Probability and statistics Books

Book Synopsis

Read more less

Book Synopsis

Read more less

Book SynopsisProvides a humorous tour through modern statistics as it is practiced in a wide variety of fields - from the humanities to the sciences. The book begins with a brief history of the subject, then proceeds to cover data analysis, probability and all topics crucial to the study of statistics.

Read more less

Book Synopsis

Read more less

Book SynopsisProbability is an area of mathematics of tremendous contemporary importance across all aspects of human endeavour. This book is a compact account of the basic features of probability and random processes at the level of first and second year mathematics undergraduates and Masters'' students in cognate fields. It is suitable for a first course in probability, plus a follow-up course in random processes including Markov chains.A special feature is the authors'' attention to rigorous mathematics: not everything is rigorous, but the need for rigour is explained at difficult junctures. The text is enriched by simple exercises, together with problems (with very brief hints) many of which are taken from final examinations at Cambridge and Oxford. The first eight chapters form a course in basic probability, being an account of events, random variables, and distributions - discrete and continuous random variables are treated separately - together with simple versions of the law of large numbersTable of ContentsPART A BASIC PROBABILITY; PART B FURTHER PROBABILITY

Read more less

Book Synopsis

Read more less

Book SynopsisHow do you remember more and forget less?How can you earn more and become more creative just by moving house?And how do you pack a car boot most efficiently?This is your shortcut to the art of the shortcut.Mathematics is full of better ways of thinking, and with over 2,000 years of knowledge to draw on, Oxford mathematician Marcus du Sautoy interrogates his passion for shortcuts in this fresh and fascinating guide. After all, shortcuts have enabled so much of human progress, whether in constructing the first cities around the Euphrates 5,000 years ago, using calculus to determine the scale of the universe or in writing today's algorithms that help us find a new life partner.As well as looking at the most useful shortcuts in history such as measuring the circumference of the earth in 240 BC to diagrams that illustrate how modern GPS works Marcus also looks at how you can use shortcuts in investing or how to learn a musical instrument to memory techniques. He talks to, among many, the Trade Review‘enjoyably clever …with vividly illustrated chapters about the real-world applications of algebra, geometry, probability theory…It’s Du Sautoy, in the end, who provides the wisest commentary’ Steven Poole, Guardian ‘If you thought Maths was all about long stuff, like long division and long multiplication and taking a long, long time to figure things out, Marcus du Sautoy shows that it's just the opposite. Full of humour, stories and the lightest of touches, this is a sight-seeing tour of some of the world's greatest neat dodges, unexpected turns and useful cut-throughs. Prepare to be caught short’ Michael Rosen ‘This book will change the way you look at the world. It's chock full of stories, ideas and clever tricks – I loved it. Marcus is a maestro at making big ideas come alive – he deserves his place alongside Richard Dawkins, E. O. Wilson and Carlo Rovelli in the pantheon of great modern science writers’ Rohan Silva, CEO and founder of Second Home ‘If mathematics has proved anything, it is that shortcuts can change the world. Marcus du Sautoy has come up with a smart, well written and entertaining guide to the connecting tunnels, underpasses and other tricks to traverse the trials of everyday life’ Roger Highfield, author, broadcaster and Science Director at the Science Museum ‘The joy of du Sautoy’s book isn’t really the art of the real-world shortcut at all. It is the romp through mathematical ideas, from place value to non Euclidean geometry to probability theory…There are vivid historical examples of scientists and others using mathematical ideas to solve problems’ Tim Harford, Financial Times

Read more less

Book SynopsisCan you solve the problem of The Unfair Subway?Marvin gets off work at random times between 3 and 5 p.m. His mother lives uptown, his girlfriend downtown. He takes the first subway that comes in either direction and eats dinner with the one he is delivered to. His mother complains that he never comes to see her, but he says she has a 50-50 chance. He has had dinner with her twice in the last 20 working days. Explain.Marvin''s adventures in probability are one of the fifty intriguing puzzles that illustrate both elementary ad advanced aspects of probability, each problem designed to challenge the mathematically inclined. From The Flippant Juror and The Prisoner''s Dilemma to The Cliffhanger and The Clumsy Chemist, they provide an ideal supplement for all who enjoy the stimulating fun of mathematics.Professor Frederick Mosteller, who teaches statistics at Harvard University, has chosen the problems for originality, general interest, or because they demonstrate valuable techniques. In addition, the problems are graded as to difficulty and many have considerable stature. Indeed, one has enlivened the research lives of many excellent mathematicians. Detailed solutions are included. There is every probability you''ll need at least a few of them.

