Probability and statistics Books
Princeton University Press Guesstimation 2.0
Book SynopsisReveals the simple techniques needed to estimate virtually anything and illustrates them using an eclectic array of problems. This title shows how to estimate everything from how closely you can orbit a neutron star without being pulled apart by gravity, to the fuel used to transport your food from the farm to the store.Trade Review"This follow-up to the popular Guesstimation offers more on the joy of mathematical estimation, and inspiration for the budding analyst."--Nature "The books do a wonderful job at helping the reader to master the craft."--Cut the Knot Insights "A delightful volume... I hope to be able to use many of the tricks I learned in the future. I also hope to teach some of them to students. This would make a great secondary textbook in many classes, ranging from quantitative literacy to a science methods class for future educators. A careful study of this book would certainly improve a student's ability to take a complicated question, break it down into solvable parts, and assemble the parts to find an answer. Because this is quite close to what I want my students to do when faced with a difficult problem in pure mathematics as well, I consider this to be a very valuable book indeed."--Dominic Klyve, MAA Reviews "Guesstimation 2.0: Solving Today's Problems on the Back of a Napkin succeeds where most popular science literature so often fails. This is because it provides its readers with a scientific tool they can use immediately in their everyday lives... [Makes] an excellent addition for the casual scientist, job interviewee, or anyone hoping to impress their friends at a party."--Gabriel Thoumi, Mongabay.com "Readers who enjoyed Weinstein's first volume will be pleased with this instalment."--Choice "Guesstimation 2.0 is a book that was made to mediate between fun and useful... Whether or not a fan of numbers, it's always cool to appear smart, therefore Guesstimation 2.0 is an excellent element to add to one's arsenal."--Sarthak Shankar, Organiser "Certainly a good read for any teacher who enjoys numbers and the world around us."--Mark Hughes, Mathematics Teaching in the Middle School "Guesstimation's problems are fun and engaging in character, and the solutions are intuitive and well explained. Each problem and solution stands independently, and is about four pages long, making the book ideal for passing a quick ten minutes, and easy to pick up and put down. If, like me, you like ill-posed questions to have concrete answers then Guesstimation is definitely a good place to hone your estimation skills!"--Fionntan Roukema, Mathematical SpectrumTable of ContentsAcknowledgments xi Preface xiii 1 How to Solve Problems 1 2 General Questions 11 *2.1 Who unrolled the toilet paper? 13 *2.2 Money height 17 *2.3 Blotting out the Sun 19 *2.4 Really extra-large popcorn 21 *2.5 Building volume 25 *2.6 Mass of money 29 *2.7 A baseball in a glass of beer 33 *2.8 Life on the phone 37 *2.9 Money under the bridge 41 *2.10 Monkeys and Shakespeare 45 *2.11 The titans of siren 49 *2.12 Airheads at the movies 53 *2.13 Heavy cars and heavier people 55 *2.14 Peeing in the pool 59 3 Recycling: What Really Matters? 63 *3.1 Water bottles 67 *3.2 99 bottles of beer on the wall ... 71 *3.3 Can the aluminum 75 *3.4 Paper or plastic? 79 *3.5 Paper doesn't grow on trees! 83 *3.6 The rain in Spain ... 87 *3.7 Bottom feeders 91 *3.8 You light up my life! 95 4 The Five Senses 101 *4.1 Don't stare at the Sun 103 *4.2 Men of vision 105 *4.3 Light a single candle 109 *4.4 Oh say can you see? 113 *4.5 Bigger eyes 117 *4.6 They're watching us! 121 *4.7 Beam the energy down, Scotty! 125 *4.8 Oh say can you hear? 131 *4.9 Heavy loads 135 5 Energy and Work 139 *5.1 Power up the stairs 143 *5.2 Power workout 145 *5.3 Water over the dam 149 *5.4 A hard nut to crack 153 *5.5 Mousetrap cars 155 *5.6 Push hard 159 *5.7 Pumping car tires 161 *5.8 Pumping bike tires 165 *5.9 Atomic bombs and confetti 169 6 Energy and Transportation 173 *6.1 Gas-powered humans 177 *6.2 Driving across country 181 *6.3 Keep on trucking 185 *6.4 Keep on biking 189 *6.5 Keep on training 193 *6.6 Keep on flying 197 *6.7 To pee or not to pee 201 *6.8 Solar-powered cars 205 *6.9 Put a doughnut in your tank 209 *6.10 Perk up your car 213 *6.11 Don't slow down 217 *6.12 Throwing tomatoes 219 7 Heavenly Bodies 223 *7.1 Orbiting the Sun 227 *7.2 Flying off the Earth 229 *7.3 The rings of Earth 233 *7.4 It is not in the stars to hold our destiny 237 *7.5 Orbiting a neutron star 241 *7.6 How high can we jump? 245 *7.7 Collapsing Sun 249 *7.8 Splitting the Moon 253 *7.9 Splitting a smaller moon 257 *7.10 Spinning faster and slower 263 *7.11 Shrinking Sun 267 *7.12 Spinning Earth 271 *7.13 The dinosaur killer and the day 273 *7.14 The Yellowstone volcano and the day 277 *7.15 The orbiting Moon 281 *7.16 The shortest day 283 8 Materials 289 *8.1 Stronger than spider silk 291 *8.2 Beanstalk to orbit 295 *8.3 Bolt failure 299 *8.4 Making mountains out of molecules 303 *8.5 Chopping down a tree 307 9 Radiation 311 *9.1 Nuclear neutrinos 315 *9.2 Neutrinos and you 319 *9.3 Solar neutrinos 323 *9.4 Supernovas can be dangerous 327 *9.5 Reviving ancient bacteria 331 *9.6 Decaying protons 335 *9.7 Journey to the center of the galaxy 337 Appendix A * Dealing with Large Numbers 341 * A.1 Large Numbers 341 * A.2 Precision, Lots of Digits, and Lying 343 * A.3 Numbers and Units 345 Appendix B * Pegs to Hang Things On 347 Bibliography 351 Index 355
£15.29
Harvard University Press The Politics of Large Numbers
Book SynopsisIn this sophisticated study of the history of statistics, Desrosières shows how the evolution of modern statistics has been inextricably bound up with the knowledge and power of governments. He traces the complex reciprocity between modern governments and the mathematical artifacts that dictate the duties of the state and measure its successes.Trade ReviewStatistics works in and on the world, simultaneously describing and remaking. It straddles the chasm between the invented and the discovered, the real and the constructed--oppositions that have structured an increasingly sterile debate about the nature of science among historians, philosophers, sociologists, and scientists. The great merit of Desrosières' study is that it points the way beyond this impasse by showing how statistical entities are simultaneously real and constructed, invented and discovered. -- Lorraine Daston * London Review of Books *This is a good book...The strength of Alain Desrosières's account lies in the rich and insightful way he has analysed his subject--statistical reasoning... Anyone interested in the history of science and economics and, particularly, applied mathematics, will be stimulated by this book. -- Hugh Pennington * Times Higher Education Supplement *Statistics, with its aura of dispassionate dustiness, does not have a good image. It is detested by generations of social-science students, a grim necessity for medical researchers, distrusted by the general public. Many of these--and some statisticians--would be surprised to discover how often statistics has responded to social developments or even influenced them. The broad theme of [The Politics of Large Numbers] is that statistical measures and probabilistic concepts are most usefully seen as matters of convention, rather than of objective reality. The social context generates the need to make things countable and to interpret the counts; it also conditions the conventions that emerge. -- Jonathan Rosenhead * Nature *This is a work of tremendous erudition that is far broader in scope and significance than its title suggests. Coming at the end of an explosive 15-year period of research, here and in Europe, on the history of statistical thinking, Desrosières's book is at once a powerful synthesis of recent scholarship and a path-setting effort to extend this research into important areas that have gone relatively unattended... His case for the applicability of the actor-network approach to the historical development of statistical thought is a compelling one, which is very effective at sociologically integrating many of the different currents that formed this broad development. -- Charles Camic * American Journal of Sociology *Desrosières' discussion of the various translations statistics has been able to achieve is both scholarly and erudite. It is also now one of a number of recent histories of statistics published over the last fifteen years that offers a critical approach to statistics. Rather than accepting that statistics is necessarily correct because it is based on the seemingly universal logic of mathematics, The Politics of Large Numbers, and other works in the same genre, are keen to show that statistics is a contingent and local enterprise, one shot through with the peculiarities of the particular social, cultural, and political context in which it is practised... Desrosières' book is a fine piece of work. -- Trevor J. Barnes * Environment and Planning *Alain Desrosières's ambitious and critical study seeks to reconstruct the modern historical contexts in which the use of statistics and statistical methods evolved rapidly... There is no other book quite like The Politics of Large Numbers. Its uniqueness lies in its impressive historical and intellectual sweep. In addition to tracing the changing connections between state construction, scientific development, and statistical reasoning in modern times, it highlights their recent intersections in ways that may be of particular interest to readers. -- Joseph P. Smaldone * Perspectives on Political Science *[The Politics of large Numbers] shows, with many historical details, that biometrics did not become a subject for mathematical statistics alone, but for administrative statistics as well. -- Jochen Fleischhacker * Population Studies *This is an ambitious, complex and sophisticated 'sociology of numbers,' a study of the history of statistics and an analysis of its function within the state. It covers the relevant technical mathematical subjects as well as the epistemological questions raised by the reification of numbers with impressive erudition and subtlety... Desrosières' work is an impressive synthesis of technical, historical, and philosophical thinking on statistics and the state in the modern Western world, available no where else. The book seems destined to be a standard reference in the areas of statistics, government, history and economics as well as other disciplines like psychology where 'reasoning through numbers' plays an essential role. The style is sophisticated and while demanding, is generally engaging. -- Carol Blum, State University of New York at Stony BrookThe book is a critical, scholarly and accurate synthesis of an extremely broad spectrum of the history of statistics, with an emphasis on the conceptual development of social statistics, culminating in twentieth-century applied econometrics. Desrosières' treatment is not highly technical, although he does exhibit an easy competence with the technical side. A significant strength of the work are the discussions of the relationships of the development of statistics to national and international statistical agencies, and the relationship of economic ideas to the statistical constructs employed to measure them. No other work exhibits the same breadth--probability, mathematical statistics, psychology, economics, sociology, surveys, public health, medical statistics. -- Stephen M. Stigler, University of ChicagoTable of ContentsIntroduction: Arguing from Social Facts Prefects and Geometers Judges and Astronomers Averages and the Realism of Aggregates Correlation and the Realism of Causes Statistics and the State: France and Great Britain Statistics and the State: Germany and the United States The Part for the Whole: Monographs or Representative Sampling Classifying and Encoding Modeling and Adjusting Conclusion: Disputing the Indisputable
