookChapter 1. Data preparation 1.1 Analyzing and transforming the original data 1.2 Preparing the basis for a dashboard 1.3 Making data samples for visualizations 1.4. Setting up an interactivity 1.5. Summary and conclusions of the chapter, quick tricks Chapter 2. Dashboard assembling 2.1 Assembling a dashboard according to the layout 2.2 Creating KPI cards 2.3 Aligning a dashboard, adding a header 2.4 Summary and conclusions of the chapter, quick tricks Chapter 3. Anatomy of diagrams 3.1 Analyzing ready-made design styles 3.2 Setting up data labels 3.3 Working with the text: remove the excess, add the necessary 3.4 Designing bar charts 3.5 Setting up the chart template 3.6 Summary and conclusions of the chapter, quick tricks Chapter 4. Final dashboard design 4.1 Aligning the headers to the grid 4.2 Creating new cards on the top of the cells 4.3 Making interactive slicers 4.4 Working with Excel colors and fonts 4.5 Improving standard Excel themes 4.6. Summary and conclusions of the chapter, quick tricks Chapter 5. Corporate identity 5.1 Creating a theme in accordance with the brandbook 5.2 Adapting the theme according to the checklist 5.3 Creating a dashboard in a dark theme 5.4 Summary and conclusions of the chapter, quick tricks Chapter 6. Data visualization rules 6.1 Types of data analysis 6.2 How to choose charts 6.3 Life hacks for multiple data series 6.4 When you need everything at once 6.5 Funnel and waterfall 6.6 Summary and conclusions of the chapter, quick tricks Conclusion

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Book SynopsisThis book provides research summaries from a number of different focuses in Mathematics, and compiles biographical sketches of top professionals in this important field.

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Book SynopsisWe utilize statistics in our daily lives when we evaluate TV program ratings, predict voting outcomes, prepare stock, predict the amounts of sales, and evaluate the effectiveness of medical treatment. We predict the result not on the basis of personal experience, but on the basis of data. However, the accuracy of the prediction depends on the data, the theory, and the depth of understanding the model. In this book, the author analyzes fundamental models to advanced models without skipping their derivation processes. It is then possible to clearly understand the assumption and approximations used in the model, and hence understand the limitation of the model. We also cover almost all of the subjects in statistics since they are all related to each other. Although this book treats advanced models, people who are not professional in science can easily understand the content since by stepping up the derivation from the fundamental level to the advanced level. The author does hope that readers can understand the meaning of the models in statistics and techniques to reach the final results.

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