Read more less

Book SynopsisUnderstanding the world of R programming and analysis has never been easier Most guides to R, whether books or online, focus on R functions and procedures.Table of ContentsIntroduction 1 About This Book 1 Similarity with This Other For Dummies Book 2 What You Can Safely Skip 2 Foolish Assumptions 2 How This Book Is Organized 3 Part 1: Getting Started with Statistical Analysis with R 3 Part 2: Describing Data 3 Part 3: Drawing Conclusions from Data 3 Part 4: Working with Probability 3 Part 5: The Part of Tens 4 Online Appendix A: More on Probability 4 Online Appendix B: Non-Parametric Statistics 4 Online Appendix C: Ten Topics That Just Didn’t Fit in Any Other Chapter 4 Icons Used in This Book 4 Where to Go from Here 5 Part 1: Getting Started with Statistical Analysis with R 7 Chapter 1: Data, Statistics, and Decisions 9 The Statistical (and Related) Notions You Just Have to Know 10 Samples and populations 10 Variables: Dependent and independent 11 Types of data 12 A little probability 13 Inferential Statistics: Testing Hypotheses 14 Null and alternative hypotheses 14 Two types of error 15 Chapter 2: R: What It Does and How It Does It 17 Downloading R and RStudio 18 A Session with R 21 The working directory 21 So let’s get started, already 22 Missing data 26 R Functions 26 User-Defined Functions 28 Comments 29 R Structures 29 Vectors 30 Numerical vectors 30 Matrices 31 Factors 33 Lists 34 Lists and statistics 35 Data frames 36 Packages 39 More Packages 42 R Formulas 43 Reading and Writing 44 Spreadsheets 44 CSV files 46 Text files 47 Part 2: Describing Data 49 Chapter 3: Getting Graphic 51 Finding Patterns 51 Graphing a distribution 52 Bar-hopping 53 Slicing the pie 54 The plot of scatter 55 Of boxes and whiskers 56 Base R Graphics 57 Histograms 57 Adding graph features 59 Bar plots 60 Pie graphs 62 Dot charts 62 Bar plots revisited 64 Scatter plots 67 Box plots 71 Graduating to ggplot2 71 Histograms 72 Bar plots 74 Dot charts 75 Bar plots re-revisited 78 Scatter plots 82 Box plots 86 Wrapping Up 89 Chapter 4: Finding Your Center 91 Means: The Lure of Averages 91 The Average in R: mean() 93 What’s your condition? 93 Eliminate $-signs forth with() 94 Exploring the data 95 Outliers: The flaw of averages 96 Other means to an end 97 Medians: Caught in the Middle 99 The Median in R: median() 100 Statistics à la Mode 101 The Mode in R 101 Chapter 5: Deviating from the Average 103 Measuring Variation 104 Averaging squared deviations: Variance and how to calculate it 104 Sample variance 107 Variance in R 107 Back to the Roots: Standard Deviation 108 Population standard deviation 108 Sample standard deviation 109 Standard Deviation in R 109 Conditions, Conditions, Conditions 110 Chapter 6: Meeting Standards and Standings 111 Catching Some Z’s 112 Characteristics of z-scores 112 Bonds versus the Bambino 113 Exam scores 114 Standard Scores in R 114 Where Do You Stand? 117 Ranking in R 117 Tied scores 117 Nth smallest, Nth largest 118 Percentiles 118 Percent ranks 120 Summarizing 121 Chapter 7: Summarizing It All 123 How Many? 123 The High and the Low 125 Living in the Moments 125 A teachable moment 126 Back to descriptives 126 Skewness 127 Kurtosis 130 Tuning in the Frequency 131 Nominal variables: table() et al 131 Numerical variables: hist() 132 Numerical variables: stem() 138 Summarizing a Data Frame 139 Chapter 8: What’s Normal? 143 Hitting the Curve 143 Digging deeper 144 Parameters of a normal distribution 145 Working with Normal Distributions 147 Distributions in R 147 Normal density function 147 Cumulative density function 152 Quantiles of normal distributions 155 Random sampling 156 A Distinguished Member of the Family 158 Part 3: Drawing Conclusions From Data 161 Chapter 9: The Confidence Game: Estimation 163 Understanding Sampling Distributions 164 An EXTREMELY Important Idea: The Central Limit Theorem 165 (Approximately) Simulating the central limit theorem 167 Predictions of the central limit theorem 171 Confidence: It Has Its Limits! 173 Finding confidence limits for a mean 173 Fit to a t 175 Chapter 10: One-Sample Hypothesis Testing 179 Hypotheses, Tests, and Errors 179 Hypothesis Tests and Sampling Distributions 181 Catching Some Z’s Again 183 Z Testing in R 185 t for One 187 t Testing in R 188 Working with t-Distributions 189 Visualizing t-Distributions 190 Plotting t in base R graphics 191 Plotting t in ggplot2 192 One more thing about ggplot2 197 Testing a Variance 198 Testing in R 199 Working with Chi-Square Distributions 201 Visualizing Chi-Square Distributions 201 Plotting chi-square in base R graphics 202 Plotting chi-square in ggplot2 203 Chapter 11: Two-Sample Hypothesis Testing 205 Hypotheses Built for Two 205 Sampling Distributions Revisited 206 Applying the central limit theorem 207 Z’s once more 208 Z-testing for two samples in R 210 t for Two 212 Like Peas in a Pod: Equal Variances 212 t-Testing in R 214 Working with two vectors 214 Working with a data frame and a formula 215 Visualizing the results 216 Like p’s and q’s: Unequal variances 219 A Matched Set: Hypothesis Testing for Paired Samples 220 Paired Sample t-testing in R 222 Testing Two Variances 222 F-testing in R 224 F in conjunction with t 225 Working with F-Distributions 226 Visualizing F-Distributions 226 Chapter 12: Testing More than Two Samples 231 Testing More Than Two 231 A thorny problem 232 A solution 233 Meaningful relationships 237 ANOVA in R 237 Visualizing the results 239 After the ANOVA 239 Contrasts in R 242 Unplanned comparisons 243 Another Kind of Hypothesis, Another Kind of Test 244 Working with repeated measures ANOVA 245 Repeated measures ANOVA in R 247 Visualizing the results 249 Getting Trendy 250 Trend Analysis in R 254 Chapter 13: More Complicated Testing 255 Cracking the Combinations 255 Interactions 257 The analysis 257 Two-Way ANOVA in R 259 Visualizing the two-way results 261 Two Kinds of Variables at Once 263 Mixed ANOVA in R 266 Visualizing the Mixed ANOVA results 268 After the Analysis 269 Multivariate Analysis of Variance 270 MANOVA in R 271 Visualizing the MANOVA results 273 After the analysis 275 Chapter 14: Regression: Linear, Multiple, and the General Linear Model 277 The Plot of Scatter 277 Graphing Lines 279 Regression: What a Line! 281 Using regression for forecasting 283 Variation around the regression line 283 Testing hypotheses about regression 285 Linear Regression in R 290 Features of the linear model 292 Making predictions 292 Visualizing the scatter plot and regression line 293 Plotting the residuals 294 Juggling Many Relationships at Once: Multiple Regression 295 Multiple regression in R 297 Making predictions 298 Visualizing the 3D scatter plot and regression plane 298 ANOVA: Another Look 301 Analysis of Covariance: The Final Component of the GLM 305 But wait — there’s more 311 Chapter 15: Correlation: The Rise and Fall of Relationships 313 Scatter plots Again 313 Understanding Correlation 314 Correlation and Regression 316 Testing Hypotheses About Correlation 319 Is a correlation coefficient greater than zero? 319 Do two correlation coefficients differ? 320 Correlation in R 322 Calculating a correlation coefficient 322 Testing a correlation coefficient 322 Testing the difference between two correlation coefficients 323 Calculating a correlation matrix 324 Visualizing correlation matrices 324 Multiple Correlation 326 Multiple correlation in R 327 Adjusting R-squared 328 Partial Correlation 329 Partial Correlation in R 330 Semipartial Correlation 331 Semipartial Correlation in R 332 Chapter 16: Curvilinear Regression: When Relationships Get Complicated 335 What Is a Logarithm? 336 What Is e? 338 Power Regression 341 Exponential Regression 346 Logarithmic Regression 350 Polynomial Regression: A Higher Power 354 Which Model Should You Use? 358 Part 4: Working with Probability 359 Chapter 17: Introducing Probability 361 What Is Probability? 361 Experiments, trials, events, and sample spaces 362 Sample spaces and probability 362 Compound Events 363 Union and intersection 363 Intersection again 364 Conditional Probability 365 Working with the probabilities 366 The foundation of hypothesis testing 366 Large Sample Spaces 366 Permutations 367 Combinations 368 R Functions for Counting Rules 369 Random Variables: Discrete and Continuous 371 Probability Distributions and Density Functions 371 The Binomial Distribution 374 The Binomial and Negative Binomial in R 375 Binomial distribution 375 Negative binomial distribution 377 Hypothesis Testing with the Binomial Distribution 378 More on Hypothesis Testing: R versus Tradition 380 Chapter 18: Introducing Modeling 383 Modeling a Distribution 383 Plunging into the Poisson distribution 384 Modeling with the Poisson distribution 385 Testing the model’s fit 388 A word about chisqtest() 391 Playing ball with a model 392 A Simulating Discussion 396 Taking a chance: The Monte Carlo method 396 Loading the dice 396 Simulating the central limit theorem 401 Part 5: The Part of Tens 405 Chapter 19: Ten Tips for Excel Emigrés 407 Defining a Vector in R Is Like Naming a Range in Excel 407 Operating on Vectors Is Like Operating on Named Ranges 408 Sometimes Statistical Functions Work the Same Way 412 And Sometimes They Don’t 412 Contrast: Excel and R Work with Different Data Formats 413 Distribution Functions Are (Somewhat) Similar 414 A Data Frame Is (Something) Like a Multicolumn Named Range 416 The sapply() Function Is Like Dragging 417 Using edit() Is (Almost) Like Editing a Spreadsheet 418 Use the Clipboard to Import a Table from Excel into R 419 Chapter 20: Ten Valuable Online R Resources 421 Websites for R Users 421 R-bloggers 421 Microsoft R Application Network 422 Quick-R 422 RStudio Online Learning 422 Stack Overflow 422 Online Books and Documentation 423 R manuals 423 R documentation 423 RDocumentation 423 YOU CANanalytics 423 The R Journal 424 Index 425

Read more less

Book Synopsis

Read more less

Book SynopsisA comprehensive guide to statistics—with information on collecting, measuring, analyzing, and presenting statistical data—continuing the popular 101 series. Data is everywhere. In the age of the internet and social media, we’re responsible for consuming, evaluating, and analyzing data on a daily basis. From understanding the percentage probability that it will rain later today, to evaluating your risk of a health problem, or the fluctuations in the stock market, statistics impact our lives in a variety of ways, and are vital to a variety of careers and fields of practice. Unfortunately, most statistics text books just make us want to take a snooze, but with Statistics 101, you’ll learn the basics of statistics in a way that is both easy-to-understand and apply. From learning the theory of probability and different kinds of distribution concepts, to identifying data patterns and graphing and presenting precise findings, this essential guide can help turn statistical math from scary and complicated, to easy and fun. Whether you are a student looking to supplement your learning, a worker hoping to better understand how statistics works for your job, or a lifelong learner looking to improve your grasp of the world, Statistics 101 has you covered.

Read more less

Book SynopsisAn accessible and contemporary introduction to the methods for determining cause and effect in the social sciencesTrade Review“A new guide to methods for determining cause and effect in the social sciences. In summarising, systematising and prioritising methodological tools for researchers, this book will be of use to all social scientists looking to validate their quantitative findings.”—Dr Simeon Mitropolitski, LSE Review of Books "Cunningham's brilliant book is that rare statistical treatise written for students and practitioners alike. Engaging language and vivid examples bring the tools of causal inference to a broad audience. Read the book, absorb its lessons, and you'll develop the skills you need to credibly assess whether a statistics class, a public policy, or a new business practice truly makes a difference."–Justin Wolfers, University of Michigan "Accessible and engaging. An excellent introduction to the statistics of causal inference."–Alberto Abadie, MIT “Learning about causal effects is the main goal of most empirical research in economics. In this engaging book, Scott Cunningham provides an accessible introduction to this area, full of wisdom and wit and with detailed coding examples for practitioners.”--Guido Imbens, coauthor of Causal Inference "This book will probably shock economics instructors with the clarity, insights, and tools that modern graphical models introduce to the teaching of econometrics. The benefits will outlast the shock."--Judea Pearl, University of California, Los Angeles “Causation versus correlation has been the basis of arguments—economic and otherwise—since the beginning of time. Causal Inference: The Mixtape uses legit real-world examples that I found genuinely thought-provoking. It’s rare that a book prompts readers to expand their outlook; this one did for me.”—Marvin Young (Young MC)

Read more less

Book SynopsisStatistics for Engineers and Scientists stands out for its clear presentation of applied statistics. The book takes a practical approach to methods of statistical modeling and data analysis that are most often used in scientific work. This edition features a unique approach highlighted by an engaging writing style that explains difficult concepts clearly, along with the use of contemporary real world data sets, to help motivate students and show direct connections to industry and research. While focusing on practical applications of statistics, the text makes extensive use of examples to motivate fundamental concepts and to develop intuition.The new edition of Statistics for Engineers and Scientists is also available in McGraw Hill Connect, featuring SmartBook 2.0, Adaptive Learning Assignments, and more!Table of ContentsChapter 1: Sampling and Descriptive StatisticsChapter 2: ProbabilityChapter 3: Propagation of ErrorChapter 4: Commonly Used DistributionsChapter 5: Confidence IntervalsChapter 6: Hypothesis TestingChapter 7: Correlation and Simple Linear RegressionChapter 8: Multiple RegressionChapter 9: Factorial ExperimentsChapter 10: Statistical Quality Control

Read more less

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 5 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 Boost 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 1, including representation of data, permutations and combinations, probability, discrete random variables and the normal distribution.