£34.81
Harvard University Press The History of Statistics
Book SynopsisStigler shows how statistics arose from the interplay of mathematical concepts and the needs of several applied sciences. His emphasis is upon how methods of probability theory were developed for measuring uncertainty, for reducing uncertainty, and as a conceptual framework for quantitative studies in the social sciences.Trade ReviewOne is tempted to say that the history of statistics in the nineteenth century will be associated with the name Stigler. -- Morris Kline * New York Times Book Review *An exceptionally searching, almost loving, study of the relevant inspirations and aberrations of its principal characters James Bernoulli, de Moivre, Bayes, Laplace, Gauss, Quetelet, Lexis, Galton, Edgeworth, and Pearson, not neglecting a grand supporting cast… The definitive record of an intellectual Golden Age, an overoptimistic climb to a height not to be maintained. -- M. Stone * Science *In this tour de force of careful scholarship, Stephen Stigler has laid bare the people, ideas, and events underlying the development of statistics… He has written an important and wonderful book… Sometimes Stigler’s prose is so evocative it is almost poetic. -- Howard Wainer * Contemporary Psychology *The book is a pleasure to read: the prose sparkles; the protagonists are vividly drawn; the illustrations are handsome and illuminating; the insights plentiful and sharp. This will remain the definitive work on the early development of mathematical statistics for some time to come. -- Lorraine J. Daston * Journal of Modern History *Stigler’s book exhibits a rare combination of mastery of technical materials, sensitivity to conceptual milieu, and near exhaustive familiarity with primary sources. An exemplary study. -- Lorraine DastonTable of ContentsIntroduction PART 1: The Development of Mathematical Statistics in Astronomy and Geodesy before 1827 1. Least Squares and the Combination of Observations Legendre in 1805 Cotes's Rule Tobias Mayer and the Libration of the Moon Saturn, Jupiter, and Enter Laplace's Rescue of the Solar System Roger Boscovich and the Figure of the Earth Laplace and the Method of Situation Legendre and the Invention of Least Squares 2. Probabilists and the Measurement of Uncertainty Jacob Bernoulli De Moivre and the Expanded Binomial Bernoulli's Failure De Moivre's Approximation De Moivre's Deficiency Simpson and Bayes Simpson's Crucial Step toward Error A Bayesian Critique 3. Inverse Probability Laplace and Inverse Probability The Choice of Means The Deduction of a Curve of Errors in 1772-1774
£32.36
Taylor & Francis Inc Generalized Estimating Equations
Book SynopsisGeneralized Estimating Equations, Second Edition updates the best-selling previous edition, which has been the standard text on the subject since it was published a decade ago. Combining theory and application, the text provides readers with a comprehensive discussion of GEE and related models. Numerous examples are employed throughout the text, along with the software code used to create, run, and evaluate the models being examined. Stata is used as the primary software for running and displaying modeling output; associated R code is also given to allow R users to replicate Stata examples. Specific examples of SAS usage are provided in the final chapter as well as on the book's website.This second edition incorporates comments and suggestions from a variety of sources, including the Statistics.com course on longitudinal and panel models taught by the authors. Other enhancements include an examination of GEE marginal effects; a more thorough presentatiTrade Review"Overall, I found this to be a very useful book on GEE, and would recommend it to anyone planning to use GEE models in their data analysis. Both the theory and practical aspects of constructing and analysing such models is covered. Inclusion of code for many of the analyses is an excellent feature."—Ken J. Beath, Macquarie University, Australia, Australian and New Zealand Journal of Statistics, April 2017" … the authors expand the text with several additions: (I) they examine and include entirely new topics related to GEE and the estimation of clustered and longitudinal models; (2) they add more detailed discussions of previously presented topics, including expanding the discussion of various models associated with GEE (penalized GEE, survey GEE, and quasi-least-square regression), adding material on hypothesis testing and diagnostics, and introducing alternative models for ordered categorical outcomes and an extension of the QIC, which is a model selection criterion measure; (3) they expand the amount of computer code by adding R code to duplicate the Stata examples wherever possible. In my opinion, the second edition is enhanced by the additions mentioned above, providing an excellent review of the GEE, wide coverage of its variations, and many useful computing techniques. I believe it would be a very useful reference book for practicing researchers and graduate students who are interested in research topics related to GEE."—CindyYu, Iowa State University in the Journal of the American Statistical Association, December 2013"The second edition … adds a few new topics related to various extensions of GEE … [and replaces] outdated S-PLUS codes with R scripts. Also, the number of exercises increased significantly … . For those who want to use this book in the classroom, including me, having extra exercise sets is certainly a welcome addition. … One main strength of this book is its comprehensive coverage of Stata implementation of the GEE. … a valuable reference and is particularly useful for practitioners. It can serve as supplemental reading in longitudinal data analysis classes as well."—Woncheol Jang, Biometrics, September 2013Praise for the First Edition:"… well-written chapters … . The book contains challenging problems in exercises and is suitable to be a textbook in a graduate-level course on estimating functions. The references are up-to-date and exhaustive. … I enjoyed reading [this book] and recommend [it] very highly to the statistical community."—Journal of Statistical Computation and Simulation, February 2005"[The book] is comprehensive and covers much useful material with formulas presented in detail … a useful and recommendable book both for those who already work with GEE methods and for newcomers to the field."—Per Kragh Andersen, University of Copenhagen, Statistics in Medicine, 2004"Generalized Estimating Equations is the first and only book to date dedicated exclusively to generalized estimating equations (GEE). I find it to be a good reference text for anyone using generalized linear models (GLIM).The authors do a good job of not only presenting the general theory of GEE models, but also giving explicit examples of various correlation structures, link functions and a comparison between population-averaged and subject-specific models. Furthermore, there are sections on the analysis of residuals, deletion diagnostics, goodness-of-fit criteria, and hypothesis testing. Good data-driven examples that give comparisons between different GEE models are provided throughout the book. Perhaps the greatest strength of this book is its completeness. It is a thorough compendium of information from the GEE literature. Overall, Generalized Estimating Equations contains a unique survey of GEE models in an attempt to unify notation and provide the most in-depth treatment of GEEs. I believe that it serves as a valuable reference for researchers, teachers, and students who study and practice GLIM methodology."—Journal of the American Statistics Association, March 2004"Generalized Estimating Equations is a good introductory book for analysing continuous and discrete data using GEE methods ... . This book is easy to read, and it assumes that the reader has some background in GLM. Many examples are drawn from biomedical studies and survey studies, and so it provides good guidance for analysing correlated data in these and other areas."—Technometrics, 2003"Overall, I found this to be a very useful book on GEE, and would recommend it to anyone planning to use GEE models in their data analysis. Both the theory and practical aspects of constructing and analysing such models is covered. Inclusion of code for many of the analyses is an excellent feature."—Ken J. Beath, Macquarie University, Australia, Australian and New Zealand Journal of Statistics, April 2017"The second edition … adds a few new topics related to various extensions of GEE … [and replaces] outdated S-PLUS codes with R scripts. Also, the number of exercises increased significantly … . For those who want to use this book in the classroom, including me, having extra exercise sets is certainly a welcome addition. … One main strength of this book is its comprehensive coverage of Stata implementation of the GEE. … a valuable reference and is particularly useful for practitioners. It can serve as supplemental reading in longitudinal data analysis classes as well."—Woncheol Jang, Biometrics, September 2013Praise for the First Edition:"… well-written chapters … . The book contains challenging problems in exercises and is suitable to be a textbook in a graduate-level course on estimating functions. The references are up-to-date and exhaustive. … I enjoyed reading [this book] and recommend [it] very highly to the statistical community."—Journal of Statistical Computation and Simulation, February 2005"[The book] is comprehensive and covers much useful material with formulas presented in detail … a useful and recommendable book both for those who already work with GEE methods and for newcomers to the field."—Per Kragh Andersen, University of Copenhagen, Statistics in Medicine, 2004"Generalized Estimating Equations is the first and only book to date dedicated exclusively to generalized estimating equations (GEE). I find it to be a good reference text for anyone using generalized linear models (GLIM).The authors do a good job of not only presenting the general theory of GEE models, but also giving explicit examples of various correlation structures, link functions and a comparison between population-averaged and subject-specific models. Furthermore, there are sections on the analysis of residuals, deletion diagnostics, goodness-of-fit criteria, and hypothesis testing. Good data-driven examples that give comparisons between different GEE models are provided throughout the book. Perhaps the greatest strength of this book is its completeness. It is a thorough compendium of information from the GEE literature. Overall, Generalized Estimating Equations contains a unique survey of GEE models in an attempt to unify notation and provide the most in-depth treatment of GEEs. I believe that it serves as a valuable reference for researchers, teachers, and students who study and practice GLIM methodology."—Journal of the American Statistics Association, March 2004"Generalized Estimating Equations is a good introductory book for analysing continuous and discrete data using GEE methods ... . This book is easy to read, and it assumes that the reader has some background in GLM. Many examples are drawn from biomedical studies and survey studies, and so it provides good guidance for analysing correlated data in these and other areas."—Technometrics, 2003Table of ContentsIntroduction. Model Construction and Estimating Equations. Generalized Estimating Equations. Residuals, Diagnostics, and Testing. Programs and Datasets. References. Author Index. Subject Index.
£92.14
Springer-Verlag New York Inc. All of Statistics
Book SynopsisTaken literally, the title All of Statistics is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like nonparametric curve estimation, bootstrapping, and clas sification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analyzing data. For some time, statistics research was con ducted in statistics departmeTrade ReviewWinner of the 2005 DeGroot Prize.From the reviews:"Presuming no previous background in statistics and described by the author as "demanding" yet "understandable because the material is as intuitive as possible" (p. viii), this certainly would be my choice of textbook if I was required to learn mathematical statistics again for a couple of semesters." Technometrics, August 2004"This book should be seriously considered as a text for a theoretical statsitics course for non-majors, and perhaps even for majors...The coverage of emerging and important topics is timely and welcomed...you should have this book on your desk as a reference to nothing less than 'All of Statistics.'" Biometrics, December 2004"Although All of Statistics is an ambitious title, this book is a concise guide, as the subtitle suggests....I recommend it to anyone who has an interest in learning something new about statistical inference. There is something here for everyone." The American Statistician, May 2005"As the title of the book suggests, ‘All of Statistics’ covers a wide range of statistical topics. … The number of topics covered in this book is vast … . The greatest strength of this book is as a first point of reference for a wide range of statistical methods. … I would recommend this book as a useful and interesting introduction to a large number of statistical topics for non-statisticians and also as a useful reference book for practicing statisticians." (Matthew J. Langdon, Journal of Applied Statistics, Vol. 32 (1), January, 2005)"This book was written specifically to give students a quick but sound understanding of modern statistics, and its coverage is very wide. … The book is extremely well done … ." (N. R. Draper, Short Book Reviews, Vol. 24 (2), 2004)"This is most definitely a book about mathematical statistics. It is full of theorems and proofs … . Presuming no previous background in statistics … this certainly would be my choice of textbook if I was required to learn mathematical statistics again for a couple of semesters." (Eric R. Ziegel, Technometrics, Vol. 46 (3), August, 2004)"The author points out that this book is for those who wish to learn probability and statistics quickly … . this book will serve as a guideline for instructors as to what should constitute a basic education in modern statistics. It introduces many modern topics … . Adequate references are provided at the end of each chapter which the instructor will be able to use profitably … ." (Arup Bose, Sankhya, Vol. 66 (3), 2004)"The amount of material that is covered in this book is impressive. … the explanations are generally clear and the wide range of techniques that are discussed makes it possible to include a diverse set of examples … . The worked examples are complemented with numerous theoretical and practical exercises … . is a very useful overview of many areas of modern statistics and as such will be very useful to readers who require such a survey. Library copies would also see plenty of use." (Stuart Barber, Journal of the Royal Statistical Society, Series A – Statistics in Society, Vol. 168 (1), 2005)Table of ContentsProbability.- Random Variables.- Expectation.- Inequalities.- Convergence of Random Variables.- Models, Statistical Inference and Learning.- Estimating the CDF and Statistical Functionals.- The Bootstrap.- Parametric Inference.- Hypothesis Testing and p-values.- Bayesian Inference.- Statistical Decision Theory.- Linear and Logistic Regression.- Multivariate Models.- Inference about Independence.- Causal Inference.- Directed Graphs and Conditional Independence.- Undirected Graphs.- Loglinear Models.- Nonparametric Curve Estimation.- Smoothing Using Orthogonal Functions.- Classification.- Probability Redux: Stochastic Processes.- Simulation Methods.
£44.99
Pearson Education Probability Statistics for Engineers Scientists
Book Synopsis
£80.24
John Wiley & Sons Inc Statistics for Big Data For Dummies
Book SynopsisDoes the subject of data analysis make you dizzy? This book features introduction to exploratory data analysis, the lowdown on collecting, cleaning, and organizing data, everything you need to know about interpreting data using common software and programming languages. It helps you to identify valid, useful, and understandable patterns in data.Table of ContentsIntroduction 1 Part I: Introducing Big Data Statistics 7 Chapter 1: What Is Big Data and What Do You Do With It? 9 Chapter 2: Characteristics of Big Data: The Three Vs 19 Chapter 3: Using Big Data: The Hot Applications 27 Chapter 4: Understanding Probabilities 41 Chapter 5: Basic Statistical Ideas 57 Part II: Preparing and Cleaning Data 81 Chapter 6: Dirty Work: Preparing Your Data for Analysis 83 Chapter 7: Figuring the Format: Important Computer File Formats 99 Chapter 8: Checking Assumptions: Testing for Normality 107 Chapter 9: Dealing with Missing or Incomplete Data 119 Chapter 10: Sending Out a Posse: Searching for Outliers 129 Part III: Exploratory Data Analysis (EDA) 141 Chapter 11: An Overview of Exploratory Data Analysis (EDA) 143 Chapter 12: A Plot to Get Graphical: Graphical Techniques 155 Chapter 13: You’re the Only Variable for Me: Univariate Statistical Techniques 173 Chapter 14: To All the Variables We’ve Encountered: Multivariate Statistical Techniques 191 Chapter 15: Regression Analysis 215 Chapter 16: When You’ve Got the Time: Time Series Analysis 243 Part IV: Big Data Applications 269 Chapter 17: Using Your Crystal Ball: Forecasting with Big Data 271 Chapter 18: Crunching Numbers: Performing Statistical Analysis on Your Computer 297 Chapter 19: Seeking Free Sources of Financial Data 319 Part V: The Part of Tens 331 Chapter 20: Ten (or So) Best Practices in Data Preparation 333 Chapter 21: Ten (or So) Questions Answered by Exploratory Data Analysis (EDA) 339 Index 349
£14.44
Taylor & Francis Inc Applied Surrogate Endpoint Evaluation Methods
Book SynopsisAn important factor that affects the duration, complexity and cost of a clinical trial is the endpoint used to study the treatmentâs efficacy. When a true endpoint is difficult to use because of such factors as long follow-up times or prohibitive cost, it is sometimes possible to use a surrogate endpoint that can be measured in a more convenient or cost-effective way. This book focuses on the use of surrogate endpoint evaluation methods in practice, using SAS and R.Trade Review"This is a timely text. The number of published studies using surrogate endpoints has increased dramatically since the early work of the 1980s; however, there is a dearth of available texts or software on this topic. Anyone with an interest in surrogate endpoint evaluation would benefit from this text."~Statistics in Medicine Table of ContentsIntroductory Material. Introduction. Notation and Example Datasets. The History of Surrogate Endpoint Validation. Contemporary Surrogate Endpoint Evaluation Methods. Multiple-Trial Surrogate Endpoint Evaluation Methods. Two Continuous Outcomes. Two Survival Endpoints. Two Categorical Endpoints. A Categorical and a Continuous Endpoint. A Survival and a Continuous Endpoint. A Survival and a Categorical Endpoint. Two Longitudinal Endpoints. A Longitudinal and a Survival Endpoint. Additional Considerations and Further Topics. Software Details. An Alternative Surrogate Endpoint Evaluation Framework: Causal-Inference. Surrogate Endpoint Evaluation Methods in Small Samples. Construction and Evaluation of Genetic Biomarkers in Early Drug Development Experiments. Additional Considerations.