Read more less

Book SynopsisProbability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.Trade ReviewFrom the reviews: "This beautifully produced book is intended for advanced undergraduates, PhD students, and researchers and practitioners, primarily in the machine learning or allied areas...A strong feature is the use of geometric illustration and intuition...This is an impressive and interesting book that might form the basis of several advanced statistics courses. It would be a good choice for a reading group." John Maindonald for the Journal of Statistical Software "In this book, aimed at senior undergraduates or beginning graduate students, Bishop provides an authoritative presentation of many of the statistical techniques that have come to be considered part of ‘pattern recognition’ or ‘machine learning’. … This book will serve as an excellent reference. … With its coherent viewpoint, accurate and extensive coverage, and generally good explanations, Bishop’s book is a useful introduction … and a valuable reference for the principle techniques used in these fields." (Radford M. Neal, Technometrics, Vol. 49 (3), August, 2007) "This book appears in the Information Science and Statistics Series commissioned by the publishers. … The book appears to have been designed for course teaching, but obviously contains material that readers interested in self-study can use. It is certainly structured for easy use. … For course teachers there is ample backing which includes some 400 exercises. … it does contain important material which can be easily followed without the reader being confined to a pre-determined course of study." (W. R. Howard, Kybernetes, Vol. 36 (2), 2007) "Bishop (Microsoft Research, UK) has prepared a marvelous book that provides a comprehensive, 700-page introduction to the fields of pattern recognition and machine learning. Aimed at advanced undergraduates and first-year graduate students, as well as researchers and practitioners, the book assumes knowledge of multivariate calculus and linear algebra … . Summing Up: Highly recommended. Upper-division undergraduates through professionals." (C. Tappert, CHOICE, Vol. 44 (9), May, 2007) "The book is structured into 14 main parts and 5 appendices. … The book is aimed at PhD students, researchers and practitioners. It is well-suited for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bio-informatics. Extensive support is provided for course instructors, including more than 400 exercises, lecture slides and a great deal of additional material available at the book’s web site … ." (Ingmar Randvee, Zentralblatt MATH, Vol. 1107 (9), 2007) "This new textbook by C. M. Bishop is a brilliant extension of his former book ‘Neural Networks for Pattern Recognition’. It is written for graduate students or scientists doing interdisciplinary work in related fields. … In summary, this textbook is an excellent introduction to classical pattern recognition and machine learning (in the sense of parameter estimation). A large number of very instructive illustrations adds to this value." (H. G. Feichtinger, Monatshefte für Mathematik, Vol. 151 (3), 2007) "Author aims this text at advanced undergraduates, beginning graduate students, and researchers new to machine learning and pattern recognition. … Pattern Recognition and Machine Learning provides excellent intuitive descriptions and appropriate-level technical details on modern pattern recognition and machine learning. It can be used to teach a course or for self-study, as well as for a reference. … I strongly recommend it for the intended audience and note that Neal (2007) also has given this text a strong review to complement its strong sales record." (Thomas Burr, Journal of the American Statistical Association, Vol. 103 (482), June, 2008) "This accessible monograph seeks to provide a comprehensive introduction to the fields of pattern recognition and machine learning. It presents a unified treatment of well-known statistical pattern recognition techniques. … The book can be used by advanced undergraduates and graduate students … . The illustrative examples and exercises proposed at the end of each chapter are welcome … . The book, which provides several new views, developments and results, is appropriate for both researchers and students who work in machine learning … ." (L. State, ACM Computing Reviews, October, 2008) "Chris Bishop’s … technical exposition that is at once lucid and mathematically rigorous. … In more than 700 pages of clear, copiously illustrated text, he develops a common statistical framework that encompasses … machine learning. … it is a textbook, with a wide range of exercises, instructions to tutors on where to go for full solutions, and the color illustrations that have become obligatory in undergraduate texts. … its clarity and comprehensiveness will make it a favorite desktop companion for practicing data analysts." (H. Van Dyke Parunak, ACM Computing Reviews, Vol. 49 (3), March, 2008)Table of ContentsProbability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Read more less

Book SynopsisThe phenomenal international bestseller that shows us how to stop trying to predict everything - and take advantage of uncertaintyWhat have the invention of the wheel, Pompeii, the Wall Street Crash, Harry Potter and the internet got in common? Why are all forecasters con-artists? Why should you never run for a train or read a newspaper? This book is all about Black Swans: the random events that underlie our lives, from bestsellers to world disasters. Their impact is huge; they''re impossible to predict; yet after they happen we always try to rationalize them. ''Taleb is a bouncy and even exhilarating guide ... I came to relish what he said, and even develop a sneaking affection for him as a person'' Will Self, Independent on Sunday''He leaps like some superhero of the mind'' Boyd Tonkin, IndependentTrade ReviewA fascinating study of how we are regularly taken for suckers by the unexpected * Guardian *Like the conversation of a raconteur ... hugely enjoyable - compelling * Financial Times *It has altered modern thinking * The Times *Confirms his status as a guru for every would-be Damien Hirst, George Soros and aspirant despot * Sunday Times *The Black Swan changed my view of how the world works -- Daniel Kahneman, author of Thinking, Fast and SlowGreat fun... brash, stubborn, entertaining, opinionated, curious, cajoling -- Stephen J. Dubner, co-author of FreakonomicsThe most prophetic voice of all * GQ *

Read more less

Book Synopsis''A great introduction to a crucial topic'' Bill Gates''Perhaps the most popular book on statistics ever published ... It''s a marvel ... gave me a peek behind the curtain of statistical manipulation, showing me how the swindling was done so that I would not be fooled again'' Tim HarfordIn 1954, Darrell Huff decided enough was enough. Fed up with politicians, advertisers and journalists using statistics to sensationalise, inflate, confuse, oversimplify and - on occasion - downright lie, he decided to shed light on their ill-informed and sneaky ways. How to Lie with Statistics is the result - the definitive and hilarious primer in the ways statistics are used to deceive.With over one and half million copies sold around the world, it has delighted generations of readers with its cheeky takes on the ins and outs of samples, averages, errors, graphs and indexes. And in the modern world of big data and misinformation, Huff remains the perfect guide tTrade ReviewMore relevant than ever . . . a great introduction to the use of statistics -- Bill GatesA hilarious exploration of mathematical mendacity.... Every time you pick it up, what happens? Bang goes another illusion! * New York Times *A pleasantly subversive little book guaranteed to undermine your faith in the almighty statistic * Atlantic *

Read more less

Book SynopsisThis best-selling book remains the most popular stand-alone text for Advanced Level Statistics. It covers the AS and A2 specifications in Statistics for Advanced Level Maths across all boards. Over 300 worked examples. Advice on how to break down calculations into easy stages. Extensive exercises including real exam questions for practice and exam preparation. End of chapter summaries for consolidation and revision.

Read more less

Book SynopsisThis self-contained textbook introduces all the relevant mathematical concepts needed to understand and use machine learning methods, with a minimum of prerequisites. Topics include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics.Trade Review'This book provides great coverage of all the basic mathematical concepts for machine learning. I'm looking forward to sharing it with students, colleagues, and anyone interested in building a solid understanding of the fundamentals.' Joelle Pineau, McGill University, Montreal'The field of machine learning has grown dramatically in recent years, with an increasingly impressive spectrum of successful applications. This comprehensive text covers the key mathematical concepts that underpin modern machine learning, with a focus on linear algebra, calculus, and probability theory. It will prove valuable both as a tutorial for newcomers to the field, and as a reference text for machine learning researchers and engineers.' Christopher Bishop, Microsoft Research Cambridge'This book provides a beautiful exposition of the mathematics underpinning modern machine learning. Highly recommended for anyone wanting a one-stop-shop to acquire a deep understanding of machine learning foundations.' Pieter Abbeel, University of California, Berkeley'Really successful are the numerous explanatory illustrations, which help to explain even difficult concepts in a catchy way. Each chapter concludes with many instructive exercises. An outstanding feature of this book is the additional material presented on the website …' Volker H. Schulz, SIAM ReviewTable of Contents1. Introduction and motivation; 2. Linear algebra; 3. Analytic geometry; 4. Matrix decompositions; 5. Vector calculus; 6. Probability and distribution; 7. Optimization; 8. When models meet data; 9. Linear regression; 10. Dimensionality reduction with principal component analysis; 11. Density estimation with Gaussian mixture models; 12. Classification with support vector machines.