£68.39
Random House USA Inc The Drunkards Walk
Book SynopsisNATIONAL BESTSELLER • From the classroom to the courtroom and from financial markets to supermarkets, an intriguing and illuminating look at how randomness, chance, and probability affect our daily lives that will intrigue, awe, and inspire.“Mlodinow writes in a breezy style, interspersing probabilistic mind-benders with portraits of theorists.... The result is a readable crash course in randomness.” —The New York Times Book ReviewWith the born storyteller's command of narrative and imaginative approach, Leonard Mlodinow vividly demonstrates how our lives are profoundly informed by chance and randomness and how everything from wine ratings and corporate success to school grades and political polls are less reliable than we believe.By showing us the true nature of chance and revealing the psychological illusions that cause us to misjudge the world around us, Mlodinow gives us the tools we need to make more
£13.88
John Wiley & Sons Inc Categorical Data Analysis
Book SynopsisPraise for the Second Edition "A must-have book for anyone expecting to do research and/or applications in categorical data analysis. " Statistics in Medicine "It is a total delight reading this book.Table of ContentsPreface xiii 1 Introduction: Distributions and Inference for Categorical Data 1 1.1 Categorical Response Data, 1 1.2 Distributions for Categorical Data, 5 1.3 Statistical Inference for Categorical Data, 8 1.4 Statistical Inference for Binomial Parameters, 13 1.5 Statistical Inference for Multinomial Parameters, 17 1.6 Bayesian Inference for Binomial and Multinomial Parameters, 22 Notes, 27 Exercises, 28 2 Describing Contingency Tables 37 2.1 Probability Structure for Contingency Tables, 37 2.2 Comparing Two Proportions, 43 2.3 Conditional Association in Stratified 2 × 2 Tables, 47 2.4 Measuring Association in I × J Tables, 54 Notes, 60 Exercises, 60 3 Inference for Two-Way Contingency Tables 69 3.1 Confidence Intervals for Association Parameters, 69 3.2 Testing Independence in Two-way Contingency Tables, 75 3.3 Following-up Chi-Squared Tests, 80 3.4 Two-Way Tables with Ordered Classifications, 86 3.5 Small-Sample Inference for Contingency Tables, 90 3.6 Bayesian Inference for Two-way Contingency Tables, 96 3.7 Extensions for Multiway Tables and Nontabulated Responses, 100 Notes, 101 Exercises, 103 4 Introduction to Generalized Linear Models 113 4.1 The Generalized Linear Model, 113 4.2 Generalized Linear Models for Binary Data, 117 4.3 Generalized Linear Models for Counts and Rates, 122 4.4 Moments and Likelihood for Generalized Linear Models, 130 4.5 Inference and Model Checking for Generalized Linear Models, 136 4.6 Fitting Generalized Linear Models, 143 4.7 Quasi-Likelihood and Generalized Linear Models, 149 Notes, 152 Exercises, 153 5 Logistic Regression 163 5.1 Interpreting Parameters in Logistic Regression, 163 5.2 Inference for Logistic Regression, 169 5.3 Logistic Models with Categorical Predictors, 175 5.4 Multiple Logistic Regression, 182 5.5 Fitting Logistic Regression Models, 192 Notes, 195 Exercises, 196 6 Building, Checking, and Applying Logistic Regression Models 207 6.1 Strategies in Model Selection, 207 6.2 Logistic Regression Diagnostics, 215 6.3 Summarizing the Predictive Power of a Model, 221 6.4 Mantel–Haenszel and Related Methods for Multiple 2 × 2 Tables, 225 6.5 Detecting and Dealing with Infinite Estimates, 233 6.6 Sample Size and Power Considerations, 237 Notes, 241 Exercises, 243 7 Alternative Modeling of Binary Response Data 251 7.1 Probit and Complementary Log–log Models, 251 7.2 Bayesian Inference for Binary Regression, 257 7.3 Conditional Logistic Regression, 265 7.4 Smoothing: Kernels, Penalized Likelihood, Generalized Additive Models, 270 7.5 Issues in Analyzing High-Dimensional Categorical Data, 278 Notes, 285 Exercises, 287 8 Models for Multinomial Responses 293 8.1 Nominal Responses: Baseline-Category Logit Models, 293 8.2 Ordinal Responses: Cumulative Logit Models, 301 8.3 Ordinal Responses: Alternative Models, 308 8.4 Testing Conditional Independence in I × J × K Tables, 314 8.5 Discrete-Choice Models, 320 8.6 Bayesian Modeling of Multinomial Responses, 323 Notes, 326 Exercises, 329 9 Loglinear Models for Contingency Tables 339 9.1 Loglinear Models for Two-way Tables, 339 9.2 Loglinear Models for Independence and Interaction in Three-way Tables, 342 9.3 Inference for Loglinear Models, 348 9.4 Loglinear Models for Higher Dimensions, 350 9.5 Loglinear—Logistic Model Connection, 353 9.6 Loglinear Model Fitting: Likelihood Equations and Asymptotic Distributions, 356 9.7 Loglinear Model Fitting: Iterative Methods and Their Application, 364 Notes, 368 Exercises, 369 10 Building and Extending Loglinear Models 377 10.1 Conditional Independence Graphs and Collapsibility, 377 10.2 Model Selection and Comparison, 380 10.3 Residuals for Detecting Cell-Specific Lack of Fit, 385 10.4 Modeling Ordinal Associations, 386 10.5 Generalized Loglinear and Association Models, Correlation Models, and Correspondence Analysis, 393 10.6 Empty Cells and Sparseness in Modeling Contingency Tables, 398 10.7 Bayesian Loglinear Modeling, 401 Notes, 404 Exercises, 407 11 Models for Matched Pairs 413 11.1 Comparing Dependent Proportions, 414 11.2 Conditional Logistic Regression for Binary Matched Pairs, 418 11.3 Marginal Models for Square Contingency Tables, 424 11.4 Symmetry, Quasi-Symmetry, and Quasi-Independence, 426 11.5 Measuring Agreement Between Observers, 432 11.6 Bradley–Terry Model for Paired Preferences, 436 11.7 Marginal Models and Quasi-Symmetry Models for Matched Sets, 439 Notes, 443 Exercises, 445 12 Clustered Categorical Data: Marginal and Transitional Models 455 12.1 Marginal Modeling: Maximum Likelihood Approach, 456 12.2 Marginal Modeling: Generalized Estimating Equations (GEEs) Approach, 462 12.3 Quasi-Likelihood and Its GEE Multivariate Extension: Details, 465 12.4 Transitional Models: Markov Chain and Time Series Models, 473 Notes, 478 Exercises, 479 13 Clustered Categorical Data: Random Effects Models 489 13.1 Random Effects Modeling of Clustered Categorical Data, 489 13.2 Binary Responses: Logistic-Normal Model, 494 13.3 Examples of Random Effects Models for Binary Data, 498 13.4 Random Effects Models for Multinomial Data, 511 13.5 Multilevel Modeling, 515 13.6 GLMM Fitting, Inference, and Prediction, 519 13.7 Bayesian Multivariate Categorical Modeling, 523 Notes, 525 Exercises, 527 14 Other Mixture Models for Discrete Data 535 14.1 Latent Class Models, 535 14.2 Nonparametric Random Effects Models, 542 14.3 Beta-Binomial Models, 548 14.4 Negative Binomial Regression, 552 14.5 Poisson Regression with Random Effects, 555 Notes, 557 Exercises, 558 15 Non-Model-Based Classification and Clustering 565 15.1 Classification: Linear Discriminant Analysis, 565 15.2 Classification: Tree-Structured Prediction, 570 15.3 Cluster Analysis for Categorical Data, 576 Notes, 581 Exercises, 582 16 Large- and Small-Sample Theory for Multinomial Models 587 16.1 Delta Method, 587 16.2 Asymptotic Distributions of Estimators of Model Parameters and Cell Probabilities, 592 16.3 Asymptotic Distributions of Residuals and Goodness-of-fit Statistics, 594 16.4 Asymptotic Distributions for Logit/Loglinear Models, 599 16.5 Small-Sample Significance Tests for Contingency Tables, 601 16.6 Small-Sample Confidence Intervals for Categorical Data, 603 16.7 Alternative Estimation Theory for Parametric Models, 610 Notes, 615 Exercises, 616 17 Historical Tour of Categorical Data Analysis 623 17.1 Pearson–Yule Association Controversy, 623 17.2 R. A. Fisher’s Contributions, 625 17.3 Logistic Regression, 627 17.4 Multiway Contingency Tables and Loglinear Models, 629 17.5 Bayesian Methods for Categorical Data, 633 17.6 A Look Forward, and Backward, 634 Appendix A Statistical Software for Categorical Data Analysis 637 Appendix B Chi-Squared Distribution Values 641 References 643 Author Index 689 Example Index 701 Subject Index 705 Appendix C Software Details for Text Examples (text website)