Read more less

Book SynopsisMathematical Statistics: Basic Ideas and Selected Topics, Volume I, Second Edition presents fundamental, classical statistical concepts at the doctorate level. It covers estimation, prediction, testing, confidence sets, Bayesian analysis, and the general approach of decision theory. This edition gives careful proofs of major results and explains how the theory sheds light on the properties of practical methods. The book first discusses non- and semiparametric models before covering parameters and parametric models. It then offers a detailed treatment of maximum likelihood estimates (MLEs) and examines the theory of testing and confidence regions, including optimality theory for estimation and elementary robustness considerations. It next presents basic asymptotic approximations with one-dimensional parameter models as examples. The book also describes inference in multivariate (multiparameter) models, exploring asymptotic normality and optimality of MLTrade Review "These methods are clearly explained by two outstanding statistical practitioners. … This book is well supported by the references, increasing its value as a guide through the often difficult world of mathematical statistics. …the authors consider key topics which include asymptotic efficiency in semiparametric models, semiparametric maximum likelihood estimation, proportional hazards regression models and Markov chain Monte Carlo methods."— Receptos Pharmaceuticals, San Diego, 2016"These methods are clearly explained by two outstanding statistical practitioners. … This book is well supported by the references, increasing its value as a guide through the often difficult world of mathematical statistics. …the authors consider key topics which include asymptotic efficiency in semiparametric models, semiparametric maximum likelihood estimation, proportional hazards regression models and Markov chain Monte Carlo methods."— Receptos Pharmaceuticals, San Diego, 2016Table of ContentsSTATISTICAL MODELS, GOALS, AND PERFORMANCE CRITERIA. METHODS OF ESTIMATION. MEASURES OF PERFORMANCE. TESTING AND CONFIDENCE REGIONS. ASYMPTOTIC APPROXIMATIONS. INFERENCE IN THE MULTIPARAMETER CASE. APPENDICES. INDEX.

Read more less

Book SynopsisThe twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and influence. ''Data science'' and ''machine learning'' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? How does it all fit together? Now in paperback and fortified with exercises, this book delivers a concentrated course in modern statistical thinking. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov Chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. Each chapter ends with class-tested exercises, and the book concludes with speculation on the future direction of statistics and data science.Table of ContentsPart I. Classic Statistical Inference: 1. Algorithms and inference; 2. Frequentist inference; 3. Bayesian inference; 4. Fisherian inference and maximum likelihood estimation; 5. Parametric models and exponential families; Part II. Early Computer-Age Methods: 6. Empirical Bayes; 7. James–Stein estimation and ridge regression; 8. Generalized linear models and regression trees; 9. Survival analysis and the EM algorithm; 10. The jackknife and the bootstrap; 11. Bootstrap confidence intervals; 12. Cross-validation and Cp estimates of prediction error; 13. Objective Bayes inference and Markov chain Monte Carlo; 14. Statistical inference and methodology in the postwar era; Part III. Twenty-First-Century Topics: 15. Large-scale hypothesis testing and false-discovery rates; 16. Sparse modeling and the lasso; 17. Random forests and boosting; 18. Neural networks and deep learning; 19. Support-vector machines and kernel methods; 20. Inference after model selection; 21. Empirical Bayes estimation strategies; Epilogue; References; Author Index; Subject Index.

Read more less

Book SynopsisAlan C. Acock's A Gentle Introduction to Stata, Revised Sixth Edition is aimed at new Stata users who want to become proficient in Stata. After reading this introductory text, new users will be able to not only use Stata well but also learn new aspects of Stata.Acock assumes that the user is not familiar with any statistical software. This assumption of a blank slate is central to the structure and contents of the book. Acock starts with the basics; for example, the part of the book that deals with data management begins with a careful and detailed example of turning survey data on paper into a Stata-ready dataset. When explaining how to go about basic exploratory statistical procedures, Acock includes notes that will help the reader develop good work habits. This mixture of explaining good Stata habits and explaining good statistical habits continues throughout the book.Acock is quite careful to teach the reader all aspects of using Stata. He covers data management, good work habits (including the use of basic do-files), basic exploratory statistics (including graphical displays), and analyses using the standard array of basic statistical tools (correlation, linear and logistic regression, and parametric and nonparametric tests of location and dispersion). He also successfully introduces some more advanced topics such as multiple imputation and multilevel modeling in a very approachable manner. Acock teaches Stata commands by using the menus and dialog boxes while still stressing the value of Stata commands and do-files. In this way, he ensures that all types of users can build good work habits. Each chapter has exercises that the motivated reader can use to reinforce the material.The tone of the book is friendly and conversational without ever being glib or condescending. Important asides and notes about terminology are set off in boxes, which makes the text easy to read without any convoluted twists or forward referencing. Rather than splitting topics by their Stata implementation, Acock arranges the topics as they would appear in a basic statistics textbook; graphics and postestimation are woven into the material naturally. Real datasets, such as the General Social Surveys from 2002, 2006, and 2016, are used throughout the book.The focus of the book is especially helpful for those in the behavioral and social sciences because the presentation of basic statistical modeling is supplemented with discussions of effect sizes and standardized coefficients. Various selection criteria, such as semipartial correlations, are discussed for model selection. Acock also covers a variety of commands available for evaluating reliability and validity of measurements.The revised sixth edition is fully up to date for Stata 17, including updated discussion and images of Stata's interface and modern command syntax. In addition, examples include new features such as the table command and collect suite for creating and exporting customized tables as well as the option for creating graphs with transparency.Table of ContentsGetting started Entering data Preparing data for analysis Working with commands, do-files, and results Descriptive statistics and graphs for one variable Statistics and graphs for two categorical variables Tests for one or two means Bivariate correlation and regression Analysis of variance Multiple regression Logistic regression Measurement, reliability, and validity Structural equation and generalized structural equation modeling Working with missing values—multiple imputation An introduction to multilevel analysis Item response theory (IRT) What’s next? Glossary of acronyms Glossary of mathematical and statistical symbols References

Read more less

Book SynopsisTable of ContentsIntroduction 1 About This Book 1 About the Fourth Edition 2 Foolish Assumptions 2 Icons Used in This Book 3 Beyond the Book 3 Where to Go from Here 3 Part 1: Getting Started with SPSS 5 Chapter 1: Introducing SPSS 7 SPSS’s Job, Our Job, and Your Job 7 SPSS’s job 8 Our job 8 Your job 9 Garbage In, Garbage Out: Recognizing the Importance of Good Data 9 Talking to SPSS: Can You Hear Me Now? 12 The graphical user interface 12 Syntax 12 Programmability 13 How SPSS works 13 Getting Help When You Need It 15 Chapter 2: Finding the Best SPSS for You 17 Campus Editions 19 Subscription Plans 20 Commercial Editions 22 What’s New in Version 27 24 Chapter 3: Getting to Know SPSS by Running a Simple Session 25 Opening a Dataset 25 Running an Analysis 27 Interpreting Results 30 Creating Graphs 33 Investigating Data 37 Part 2: Getting Data into and out of SPSS 43 Chapter 4: Understanding SPSS Data: Defining Metadata 45 Entering Variable Definitions on the Variable View Tab 46 Name 47 Type 47 Width 51 Decimals 52 Label 52 Values 53 Missing 54 Columns 55 Align 55 Measure 55 Role 56 Entering and Viewing Data Items on the Data View Tab 56 Chapter 5: Opening Data Files 59 Getting Acquainted with the SPSS File Format 59 Reading Simple Data from a Text File 60 Transferring Data from Another Program 65 Reading an Excel file 67 Reading from an unknown program type 68 Saving Data 69 Chapter 6: Getting Data and Results from SPSS 71 Exporting Data to Another Program 71 Navigating SPSS Statistics Viewer 72 Moving SPSS Output to Other Applications 74 Copying and pasting output 74 Exporting output 75 Printing Data 80 Chapter 7: More about Defining Your Data 81 Working with Dates and Times 82 Using the Date and Time Wizard 84 Creating and Using a Multiple-Response Set 86 Copying Data Properties 90 Part 3: Messing with Data in SPSS 95 Chapter 8: The Transform and Data Menus 97 Sorting Cases 97 Selecting the Data You Want to Look At 100 Splitting Data for Easier Analysis 103 Counting Case Occurrences 104 Recoding Variables 107 Recoding into different variables 107 Automatic recoding 110 Binning 113 Optimal Binning 117 Chapter 9: Computing New Variables 119 Calculating a New Variable with a Formula 120 Calculating a New Variable with a Condition 122 Using System Variables 124 Contrasting $Sysmis with SYSMIS 125 Understanding Missing Data in Formulas 127 Efficiently Calculating with Multiple Formulas 129 Chapter 10: Some Useful Functions 133 The LENGTH Function 134 The ANY Function 137 The MEAN Function and Missing Data 139 RND, TRUNC, and MOD 141 Logicals, the MISSING Function, and the NOT Function 143 String Parsing and Nesting Functions 144 Calculating Lags 146 Chapter 11: Combining Files 147 Merging Files by Adding Cases 147 Merging Files by Adding Variables 152 Part 4: Graphing Data 161 Chapter 12: On the Menu: Graphing Choices in SPSS 163 Building Graphs the Chart Builder Way 164 The Gallery tab 164 The Basic Elements tab 168 The Groups/Point ID tab 169 The Titles/Footnotes tab 170 The Element Properties tab 170 The Chart Appearance tab 176 The Options tab 177 Building Graphs with Graphboard Template Chooser 178 Chapter 13: Building Graphs Using Chart Builder 183 Simple Graphs 184 Simple scatterplots 184 Simple dot plots 185 Simple bar graphs 186 Simple error bars 187 Simple histograms 189 Population pyramids 191 Stacked area charts 192 Fancy Graphs 194 Charts with multiple lines 194 Colored scatterplots 196 Scatterplot matrices 198 Stacked bar charts 199 Pie charts 200 Clustered range bar graphs 202 Differenced area graphs 202 Dual-axis graph 204 Fancy Maps Using Graphboard Template Chooser 205 Heat map 206 Choropleth of values 206 Coordinates on a reference map 209 Part 5: Analyzing Data 211 Chapter 14: Using Descriptive Statistics 213 Looking at Levels of Measurement 213 Defining the four levels of measurement 214 Defining summary statistics 215 Focusing on Frequencies for Categorical Variables 217 Understanding Frequencies for Continuous Variables 221 Summarizing Continuous Variables with the Descriptives Procedure 224 Chapter 15: Knowing When Not to Trust Your Data 227 Sampling 227 Understanding Sample Size 228 Testing Hypotheses 229 Calculating Confidence Intervals 231 Conducting In-Depth Hypothesis Testing 232 Using the Normal Distribution 235 Working with Z-Scores 236 Chapter 16: Testing One Group 239 Conducting Inferential Tests 239 Running the Chi-Square Goodness of Fit Test 240 Running the One-Sample T-Test Procedure 246 Chapter 17: Showing Relationships between Categorical Variables 251 Running the Crosstabs Procedure 252 Running the Chi-Square Test of Independence 256 Comparing Column Proportions 260 Adding Control Variables 261 Creating a Clustered Bar Chart 264 Chapter 18: Showing Relationships between Continuous Dependent and Categorical Independent Variables 267 Conducting Inferential Tests 268 Using the Compare Means Dialog 268 Running the Independent-Samples T-Test Procedure 269 Comparing the Means Graphically 275 Running the Summary Independent-Samples T-Test Procedure 277 Running the Paired-Samples T-Test Procedure 280 Chapter 19: Showing Relationships between Continuous Variables 285 Viewing Relationships 286 Running the Bivariate Procedure 288 Running the Simple Linear Regression Procedure 292 Part 6: Getting More Advanced with Analyzing Data 301 Chapter 20: Doing More Advanced Analyses 303 Running the One-Way ANOVA Procedure 303 Conducting Post Hoc Tests 311 Comparing Means Graphically 314 Running the Multiple Linear Regression Procedure 315 Viewing Relationships 325 Chapter 21: What Is Normal Anyway? 327 Understanding Nonparametric Tests 328 Understanding Distributions 328 Running a Nonparametric Independent Samples Test 331 Running a Nonparametric Related Samples Test 338 Chapter 22: When to Do What 345 Determining Which Statistical Test to Perform 346 Using Advanced Techniques 350 Part 7: Making SPSS Your Own 351 Chapter 23: Changing Settings 353 General Options 354 Language Options 356 Viewer Options 357 Data Options 358 Currency Options 360 Output Options 361 Chart Options 362 Pivot Tables Options 364 File Locations Options 365 Scripts Options 366 Multiple Imputations Options 368 Syntax Editor Options 369 Privacy Options 370 Chapter 24: Editing Charts and Chart Templates 371 Changing and Editing Axes 372 Changing the axis range 372 Scaling the axis range 373 Changing Style: Lines and Symbols 376 Editing chart lines 376 Editing data points 378 Applying Templates 380 Chapter 25: Editing Tables 383 Working with TableLooks 384 Style Output 387 Pivoting Trays 390 Part 8: Programming SPSS with Command Syntax 393 Chapter 26: Getting Acquainted with Syntax 395 Pasting 396 Performing a Series of Related Compute Statements 399 Labeling 400 Repeatedly Generating the Same Report 400 Chapter 27: Adding Syntax to Your Toolkit 403 Your Wish Is My Command 404 Understanding Keywords 405 Declaring Data 406 Commenting Your Way to Clarity 407 Running Your Code 408 Controlling Flow and Executing Conditionals 410 IF 410 DO IF 411 SELECT IF 412 Part 9: The Part of Tens 413 Chapter 28: Ten (or So) Modules You Can Add to SPSS 415 The Advanced Statistics Module 416 The Custom Tables Module 416 The Regression Module 418 The Categories Module 418 The Data Preparation Module 419 The Decision Trees Module 419 The Forecasting Module 420 The Missing Values Module 421 The Bootstrapping Module 421 The Complex Samples Module 422 The Conjoint Module 422 The Direct Marketing Module 422 The Exact Tests Module 423 The Neural Networks Module 424 Chapter 29: Ten Useful SPSS Resources 425 Supporting Websites for This Book 425 LinkedIn and LinkedIn Groups 426 IBM SPSS Statistics Certification 427 IBM Data Science Community 427 SPSSX-L 427 Online Videos 428 Twitter 429 Live Instruction 430 Asynchronous Instruction and Tutorials 431 SPSS Statistics for Data Analysis and Visualization 432 Chapter 30: Ten SPSS Statistics Gotchas 433 Failing to Declare Level of Measurement 433 Conflating String Values with Labels 434 Failing to Declare Missing Data 435 Failing to Find Add-on Modules and Plug-Ins 435 Failing to Meet Statistical and Software Assumptions 437 Confusing Pasting Syntax with Copy and Paste 438 Thinking You Create Variables in SPSS as You Do in Excel 438 Getting Confused by Listwise Deletion 439 Losing Track of Your Active Dataset 440 Forgetting to Turn Off Select and Split and Weight 441 Index 443

Read more less

Book SynopsisTrade Review"Stimulating reading" --New ScientistTable of ContentsFundamental principles of theoretical physics; The Gibbs distribution; Ideal gases; Solids; Non-ideal gases; Solutions; Chemical reactions; Fluctuations; Surfaces.

Read more less

Book SynopsisTHE CLASSIC GUIDE, NOW FULLY REVISED AND UPDATEDWhy do we need Statistics?What do terms like ''dispersion'', ''correlation'', ''normal distribution'' and ''significance'' actually mean?How can I learn how to think statistically?This bestselling introduction is for anyone who wants to know how statistics works and the powerful ideas behind it. Teaching through words and diagrams instead of requiring you to do complex calculations, it assumes no expert knowledge and makes the subject accessible even to readers who consider themselves non-mathematical. This clear and informative ''tutorial in print'' includes questions for you to respond to in the light of what you have read so far, ensuring your developing ability to think statistically.

Read more less

Book SynopsisRandom matrix theory, both as an application and as a theory, has evolved rapidly over the years. This title chronicles these developments, emphasizing log-gases as a physical picture. It covers topics such as beta ensembles and Jack polynomials. It develops the application and theory of Gaussian and circular ensembles of random matrix theory.Trade Review"Log-Gases and Random Matrices is an excellent book. It is bound to become an instant classic and the standard reference to a large body of contemporary random matrix theory. It is a well-written tour through a vast landscape. The contemporary literature is extensively referenced and incorporated in the text, and the material is presented from several perspectives. Forrester has achieved the pedagogical equivalent of Dyson's 'Threefold Way' by writing an advanced monograph appealing equally to physicists, mathematicians, and statisticians."--Steven Joel Miller and Eduardo Duenez, Mathematical ReviewsTable of Contents*FrontMatter, pg. i*Preface, pg. v*Contents, pg. xi*Chapter One. Gaussian Matrix Ensembles, pg. 1*Chapter Two. Circular Ensembles, pg. 53*Chapter Three. Laguerre And Jacobi Ensembles, pg. 85*Chapter Four. The Selberg Integral, pg. 133*Chapter Five. Correlation functions at ss = 2, pg. 186*Chapter Six. Correlation Functions At ss= 1 And 4, pg. 236*Chapter Seven. Scaled limits at ss = 1, 2 and 4, pg. 283*Chapter Eight. Eigenvalue probabilities - Painleve systems approach, pg. 328*Chapter Nine. Eigenvalue probabilities- Fredholm determinant approach, pg. 380*Chapter Ten. Lattice paths and growth models, pg. 440*Chapter Eleven. The Calogero-Sutherland model, pg. 505*Chapter Twelve. Jack polynomials, pg. 543*Chapter Thirteen. Correlations for general ss, pg. 592*Chapter Fourteen. Fluctuation formulas and universal behavior of correlations, pg. 658*Chapter Fifteen. The two-dimensional one-component plasma, pg. 701*Bibliography, pg. 765*Index, pg. 785