£114.26
John Wiley & Sons Inc Against the Gods
Book SynopsisA Business Week, New York Times Business, and USA Today Bestseller "Ambitious and readable... an engaging introduction to the oddsmakers, whom Bernstein regards as true humanists helping to release mankind from the choke holds of superstition and fatalism. " -The New York Times "An extraordinarily entertaining and informative book.Trade ReviewAGAINST THE GODS appeared in the "Washington Is Also Reading..." section of The Washington Post Book World. The book is described as, "A comprehensive history of man's efforts to understand risk and probability, from ancient gamblers in Greece to modern chaos theory."-The Washington Post Book World, September 20, 1998Table of ContentsAcknowledgments ix Introduction 1 To 1200: Beginnings 1. The Winds of the Greeks and the Role of the Dice 11 2. As Easy as I, II, III 23 1200–1700: A Thousand Outstanding Facts 3. The Renaissance Gambler 39 4. The French Connection 57 5. The Remarkable Notions of the Remarkable Notions Man 73 1700–1900: Measurement Unlimited 6. Considering the Nature of Man 99 7. The Search for Moral Certainty 116 8. The Supreme Law of Unreason 135 9. The Man with the Sprained Brain 152 10. Peapods and Perils 172 11. The Fabric of Felicity 187 1900–1960: Clouds of Vagueness and the Demand for Precision 12. The Measure of Our Ignorance 197 13. The Radically Distinct Notion 215 14. The Man Who Counted Everything Except Calories 231 15. The Strange Case of the Anonymous Stockbroker 247 Degrees of Belief: Exploring Uncertainty 16. The Failure of Invariance 269 17. The Theory Police 284 18. The Fantastic System of Side Bets 304 19. Awaiting the Wildness 329 Notes 339 Bibliography 353 Name Index 365 Subject Index 369
£43.50
John Wiley & Sons Inc Statistical Design and Analysis of Experiments
Book SynopsisEmphasizes the strategy of experimentation, data analysis, and the interpretation of experimental results. * Features numerous examples using actual engineering and scientific studies. * Presents statistics as an integral component of experimentation from the planning stage to the presentation of the conclusions.Trade Review"With an excellent presentation, this is suitable as a textbook in a graduate level course in design of experiments." (Journal of Statistical Computation and Simulation, April 2005) "...can really provide useful information for the intended audience..." (Zentralblatt Math, Vol. 1029, 2004) “...a practitioner’s guide to statistical methods for designing and analyzing experiments...” (Quarterly of Applied Mathematics, Vol. LXI, No. 3, September 2003) "...a perfect desktop reference..." (Technometrics, Vol. 45, No. 3, August 2003)Table of ContentsPreface. PART I: FUNDAMENTAL STATISTICAL CONCEPTS. Statistics in Engineering and Science. Fundamentals of Statistical Inference. Inferences on Means and Standard Deviations. PART II: DESIGN AND ANALYSIS WITH FACTORIAL STRUCTURE. Statistical Principles in Experimental Design. Factorial Experiments in Completely Randomized Designs. Analysis of Completely Randomized Designs. Fractional Factorial Experiments. Analysis of Fractional Factorial Experiments. PART III: DESIGN AND ANALYSIS WITH RANDOM EFFECTS. Experiments in Randomized Block Designs. Analysis of Designs with Random Factor Levels. Nested Designs. Special Designs for Process Improvement. Analysis of Nested Designs and Designs for Process Improvement. PART IV: DESIGN AND ANALYSIS WITH QUANTITATIVE PREDICTORS AND FACTORS. Linear Regression with One Predicator Variables. Linear Regression with Several Predicator Variables. Linear Regression with Factors and Covariates as Predictors. Designs and Analyses for Fitting Re sponse Surfaces. Model Assessment. Variable Selection Techniques. Appendix: Statistical Tables. Index.
£157.45
Cambridge University Press New Cambridge Statistical Tables
Book SynopsisThe second edition of this very successful and authoritative set of tables still benefits from clear typesetting, which makes the figures easy to read and use. It has, however, been improved by the addition of new tables that provide Bayesian confidence limits for the binomial and Poisson distributions, and for the square of the multiple correlation coefficient, which have not been previously available. The intervals are the shortest possible, consistent with the requirement on probability. Great care has been taken to ensure that it is clear just what is being tabulated and how the values may be used; the tables are generally capable of easy interpolation. The book contains all the tables likely to be required for elementary statistical methods in the social, business and natural sciences. It will be an essential aid for teachers, researchers and students in those subjects where statistical analysis is not wholly carried out by computers.Trade Review'This is an excellent book offered at an unusually low price of £3.50. Any forensic scientist who analyses data will be well advised to ensure that a copy is always close to hand.' Journal of the Forensic Science Society' … very extensive...clear well explained tables.' P. J. Avery, British Journal of Biomedical Science' … these are among the best available and they are well set out.' P. Sprent, Journal of Applied EcologyTable of Contents1. The binomial distribution function; 2. The Poisson distribution function; 3. Binomial coefficients; 4. The normal distribution function; 5. Percentage points of the normal distribution; 6. Logarithms of factorials; 7. The chi-squared distribution function; 8. Percentage points of the chi-squared distribution; 9. The t-distribution function; 10. Percentage points of the t-distribution; 11. Percentage points of Behrens' distribution; 12. Percentage points of the F-distribution; 13. Percentage points of the correlation coefficient r when rho = 0; 14. Percentage points of Spearman's S; 15. Percentage points of Kendall's K; 16. The z-transformation of the correlation coefficient; 17. The inverse of the z-transformation; 18. Percentage points of the distribution of the number of runs; 19. Upper percentage points of the two-sample Kolmogorov–Smirnov distribution; 20 Percentage points of Wilcoxon's signed-rank distribution; 21. Percentage points of the Mann–Whitney distribution; 22A. Expected values of normal order statistics (normal scores); 22B. Sums of squares of normal scores; 23. Upper percentage points of the one-sample Kolmogorov–Smirnov distribution; 24. Upper percentage points of Friedmann's distribution; 25. Upper percentage points of the Kruskal–Wallis distribution; 26. Hypergeometric probabilities; 27. Random sampling numbers; 28. Random normal deviates; 29. Bayesian confidence limits for a binomial parameter; 30. Bayesian confidence limits for a Poisson mean; 31. Bayesian confidence limits for the square of a multiple correlation coefficient; A note on interpolation; Constants.