Read more less

Book SynopsisThis book builds theoretical statistics from the first principles of probability theory. Starting from the basics of probability, the authors develop the theory of statistical inference using techniques, definitions, and concepts that are statistical and are natural extensions and consequences of previous concepts. Intended for first-year graduate students, this book can be used for students majoring in statistics who have a solid mathematics background. It can also be used in a way that stresses the more practical uses of statistical theory, being more concerned with understanding basic statistical concepts and deriving reasonable statistical procedures for a variety of situations, and less concerned with formal optimality investigations.Table of Contents1. Probability Theory. Set Theory. Probability Theory. Conditional Probability and Independence. Random Variables. Distribution Functions. Density and Mass Functions. Exercises. Miscellanea. 2. Transformations and Expectations. Distribution of Functions of a Random Variable. Expected Values. Moments and Moment Generating Functions. Differentiating Under an Integral Sign. Exercises. Miscellanea. 3. Common Families of Distributions. Introductions. Discrete Distributions. Continuous Distributions. Exponential Families. Locations and Scale Families. Inequalities and Identities. Exercises. Miscellanea. 4. Multiple Random Variables. Joint and Marginal Distributions. Conditional Distributions and Independence. Bivariate Transformations. Hierarchical Models and Mixture Distributions. Covariance and Correlation. Multivariate Distributions. Inequalities. Exercises. Miscellanea. 5. Properties of a Random Sample. Basic Concepts of Random Samples. Sums of Random Variables from a Random Sample. Sampling for the Normal Distribution. Order Statistics. Convergence Concepts. Generating a Random Sample. Exercises. Miscellanea. 6. Principles of Data Reduction. Introduction. The Sufficiency Principle. The Likelihood Principle. The Equivariance Principle. Exercises. Miscellanea. 7. Point Estimation. Introduction. Methods of Finding Estimators. Methods of Evaluating Estimators. Exercises. Miscellanea. 8. Hypothesis Testing. Introduction. Methods of Finding Tests. Methods of Evaluating Test. Exercises. Miscellanea. 9. Interval Estimation. Introduction. Methods of Finding Interval Estimators. Methods of Evaluating Interval Estimators. Exercises. Miscellanea. 10. Asymptotic Evaluations. Point Estimation. Robustness. Hypothesis Testing. Interval Estimation. Exercises. Miscellanea. 11. Analysis of Variance and Regression. Introduction. One-way Analysis of Variance. Simple Linear Regression. Exercises. Miscellanea. 12. Regression Models. Introduction. Regression with Errors in Variables. Logistic Regression. Robust Regression. Exercises. Miscellanea. Appendix. Computer Algebra. References.

Read more less

Book SynopsisPraise for the First Edition An indispensable addition to any serious collection on lifetime data analysis and . . . a valuable contribution to the statistical literature. Highly recommended . . . -Choice This is an important book, which will appeal to statisticians working on survival analysis problems. -Biometrics A thorough, unified treatment of statistical models and methods used in the analysis of lifetime data . . . this is a highly competent and agreeable statistical textbook. -Statistics in Medicine The statistical analysis of lifetime or response time data is a key tool in engineering, medicine, and many other scientific and technological areas. This book provides a unified treatment of the models and statistical methods used to analyze lifetime data. Equally useful as a reference for individuals interested in the analysis of lifetime data and as a text for advanced students, Statistical Models and Methods for Lifetime Data, SecoTrade Review“...a welcome addition to the literature on survival analysis...for a unified and thorough reference of classical theory and models, this book is an excellent choice.” (Journal of the American Statistical Association, March 2004) "This book is a role-model for other who are planning to write books…every statistician and applied researcher ought to have this book in their collection." (Journal of Statistical Computation and Simulation, October 2003) "...expanded and updated with recent research...a valuable reference...this book...merits a place on the bookshelf of anyone concerned with the analysis of lifetime data from any field. (Technometrics, Vol. 45, No. 3, August 2003) "...updated version of the popular text...this excellent book will serve as either a reference or a graduate-level textbook." (Short Book Reviews, Vol. 23, No. 2, August 2003) "...excellent...provides a wealth of information for those familiar with the area." (Pharmaceutical Research, Vol. 20, No. 9, September 2003) "...the author's aim is to cover lifetime data analysis without concentrating exclusively on any field of applications...he succeeds quite well..." (Zentralblatt Math, 2003) “...rewritten to reflect new developments...” (Quarterly of Applied Mathematics, Vol. LXI, No. 2, June 2003) "Compared with the large number of other good textbooks in the this field, this is one of the best. I highly recommend that all applied statisticians add this volume to their libraries." (Applied Clinical Trials, May 2003)Table of ContentsBasic Concepts and Models. Observation Schemes, Censoring and Likelihood. Some Nonparametric and Graphical Procedures. Inference Procedures for Parametric Models. Inference procedures for Log-Location-Scale Distributions. Parametric Regression Models. Semiparametric Multiplicative Hazards Regression Models. Rank-Type and Other Semiparametric Procedures for Log-Location-Scale Models. Multiple Modes of Failure. Goodness of Fit Tests. Beyond Univariate Survival Analysis. Appendix A. Glossary of Notation and Abbreviations. Appendix B. Asymptotic Variance Formulas, Gamma Functions and Order Statistics. Appendix C. Large Sample Theory for Likelihood and Estimating Function Methods. Appendix D. Computational Methods and Simulation. Appendix E. Inference in Location-Scale Parameter Models. Appendix F. Martingales and Counting Processes. Appendix G. Data Sets. References.

Read more less

Book SynopsisOtt and Longnecker's AN INTRODUCTION TO STATISTICAL METHODS AND DATA ANALYSIS, Seventh Edition, provides a broad overview of statistical methods for advanced undergraduate and graduate students from a variety of disciplines who have little or no prior course work in statistics. The authors teach students to solve problems encountered in research projects, to make decisions based on data in general settings both within and beyond the university setting, and to become critical readers of statistical analyses in research papers and news reports. The first eleven chapters present material typically covered in an introductory statistics course, as well as case studies and examples that are often encountered in undergraduate capstone courses. The remaining chapters cover regression modeling and design of experiments.Table of ContentsPART 1: INTRODUCTION. 1. Statistics and the Scientific Method. Introduction. Why Study Statistics? Some Current Applications of Statistics. A Note to the Student. Summary. Exercises. PART 2: COLLECTING DATA. 2. Using Surveys and Scientific Studies to Collect Data. Introduction and Abstract of Research Study. Observational Studies. Sampling Designs for Surveys. Experimental Studies. Designs for Experimental Studies. Research Study: Exit Polls versus Election Results. Summary. Exercises. PART 3: SUMMARIZING DATA. 3. Data Description. Introduction and Abstract of Research Study. Calculators, Computers, and Software Systems. Describing Data on a Single Variable: Graphical Methods. Describing Data on a Single Variable: Measures of Central Tendency. Describing Data on a Single Variable: Measures of Variability. The Boxplot. Summarizing Data from More Than One Variable: Graphs and Correlation. Research Study: Controlling for Student Background in the Assessment of Teaching. Summary and Key Formulas. Exercises. 4. Probability And Probability Distributions. Introduction and Abstract of Research Study. Finding the Probability of an Event. Basic Event Relations and Probability Laws. Conditional Probability and Independence. Bayes' Formula. Variables: Discrete and Continuous. Probability Distributions for Discrete Random Variables. Two Discrete Random Variables: The Binomial and the Poisson. Probability Distributions for Continuous Random Variables. A Continuous Probability Distribution: The Normal Distribution. Random Sampling. Sampling Distributions. Normal Approximation to the Binomial. Evaluating Whether or Not a Population Distribution Is Normal. Research Study: Inferences about Performance Enhancing Drugs among Athletes. Minitab Instructions. Summary and Key Formulas. Exercises. PART 4: ANALYZING DATA, INTERPRETING THE ANALYSES, AND COMMUNICATING RESULTS. 5. Inferences about Population Central Values. Introduction and Abstract of a Research Study. Estimation of ��. Choosing the Sample Size for Estimating ��. A Statistical Test for ��. Choosing the Sample Size for ��. The Level of Significance of a Statistical Test. Inferences about �� for a Normal Population, �� Unknown. Inferences about �� when Population in Nonnormal and n is small: Bootstrap Methods. Inferences about the Median. Research Study: Percent Calories from Fat. Summary and Key Formulas. Exercises. 6. Inferences Comparing Two Population Central Values. Introduction and Abstract of a Research Study. Inferences about ��1 ��� ��2: Independent Samples. A Nonparametric Alternative: The Wilcoxon Rank Sum Test. Inferences about ��1 ��� ��2: Paired Data. A Nonparametric Alternative: The Wilcoxon Signed-Rank Test. Choosing Sample Sizes for Inferences about ��1 ��� ��2. Research Study: Effects of Oil Spill on Plant Growth. Summary. Exercises. 7. Inferences about Population Variances. Introduction and Abstract of a Research Study. Estimation and Tests for a Population Variance. Estimation and Tests for Comparing Two Population Variances. Tests for Comparing t > 2 Population Variances. Research Study: Evaluation of Methods for Detecting E. coli. Summary and Key Formulas. Exercises. 8. Inferences About More Than Two Population Central Values Introduction and Abstract of a Research Study. A Statistical Test About More Than Two Population Means: An Analysis of Variance. The Model for Observations in a Completely Randomized Design. Checking on the AOV Conditions. An Alternative Analysis: Transformations of the Data. A Nonparametric Alternative: The Kruskal-Wallis Test. Research Study: Effect on Timing on the Treatment of Port-Wine Stains with Lasers. Summary and Key Formulas. Exercises. 9. Multiple Comparisons. Introduction and Abstract of Research Study. Linear Contrasts. Which Error Rate Is Controlled? Fisher's Least Significant Difference. Tukey's W Procedure. Student-Neuman-Keuls Procedure. Dunnett's Procedure: Comparison of Treatments to a Control. Scheff��'s S Method. A Nonparametric Multiple-Comparison Procedure. Research Study: Are Interviewers' Decisions Affected by Different Handicap Types? Summary and Key Formulas. Exercises. 10. Categorical Data. Introduction and Abstract of Research Study. Inferences about a Population Proportion ���. Inferences about the Difference between Two Population Proportions, ���1 ��� ���2. Inferences about Several Proportions: Chi-Square Goodness-of-Fit Test. Tests for Independence and Homogeneity. Measuring Strength of Relaxation. Odds and Odd Ratios. Combining Sets of 2 ��� 2 Contingency Tables (optional). Research Study: Does Gender Bias Exist in the Selection of Students for Vocational Education? Summary and Key Formulas. Exercises. PART 5: ANALYZING DATA: REGRESSION METHODS AND MODEL BUILDING. 11. Linear Regression and Correlation. Introduction and Abstract of Research Study. Estimating Model Parameters. Inferences about Regression Parameters. Predicting New y Values Using Regression. Examining Lack of Fit in Linear Regression. The Inverse Regression Problem (Calibration). Correlation. Research Study: Two Methods for Detecting E. coli. Summary and Key Formulas. Exercises. 12. Multiple Regression and the General Linear Model. Introduction and Abstract of Research Study. The General Linear Model. Estimating Multiple Regression Coefficients. Inferences in Multiple Regression. Testing a Subset of Regression Coefficients. Forecasting Using Multiple Regression. Comparing the Slopes of Several Regression Lines. Logistic Regression. Some Multiple Regression Theory (Optional). Research Study: Designing an Electric Drill. Summary and Key Formulas. Exercises. 13. Further Regression Topics. Introduction and Abstract of Research Study. Selecting the Variables (Step 1). Formulating the Model (Step 2). Checking Model Assumptions (Step 3). Research Study: Construction Costs for Nuclear Power Plants. Summary and Key Formulas. Exercises. PART 6: DESIGN OF EXPERIMENTS AND ANALYSIS OF VARIANCE. 14. Analysis of Variance for Completely Randomized Designs. Introduction and Abstract of Research Study. Completely Randomized Design with Single Factor. Factorial Treatment Structure. Factorial Treatment Structures with an Unequal Number of Replications. Estimation of Treatment Differences and Comparisons of Treatment Means. Determining the Number of Replications. Research Study: Development of a Low-Fat Processed Meat. Summary and Key Formulas. Exercises. 15. Analysis of Variance for Blocked Designs. Introduction and Abstract of Research Study. Randomized Complete Block Design. Latin Square Design. Factorial Treatment Structure in a Randomized Complete Block Design. A Nonparametric Alternative���Friedman's Test. Research Study: Control of Leatherjackets. Summary and Key Formulas. Exercises. 16. Analysis of Covariance. Introduction and Abstract of Research Study. A Completely Randomized Design with One Covariate. The Extrapolation Problem. Multiple Covariates and More Complicated Designs. Research Study: Evaluations of Cool-Season Grasses for Putting Greens. Summary. Exercises. 17. Analysis of Variance for Some Fixed-, Random-, and Mixed-Effects Models. Introduction and Abstract of Research Study. A One-Factor Experiment with Random Treatment Effects. Extensions of Random-Effects Models. Mixed-Effects Models. Rules for Obtaining Expecting Mean Squares. Nested Factors. Research Study: Factors Affecting Pressure Drops Across Expansion Joints . Summary. Exercises. 18. Split-Plot, Repeated Measures, and Crossover Designs. Introduction and Abstract of Research Study. Split-Plot Designs. Single-Factor Experiments with Repeated Measures on One of the Factors. Two-Factor Experiments with Repeated Measures on One of the Factors. Crossover Design. Research Study: Effects of Oil Spill on Plant Growth. Summary. Exercises. 19. Analysis of Variance for Some Unbalanced Designs. Introduction and Abstract of Research Study. A Randomized Block Design with One or More Missing Observations. A Latin Square Design with Missing Data. Balanced Incomplete Block (BIB) Designs. Research Study: Evaluation of the Consistency of Property Assessment. Summary and Key Formulas. Exercises. PART 7: COMMUNICATING AND DOCUMENTING THE RESULTS OF ANALYSES 20. Communicating and Documenting the Results of a Study or Experiment. Introduction. The Difficulty of Good Communication. Communication Hurdles: Graphical Distortions. Communication Hurdles: Biased Samples. Communication Hurdles: Sample Size. The Statistical Report. Documentation and Storage of Results. Summary. Exercises.