£17.67
World Scientific Publishing Co Pte Ltd First Look At Rigorous Probability Theory, A (2nd
Book SynopsisThis textbook is an introduction to probability theory using measure theory. It is designed for graduate students in a variety of fields (mathematics, statistics, economics, management, finance, computer science, and engineering) who require a working knowledge of probability theory that is mathematically precise, but without excessive technicalities. The text provides complete proofs of all the essential introductory results. Nevertheless, the treatment is focused and accessible, with the measure theory and mathematical details presented in terms of intuitive probabilistic concepts, rather than as separate, imposing subjects. In this new edition, many exercises and small additional topics have been added and existing ones expanded. The text strikes an appropriate balance, rigorously developing probability theory while avoiding unnecessary detail.Table of ContentsThe Need for Measure Theory; Probability Triples; Further Probabilistic Foundations; Expected Values; Inequalities and Convergence; Distributions of Random Variables; Stochastic Processes and Gambling Games; Discrete Markov Chains; More Probability Theorems; Weak Convergence; Characteristic Functions; Decomposition of Probability Laws; Conditional Probability and Expectation; Martingales; General Stochastic Processes.
£25.65
V&R unipress GmbH Alexandr A. Chuprov: Life, Work, Correspondence
Book Synopsis
£62.79
Viva Books Statistics
Book SynopsisStatistics in unusual in its emphasis on the models that underlie statistical inference. The authors make the models comprehensible and show why choosing the wrong model can lead students astray. Carefully constructed exercises in every chapter offer practice in computational skills. Other call for rough estimates and qualitative judgments, so students are forced to come to grips with the concepts instead of mechanically applied formulas. Most sections close with an exercise set; the answers are in the back of the book, often with complete solutions. Chapters also have review exercises, without answers, for homework and tests. Illustrations are in integral part of the exposition. Beginners learn how to read histograms and scatterplots and how to think about these graphics in the context of real problems.
£28.49
Hodder Education Cambridge International AS & A Level Mathematics
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.
£28.12
Springer Mixed Effects Models and Extensions in Ecology with R
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.
£87.99
Orion Publishing Co How to Read Numbers
Book SynopsisEvery day, most of us will read or watch something in the news that is based on statistics in some way. Sometimes it''ll be obvious - ''X people develop cancer every year'' - and sometimes less obvious - ''How smartphones destroyed a generation''. Statistics are an immensely powerful tool for understanding the world, but in the wrong hands they can be dangerous.Introducing you to the common mistakes that journalists make and the tricks they sometimes deploy, HOW TO READ NUMBERS is a vital guide that will help you understand when and how to trust the numbers in the news - and, just as importantly, when not to.Trade ReviewA charming, practical and insightful guide. You might not even notice how much you're learning - you'll be too busy having fun -- TIM HARFORD, author of HOW TO MAKE THE WORLD ADD UPA vital plea to take statistics more seriously - the prose being as clear and elegant as the numbers -- SATHNAM SANGHERA, author of EMPIRELANDReading this book is strongly correlated with not looking stupid. Highly recommended -- HELEN LEWIS, author of Difficult WomenAn excellent guide to everyday statistics . . . the authors do a splendid job of stringing words together so smartly that even difficult concepts are explained and so understood with ease. [A] timely and lively book -- Manjit Kumar * THE TIMES *Wonderfully written - incredibly readable. It should be made compulsory reading for everyone before they leave school -- EVAN DAVISAn erudite, enlightening guide to the numbers we read in the news - and why they are so often wrong. The authors make sense of dense material and offer engrossing insights into sampling bias, statistical significance and the dangers of believing the casual language used in newspapers * INDEPENDENT *[A] fascinating, easy-to-read explanation of how to interpret numbers in the news . . . their enlightening book provides us with the tools to spot when we're being led astray -- Nick Rennison * DAILY MAIL *An absolute lifesaver . . . Breezy, easy to read, funny and loaded with useful information -- IAN DUNT, author of HOW TO BE A LIBERALA great combination of important and accessible -- MISHAL HUSAINBrilliant . . . part of the joy of How to Read Numbers is how light and fun it is. At the end of the process, you'll be better equipped to understand what it means when a glass of red wine can both increase and decrease your chances of getting cancer, how many portions of fruit and veg you need to eat each day, and any number of stories about numbers you might read or hear * THE BIG ISSUE *
£9.49
Cambridge University Press Statistical Hypothesis Testing in Context Volume
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.
£47.49
Springer International Publishing AG ggplot2: Elegant Graphics for Data Analysis
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.
£37.99
Basic Books The Art of Statistics: How to Learn from Data
Book Synopsis
£17.59
Stata Press Financial Econometrics Using Stata
Book SynopsisFinancial Econometrics Using Stata is an essential reference for graduate students, researchers, and practitioners who use Stata to perform intermediate or advanced methods. After discussing the characteristics of financial time series, the authors provide introductions to ARMA models, univariate GARCH models, multivariate GARCH models, and applications of these models to financial time series. The last two chapters cover risk management and contagion measures. After a rigorous but intuitive overview, the authors illustrate each method by interpreting easily replicable Stata examples.Table of ContentsIntroduction to financial time series. ARMA models. Modeling volatilities, ARCH models, and GARCH models. Multivariate GARCH models. Risk management. Contagion analysis.
£65.54
Pelagic Publishing Statistics for Ecologists Using R and Excel: Data
Book SynopsisThis is a book about the scientific process and how you apply it to data in ecology. You will learn how to plan for data collection, how to assemble data, how to analyze data and finally how to present the results. The book uses Microsoft Excel and the powerful Open Source R program to carry out data handling as well as producing graphs. Statistical approaches covered include: data exploration; tests for difference – t-test and U-test; correlation – Spearman’s rank test and Pearson product-moment; association including Chi-squared tests and goodness of fit; multivariate testing using analysis of variance (ANOVA) and Kruskal–Wallis test; and multiple regression. Key skills taught in this book include: how to plan ecological projects; how to record and assemble your data; how to use R and Excel for data analysis and graphs; how to carry out a wide range of statistical analyses including analysis of variance and regression; how to create professional looking graphs; and how to present your results. New in this edition: a completely revised chapter on graphics including graph types and their uses, Excel Chart Tools, R graphics commands and producing different chart types in Excel and in R; an expanded range of support material online, including; example data, exercises and additional notes & explanations; a new chapter on basic community statistics, biodiversity and similarity; chapter summaries and end-of-chapter exercises. Praise for the first edition: This book is a superb way in for all those looking at how to design investigations and collect data to support their findings. – Sue Townsend, Biodiversity Learning Manager, Field Studies Council [M]akes it easy for the reader to synthesise R and Excel and there is extra help and sample data available on the free companion webpage if needed. I recommended this text to the university library as well as to colleagues at my student workshops on R. Although I initially bought this book when I wanted to discover R I actually also learned new techniques for data manipulation and management in Excel – Mark Edwards, EcoBlogging A must for anyone getting to grips with data analysis using R and excel. – Amazon 5-star review It has been very easy to follow and will be perfect for anyone. – Amazon 5-star review A solid introduction to working with Excel and R. The writing is clear and informative, the book provides plenty of examples and figures so that each string of code in R or step in Excel is understood by the reader. – Goodreads, 4-star reviewTrade ReviewThe text that I have found most helpful in getting back to using R has been Mark Gardener's Statistics for Ecologists Using R and Excel. This excellent little book leads the reader nicely through the basics. Starting with how to down load R and getting data into the programme through exploratory statistics and into basic analysis with a section on reporting results which includes visualising data. It also makes it easy for the reader to synthesise R and Excel and there is extra help and sample data available on the free companion webpage if needed. I recommended this text to the university library as well as to colleagues at my student workshops on R. Although I initially bought this book when I wanted to discover R I actually also learned new techniques for data manipulation and management in Excel. (This review refers to the first edition.) -- Mark Edwards * EcoBlogging *This book is a superb way in for all those looking at how to design investigations and collect data to support their findings. (This review refers to the first edition.) -- Sue Townsend, Biodiversity Learning Manager, Field Studies CouncilTable of ContentsPreface xi 1. Planning 2. Data recording 3. Beginning data exploration – using software tools 4. Exploring data – looking at numbers 5. Exploring data – which test is right? 6. Exploring data – using graphs 7. Tests for differences 8. Tests for linking data – correlations 9. Tests for linking data – associations 10. Differences between more than two samples 11. Tests for linking several factors 12. Community ecology 13. Reporting results 14. Summary Glossary Appendices Index
£53.99
Profile Books Ltd The Perfect Bet: Taking the Luck out of Gambling
Book SynopsisGamblers have been trying to figure out how to game the system since our ancestors first made wagers over dice fashioned from knucklebones: in revolutionary Paris, the 'martingale' strategy was rumoured to lead to foolproof success at roulette ; today, professional gamblers are using cutting-edge techniques to tilt the odds in their favour. Science is giving us the competitive edge over opponents, casinos and bookmakers. But is there such a thing as a perfect bet? The Perfect Bet looks beyond probability and statistics to examine how wagers have inspired a plethora of new disciplines - spanning chaos theory, machine learning and game theory - which are not just revolutionising gambling, but changing our fundamental notions about chance, randomness and luck. Explaining why poker is gaming's last bastion of human superiority over AI, how methods originally developed for the US nuclear programme are helping pundits predict sports results and why a new breed of algorithms are losing banks millions, The Perfect Bet has the inside track on any wager you'd care to place.Trade ReviewThis book is full of magic. It's brimming with clever people and clever ideas... The links between betting and science run deep and wide, allowing Kucharski to cover some thrilling intellectual territory. * New Scientist *Terrific: beautifully written, solidly researched and full of surprises * New York Times Numberplay blog *Elegant and amusing ... anyone planning to enter a casino or place an online bet would be advised to keep this book handy * Wall Street Journal *Kucharski's clear prose and eye for an entertaining historical anecdote give his book an accessible feel ... an enjoyable account. * Racing Post *[An] enjoyable... paean to human ingenuity, and a Robin Hood tale of wealth redistribution. * Daily Telegraph *Great stories of how smart people have used maths, statistics and science to try and beat the odds - legally' -- David Spiegelhalter, Winton Professor for the Public Understanding of Risk, University of CambridgeA wild ride through the history, psychology, mathematics, and technology of gaming - a remarkable look behind the curtain of what most people think is intuitive, but isn't -- Paul Offit, author of Bad FaithWith an entertaining writing style, Adam Kucharski guides us through the history and state of the art of "The Perfect Bet," showing us how mathematics and computers are used to come up with optimal ways to gamble, play games, bluff, and invest our money. Extremely well-written and carefully researched. I highly recommend it. -- Arthur Benjamin, Author of 'The Magic of Maths'A lucid yet sophisticated look at the mathematics of probability as it's played out on gaming tables, arenas, and fields... Gamblers and math buffs alike will enjoy it for its smart approach to real-world problems * Kirkus Reviews *
£9.49
Springer Nature Switzerland AG Foundations of Modern Probability
Book SynopsisThe first edition of this single volume on the theory of probability has become a highly-praised standard reference for many areas of probability theory. Chapters from the first edition have been revised and corrected, and this edition contains four new chapters. New material covered includes multivariate and ratio ergodic theorems, shift coupling, Palm distributions, Harris recurrence, invariant measures, and strong and weak ergodicity.Trade Review“The book under review is the magnum opus of a brilliant scholar of probability. … An important feature of the book … is the writing style. … The choice of topics is excellent, some of which are not covered elsewhere. This book would make an outstanding text for graduate courses in measure theoretic probability, and for graduate students and faculty doing research in probability. The book is a delight to read. Highly recommended.” (Myron Hlynka, Mathematical Reviews, April, 2022)Table of ContentsIntroduction and Reading Guide.- I.Measure Theoretic Prerequisites: 1.Sets and functions, measures and integration.- 2.Measure extension and decomposition.- 3.Kernels, disintegration, and invariance.- II.Some Classical Probability Theory: 4.Processes, distributions, and independence.- 5.Random sequences, series, and averages.- 6.Gaussian and Poisson convergence.- 7.Infinite divisibility and general null-arrays.- III.Conditioning and Martingales: 8.Conditioning and disintegration.- 9.Optional times and martingales.- 10.Predictability and compensation.- IV.Markovian and Related Structures:11.Markov properties and discrete-time chains.- 12.Random walks and renewal processes.- 13.Jump-type chains and branching processes.- V.Some Fundamental Processes: 14.Gaussian processes and Brownian motion.- 15.Poisson and related processes.- 16.Independent-increment and Lévy processes.- 17.Feller processes and semi-groups.- VI.Stochastic Calculus and Applications: 18.Itô integration and quadratic variation.- 19.Continuous martingales and Brownian motion.- 20.Semi-martingales and stochastic integration.- 21.Malliavin calculus.- VII.Convergence and Approximation: 22.Skorohod embedding and functional convergence.- 23.Convergence in distribution.- 24.Large deviations.- VIII.Stationarity, Symmetry and Invariance: 25.Stationary processes and ergodic theorems.- 26.Ergodic properties of Markov processes.- 27.Symmetric distributions and predictable maps.- 28.Multi-variate arrays and symmetries.- IX.Random Sets and Measures: 29.Local time, excursions, and additive functionals.- 30.Random mesures, smoothing and scattering.- 31.Palm and Gibbs kernels, local approximation.- X.SDEs, Diffusions, and Potential Theory: 32.Stochastic equations and martingale problems.- 33.One-dimensional SDEs and diffusions.- 34.PDE connections and potential theory.- 35.Stochastic differential geometry.- Appendices.- 1.Measurable maps.- 2.General topology.- 3.Linear spaces.- 4.Linear operators.- 5.Function and measure spaces.- 6.Classes and spaces of sets,- 7.Differential geometry.- Notes and References.- Bibliography.- Indices: Authors.- Topics.- Symbols.
£49.49
Macmillan Higher Education The Practice of Statistics for the Apr Course
Book SynopsisExperience the best: The Practice of Statistics is the ultimate choice for AP Statistics. Authored by seasoned high school AP Statistics educators, Daren Starnes and Josh Tabor, along with a team of experienced AP teacher/leaders, the Seventh Edition of The Practice of Statistics brings a fresh perspective through 9 Units that align perfectly with the CED. Created to instill a deep understanding of the core principles of statistics and the problem-solving methods involved, TPS7 equips students with the essential statistical thinking skills necessary for future endeavors, careers, and everyday decision-making, while also ensuring success on the AP Statistics Exam. With a multitude of worked examples and practice exercises strategically placed throughout, students have plenty of opportunities to strengthen their skills on a daily basis and prepare for the exam format. And thats not all - the renowned resource program now offers even greater support with the introduction of the new Achieve digital platform. The online homework program has been revamped to provide an extensive homework and assessment system, offering comprehensive support for daily assignments, quizzes, and tests. For students who may be struggling or seeking an extra challenge, the extensive video program is there to offer guidance. Meanwhile, teachers are backed by the most comprehensive Teachers Edition and resource program available. No matter if youre a first-time or experienced AP Statistics teacher, this program is perfect for you. Better than ever: The Practice of Statistics is the most trusted program for AP Statistics.