Read more less

Book SynopsisThis classic on games and how to play them intelligently is being re-issued in a new, four volume edition. This book has laid the foundation to a mathematical approach to playing games. The wise authors wield witty words, which wangle wonderfully winning ways. In Volume 1, the authors do the Spade Work, presenting theories and techniques to "dissect" games of varied structures and formats in order to develop winning strategies.Trade Review" ""Winning Ways is an absolute must have for those who are interested in mathematical game theory. It is sure to please any fan of recreational mathematics or simply anyone who is interested in games and how to play them well."" -Jacob McMillen, Math Horizons, November 2005 ""This new edition confirms the status of the book as a standard reference, which it will continue to be for at least another decade."" -Adhemar Bultheel, Bulletin of the Belgian Mathematical Society , December 2005"Table of ContentsPreface to Second Edition, Preface, Spade-Work!, 1. WhoseGame?, 2. Finding the Correct Number is Simplicity Itself, 3. Some Harder Games and How to Make Them Easier, 4. Taking and Breaking, 5. Numbers, Nimbers and Numberless Wonders, 6. The Heat of Battle, 7. Hackenbush, 8. It’s a Small Small Small Small World, Index

Read more less

Book SynopsisThis textbook provides the foundation for a course that takes PhD students in empirical accounting research from the very basics of statistics, data analysis, and causal inference up to the point at which they conduct their own research. Starting with foundations in statistics, econometrics, causal inference, and institutional knowledge of accounting and finance, the book moves on to an in-depth coverage of the core papers in capital market research. The latter half of the book examines contemporary approaches to research design and empirical analysis, including natural experiments, instrumental variables, fixed effects, difference-in-differences, regression discontinuity design, propensity-score matching, and machine learning. Readers of the book will develop deep data analysis skills using modern tools. Extensive replication and simulation analysis is included throughout.Key Features: Extensive coverage of empirical accounting research over more than 50 years. Integrated coverage of statistics and econometrics, institutional knowledge, and research design. Numerous replications and a dozen simulation analyses to immerse readers in papers and empirical analysis. All tables and figures in the book can be reproduced by readers using included code. Easy-to-use templates facilitate hands-on exercises and introduce reproduceable research concepts. (Solutions available to instructors.)

Read more less

Book SynopsisRenowned for its clear prose and no-nonsense emphasis on core concepts, Statistics covers fundamentals using real examples to illustrate the techniques.Table of ContentsPART I. DESIGNS OF EXPERIMENTS Chapter 1. Controlled Experiments Chapter 2. Observational Studies PART II. DESCRIPTIVE STATISTICS Chapter 3. The Histogram Chapter 4. The Average Standard Deviation Chapter 5. The Normal Approximation for Data Chapter 6. Measurement Error Chapter 7. Plotting Points and Lines PART III. CORRELATION AND REGRESSION Chapter 8. Correlation Chapter 9. More about Correlation Chapter 10. Regression Chapter 11. The R.M.S. Error for Regression Chapter 12. The Regression Line PART IV. PROBABILITY Chapter 13. What Are the Chances Chapter 14. More about Chance Chapter 15. The Binomial Formula PART V. CHANCE VARIABILITY Chapter 16. The Law of Averages Chapter 17. The Expected Value and Standard Error Chapter 18. The Normal Approximation for Probablity PART VI. SAMPLING Chapter 19. Sample Surveys Chapter 20. Chance Errors in Sampling Chapter 21. The Accuracy of Percentages Chapter 22. Measuring Employment and Significance Unemployment Chapter 23. The Accuracy of Averages PART VII. CHANCE MODELS Chapter 24. Model for Measurement Error Chapter 25. Chance Models in Genetics PART VIII. TESTS OF SIGNIFICANCE Chapter 26. Tests of Significance Chapter 27 More Tests for Averages Chapter 28. The Chi-Square Test Chapter 29. A Closer Look at Tests of Significance

Read more less

Book SynopsisThis book is a guide to the practical application of statistics in data analysis as typically encountered in the physical sciences. It is primarily addressed at students and professionals who need to draw quantitative conclusions from experimental data. Although most of the examples are taken from particle physics, the material is presented in a sufficiently general way as to be useful to people from most branches of the physical sciences. The first part of the book describes the basic tools of data analysis: concepts of probability and random variables, Monte Carlo techniques, statistical tests, and methods of parameter estimation. The last three chapters are somewhat more specialized than those preceding, covering interval estimation, characteristic functions, and the problem of correcting distributions for the effects of measurement errors (unfolding).Trade Review"Glen Cowan is a particle physicist who seems to have got everything right. Results are stated clearly, without mathematical proof but with enough explanation to satisfy the physicist's need to understand not only how, but also why...Those teaching an advanced undergraduate or graduate course in statistics or physicists will find this a good textbook...Do not be fooled by the fact that it does not have the "textbook look" - the exercises have been made available separately on a Web site. " CERN Courier"The material presented in this book is dense.In less than two hundred pages, it takes the reader from the basic notions of probability, through neural networks, Monte Carlo methods, and regularization techniques." Short Book ReviewsTable of ContentsPreface ; Notation ; 1. Fundamental Concepts ; 2. Examples of Probability Functions ; 3. The Monte Carlo Method ; 4. Statistical Tests ; 5. General Concepts of Parameter Estimation ; 6. The Method of Maximum Likelihood ; 7. The Method of Least Squares ; 8. The Method of Moments ; 9. Statistical Errors, Confidence Intervals and Limits ; 10. Characteristic Functions and Related Examples ; 11. Unfolding ; Bibliography ; Index