£77.99
MIT Press Ltd The Math You Need
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
£49.40
Kaplan Publishing Painless Statistics
Book SynopsisWhether you’re a student or an adult looking to refresh your knowledge, Barron’s Painless Statistics provides review and practice in an easy, step-by-step format.An essential resource for: Virtual learning Homeschool Learning pods Supplementing classes/in-person learning Inside you’ll find: Clear examples for all topics, including data and distributions, basic probability, confidence intervals, bivariate statistics, and much more Diagrams, charts, and instructive math illustrations Painless tips, common pitfalls, and informative sidebars Math talk boxes that translate complex “math speak” into easy-to-understand language Brain Tickler quizzes throughout each chapter to test your progress
£11.69
Elsevier Science & Technology Simulation
Book SynopsisTrade Review"This textbook contains and describes all the tools one needs to plan and to carry out a simulation study as well as to analyze its results." --J.Wolters, zbMATH Open "It presents the statistics needed to analyze simulated data and to validate the simulation model. In this edition, several new topics are included as well as a number of new exercises." --Vigirdas Mackevicius, zbMATH OpenTable of Contents1. Introduction 2. Elements of Probability 3. Random Numbers 4. Generating Discrete Random Variables 5. Generating Continuous Random Variables 6. The Multivariate Normal Distribution and Copulas 7. The Discrete Event Simulation Approach 8. Statistical Analysis of Simulated Data 9. Variance Reduction Techniques 10. Additional Variance Reduction Techniques 11. Statistical Validation Techniques 12. Markov Chain Monte Carlo Methods
£69.26
Penguin Putnam Inc Numbers Dont Lie 71 Stories to Help Us Understand
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
£15.20
MP-AMM American Mathematical A First Course in Stochastic Calculus
Book SynopsisA complete guide for advanced undergraduate students to take the next step in exploring probability theory and for master's students in mathematical finance who would like to build an intuitive and theoretical understanding of stochastic processes.Trade ReviewLouis-Pierre Arguin's masterly introduction to stochastic calculus seduces the reader with its quietly conversational style; even rigorous proofs seem natural and easy. Full of insights and intuition, reinforced with many examples, numerical projects, and exercises, this book by a prize-winning mathematician and great teacher fully lives up to the author's reputation. I give it my strongest possible recommendation."" —Jim Gatheral, Baruch College""I happen to be of a different persuasion, about how stochastic processes should be taught to undergraduate and MA students. But I have long been thinking to go against my own grain at some point and try to teach the subject at this level—together with its applications to finance—in one semester. Louis-Pierre Arguin's excellent and artfully designed text will give me the ideal vehicle to do so."" —Ioannis Karatzas, Columbia University, New YorkTable of Contents Basic notions of probability Gaussian processes Properties of Brownian motion Martingales Ito calculus Multivariate Ito calculus Ito processes and stochastic differential equations The Markov property Change of probability Applications to mathematical finance Bibliography Index
£71.06
Dorling Kindersley Ltd Simply Maths
Book Synopsis
£11.69
CRC Press Statistical Learning with Sparsity
Book SynopsisDiscover New Methods for Dealing with High-Dimensional DataA sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underlying signal in a set of data.Top experts in this rapidly evolving field, the authors describe the lasso for linear regression and a simple coordinate descent algorithm for its computation. They discuss the application of â1 penalties to generalized linear models and support vector machines, cover generalized penalties such as the elastic net and group lasso, and review numerical methods for optimization. They also present statistical inference methods for fitted (lasso) models, including the bootstrap, Bayesian methods, and recently developed approaches. In addition, the book examines matrix dTrade Review"The authors study and analyze methods using the sparsity property of some statistical models in order to recover the underlying signal in a dataset. They focus on the Lasso technique as an alternative to the standard least-squares method."—Zentralblatt MATH 1319Table of ContentsIntroduction. The Lasso for Linear Models. Generalized Linear Models. Generalizations of the Lasso Penalty. Optimization Methods. Statistical Inference. Matrix Decompositions, Approximations, and Completion. Sparse Multivariate Methods. Graphs and Model Selection. Signal Approximation and Compressed Sensing. Theoretical Results for the Lasso. Bibliography. Author Index. Index.
£39.89
Profile Books Ltd Chancing It: The Laws of Chance and How They Can
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 *
£12.06
Princeton University Press Statistics by Simulation
Book Synopsis
£32.30
CRC Press Linear Models with R
Book SynopsisA Hands-On Way to Learning Data AnalysisPart of the core of statistics, linear models are used to make predictions and explain the relationship between the response and the predictors. Understanding linear models is crucial to a broader competence in the practice of statistics. Linear Models with R, Third Edition explains how to use linear models in physical science, engineering, social science, and business applications. The book incorporates several improvements that reflect how the world of R has greatly expanded since the publication of the second edition.New to the Third Edition 40% more content with more explanation and examples throughout New chapter on sampling featuring simulation-based methods Model assessment methods discussed Explanation chapter expanded to include introductory ideas about causation Model interpretation in the presence of transformation Crossvalidation for model s
£71.24
Cengage Learning, Inc Essentials of Statistics for Business Economics
Book SynopsisDiscover how statistical information impacts decisions in today's business world as Anderson/Sweeney/Williams/Camm/Cochran/Fry/Ohlmann's leading ESSENTIALS OF STATISTICS FOR BUSINESS AND ECONOMICS, 9E connects concepts in each chapter to real-world practice. This edition delivers sound statistical methodology, a proven problem-scenario approach and meaningful applications that reflect the latest developments in business and statistics today. More than 350 new and proven real business examples, a wealth of practical cases and meaningful hands-on exercises highlight statistics in action. You gain practice using leading professional statistical software with exercises and appendices that walk you through using JMP Student Edition 14 and Excel 2016. WebAssign's online course management systems is available separately to further strengthen this business statistics approach and helps you maximize your course success.Table of Contents1. Data and Statistics. 2. Descriptive Statistics: Tabular and Graphical Displays. 3. Descriptive Statistics: Numerical Measures. 4. Introduction to Probability. 5. Discrete Probability Distributions. 6. Continuous Probability Distributions. 7. Sampling and Sampling Distributions. 8. Interval Estimation. 9. Hypothesis Tests. 10. Inference about Means and Proportions with Two Populations. 11. Inferences about Population Variances. 12. Comparing Multiple Proportions, Test of Independence and Goodness of Fit. 13. Experimental Design and Analysis of Variance. 14. Simple Linear Regression. 15. Multiple Regression. Appendix A: References and Bibliography. Appendix B: Tables. Appendix C: Summation Notation. Appendix D: Self-Test Solutions and Answers to Even-Numbered Exercises. (online) Appendix E: Microsoft Excel 2016 and Tools for Statistical Analysis. Appendix F: Computing p-Values Using Minitab and Excel.
£84.54
Princeton University Press The Rise of Statistical Thinking 18201900
Book Synopsis
£25.20
HarperCollins Publishers THE TYRANNY OF NUMBERS Why Counting Cant Make Us Happy
Book SynopsisNever before have we attempted to measure as much as we do today. Why are we so obsessed with numbers? What can they really tell us?Trade Review‘A great antidote to cynicism, and a sharply witty reminder of what is important in life.’ Independent ‘Wonderfully subversive.’ Guardian.
£11.39
HarperCollins Publishers Statistics Collins Advanced Mathematics
Book SynopsisEnsure extensive knowledge and top marks in A Level Statistics for your students, with enhanced learning through interesting contexts and tools. Full guidance on coursework tasks and practice exercises for each topic compound understanding and expertise. Filled with A Level content and practice (not updated for 2004 curriculum onwards).
£27.99
HarperCollins Publishers Cambridge International AS A Level Mathematics
Book SynopsisThis book provides in-depth coverage of Probability & Statistics 1 for Cambridge International AS and A Level Mathematics 9709, for examination from 2020 onwards. With a clear focus on mathematics in life and work, this text builds the key mathematical skills and knowledge that will open up a wide range of careers and further study.Exam Board: Cambridge Assessment International EducationFirst teaching: 2018 First examination: 2020This student book is part of a series of nine books covering the complete syllabus for Cambridge International AS and A Level Mathematics (9709) and Further Mathematics (9231), for first teaching from September 2018 and examination from 2020. This title is endorsed by Cambridge Assessment International Education.Written by expert authors, this Student Book: covers the complete content of Probability & Statistics 1 with clear references to what you will learn at the start of each chapter, and coverage that clearly and directly matches the Cambridge syllabus set
£20.99
HarperCollins Publishers Cambridge International AS A Level Mathematics
Book SynopsisThis book provides in-depth coverage of Probability & Statistics 2 for Cambridge International A Level Mathematics 9709, for examination from 2020 onwards. With a clear focus on mathematics in life and work, this text builds the key mathematical skills and knowledge that will open up a wide range of careers and further study.
£20.99
HarperCollins Publishers Inc The Upside of Irrationality
Book SynopsisNew York Times Bestseller“Dan Ariely is a genius at understanding human behavior: no economist does a better job of uncovering and explaining the hidden reasons for the weird ways we act.” — James Surowiecki, author of The Wisdom of Crowds Behavioral economist and New York Times bestselling author of Predictably Irrational Dan Ariely offers a much-needed take on the irrational decisions that influence our dating lives, our workplace experiences, and our temptation to cheat in any and all areas. Fans of Freakonomics, Survival of the Sickest, and Malcolm Gladwell’s Blink and The Tipping Point will find many thought-provoking insights in The Upside of Irrationality.How can large bonuses sometimes make CEOs less productive?Why is revenge so important to us?How can confusing directions actually help us?
£16.14
HarperCollins Publishers Inc The Upside of Irrationality
Book Synopsis“Dan Ariely is a genius at understanding human behavior: no economist does a better job of uncovering and explaining the hidden reasons for the weird ways we act.” — James Surowiecki, author of The Wisdom of Crowds Behavioral economist and New York Times bestselling author of Predictably Irrational Dan Ariely returns to offer a much-needed take on the irrational decisions that influence our dating lives, our workplace experiences, and our temptation to cheat in any and all areas. Fans of Freakonomics, Survival of the Sickest, and Malcolm Gladwell’s Blink and The Tipping Point will find many thought-provoking insights in The Upside of Irrationality.
£26.24
HarperCollins Publishers Inc The End of Average
Book Synopsis
£14.39
HarperCollins Publishers Inc The Hot Hand
Book Synopsis
£24.38
McGraw-Hill Education - Europe Introduction to Probability and Statistics
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
£53.09