Read more less

Book SynopsisModern statistics is very different from the dry and dusty discipline of the popular imagination. In its place is an exciting subject which uses deep theory and powerful software tools to shed light and enable understanding. And it sheds this light on all aspects of our lives, enabling astronomers to explore the origins of the universe, archaeologists to investigate ancient civilisations, governments to understand how to benefit and improve society, and businesses to learn how best to provide goods and services. Aimed at readers with no prior mathematical knowledge, this Very Short Introduction explores and explains how statistics work, and how we can decipher them. ABOUT THE SERIES: The Very Short Introductions series from Oxford University Press contains hundreds of titles in almost every subject area. These pocket-sized books are the perfect way to get ahead in a new subject quickly. Our expert authors combine facts, analysis, perspective, new ideas, and enthusiasm to make interesting and challenging topics highly readable.Table of ContentsPreface ; 1. Surrounded by Statistics ; 2. Simple descriptions ; 3. Collecting good data ; 4. Probability ; 5. Estimation and inference ; 6. Statistical models and methods ; 7. Statistical computing ; Further reading ; Index

Read more less

Book SynopsisThis practical introduction for final-year undergraduate and graduate students is ideally suited to computer scientists without a background in calculus and linear algebra. Numerous examples and exercises are provided. Additional resources available online and in the comprehensive software package include computer code, demos and teaching materials for instructors.Trade Review'This book is an exciting addition to the literature on machine learning and graphical models. What makes it unique and interesting is that it provides a unified treatment of machine learning and related fields through graphical models, a framework of growing importance and popularity. Another feature of this book lies in its smooth transition from traditional artificial intelligence to modern machine learning. The book is well-written and truly pleasant to read. I believe that it will appeal to students and researchers with or without a solid mathematical background.' Zheng-Hua Tan, Aalborg University, Denmark'With approachable text, examples, exercises, guidelines for teachers, a MATLAB toolbox and an accompanying website, Bayesian Reasoning and Machine Learning by David Barber provides everything needed for your machine learning course. Only students not included.' Jaakko Hollmén, Aalto University'The chapters on graphical models form one of the clearest and most concise presentations I have seen … The exposition throughout uses numerous diagrams and examples, and the book comes with an extensive software toolbox - these will be immensely helpful for students and educators. It's also a great resource for self-study.' Arindam Banerjee, University of Minnesota'I repeatedly get unsolicited comments from my students that the contents of this book have been very valuable in developing their understanding of machine learning … My students praise this book because it is both coherent and practical, and because it makes fewer assumptions regarding the reader's statistical knowledge and confidence than many books in the field.' Amos Storkey, University of EdinburghTable of ContentsPreface; Part I. Inference in Probabilistic Models: 1. Probabilistic reasoning; 2. Basic graph concepts; 3. Belief networks; 4. Graphical models; 5. Efficient inference in trees; 6. The junction tree algorithm; 7. Making decisions; Part II. Learning in Probabilistic Models: 8. Statistics for machine learning; 9. Learning as inference; 10. Naive Bayes; 11. Learning with hidden variables; 12. Bayesian model selection; Part III. Machine Learning: 13. Machine learning concepts; 14. Nearest neighbour classification; 15. Unsupervised linear dimension reduction; 16. Supervised linear dimension reduction; 17. Linear models; 18. Bayesian linear models; 19. Gaussian processes; 20. Mixture models; 21. Latent linear models; 22. Latent ability models; Part IV. Dynamical Models: 23. Discrete-state Markov models; 24. Continuous-state Markov models; 25. Switching linear dynamical systems; 26. Distributed computation; Part V. Approximate Inference: 27. Sampling; 28. Deterministic approximate inference; Appendix. Background mathematics; Bibliography; Index.

Read more less

Book SynopsisPraise for George E.P. Box and An Accidental Statistician I found most interesting the parts describing how he developed as a statistician, the intellectual influences on him, and the genesis of the ideas for which he is so well known...Trade ReviewMentioned in The Economist - 20 December 2014Table of ContentsForeword xi Second Foreword xv Preface xix Acknowledgments xxi From ThePublisher xxiii 1 Early Years 1 ‘‘Who in the world am I? Ah, that’s the great puzzle.’’ 2 Army Life 19 ‘‘Contrarywise, if it was so, it might be: and if it were so, it would be: but as it isn’t, it ain’t. That’s logic.’’ 3 ICI and the Statistical Methods Panel 44 ‘‘Can you answer useful questions?’’ 4 George Barnard 53 ‘‘When I use a word . . . it means just what I choose it to mean–neither more nor less.’’ 5 An Invitation to the United States 63 ‘‘The time has come, ‘the walrus said,’ to talk of many things. Of shoes and ships and sealing wax, of cabbages and kings.’’ 6 Princeton 78 ‘‘Ah! Then yours wasn’t a really good school.’’ 7 A New Life in Madison 94 ‘‘Digging for apples, your honor!’’ 8 Time Series 124 ‘‘What do you know about this business?’’ 9 George Tiao and the Bayes Book 139 ‘‘It gets easier further on.’’ 10 GrowingUp (Helen and Harry) 144 ‘‘There are 364 days when you might get unbirthday presents, and only 1 for birthday presents, you know.’’ 11 Fisher—Father and Son 151 ‘‘I only hope the boat won’t tipple over!’’ 12 Bill Hunter and Some Ideas on Experimental Design 157 ‘‘There goes Bill!’’ 13 The Quality Movement 181 ‘‘The race is over!. . . ‘Everybody has won and all must have prizes.’’’ 14 Adventures with Claire 197 ‘‘What else had you to learn?’’ ‘‘Well, there was Mystery.’’ 15 The Many Sides of Mac 209 ‘‘There’s nothing like eating hay when you’re feeling faint.’’ 16 Life in England 218 ‘‘What matters is how far we go? There is another shore, you know, upon the other side.’’ 17 Journeys to Scandinavia 224 ‘‘What sort of people live here?’’ 18 A Second Home in Spain 228 ‘‘I know something interesting is sure to happen.’’ 19 The Royal Society of London 245 20 Conclusion 247 21 Memories 248 Index 265

Read more less

Book SynopsisSince the first edition of this book published, Bayesian networks have become even more important for applications in a vast array of fields. This second edition includes new material on influence diagrams, learning from data, value of information, cybersecurity, debunking bad statistics, and much more. Focusing on practical real-world problem-solving and model building, as opposed to algorithms and theory, it explains how to incorporate knowledge with data to develop and use (Bayesian) causal models of risk that provide more powerful insights and better decision making than is possible from purely data-driven solutions.Features Provides all tools necessary to build and run realistic Bayesian network models Supplies extensive example models based on real risk assessment problems in a wide range of application domains provided; for example, finance, safety, systems reliability, law, forensics, cybersecurity and more Introduces all necessary

Read more less

Book SynopsisMeta-analysis is a powerful statistical methodology for synthesizing research evidence across independent studies. This is the first comprehensive handbook of meta-analysis written specifically for ecologists and evolutionary biologists, and it provides an invaluable introduction for beginners as well as an up-to-date guide for experienced meta-anaTrade Review"[T]his is a comprehensive and up-to-date compendium of all relevant aspects for meta-analysis conduction in ecology, evolution, and related topics. Scientists from these areas who already have some knowledge on meta-analysis will find valuable guidance."--Daniela Vetter, Quarterly Review of BiologyTable of ContentsPreface xi SECTION I: Introduction & Planning 1.Place of Meta-analysis among Other Methods of Research Synthesis 3 Julia Koricheva & Jessica Gurevitch 2.The Procedure of Meta-analysis in a Nutshell 14 Isabelle M. Cote & Michael D. Jennions SECTION II : Initiating a Meta-analysis 3.First Steps in Beginning a Meta-analysis 27 Gavin B. Stewart, Isabelle M. Cote, Hannah R. Rothstein, & Peter S. Curtis 4.Gathering Data: Searching Literature & Selection Criteria 37 Isabelle M. Cote, Peter S. Curtis, Hannah R. Rothstein, & Gavin B. Stewart 5.Extraction & Critical Appraisal of Data 52 Peter S. Curtis, Kerrie Mengersen, Marc J. Lajeunesse, Hannah R. Rothstein, & Gavin B. Stewart 6.Effect Sizes: Conventional Choices & Calculations 61 Michael S. Rosenberg, Hannah R. Rothstein, & Jessica Gurevitch 7.Using Other Metrics of Effect Size in Meta-analysis 72 Kerrie Mengersen & Jessica Gurevitch SECTION III : Essential Analytic Models & Methods 8.Statistical Models & Approaches to Inference 89 Kerrie Mengersen, Christopher H. Schmid, Michael D. Jennions, & Jessica Gurevitch 9.Moment & Least-Squares Based Approaches to Meta-analytic Inference 108 Michael S. Rosenberg 10.Maximum Likelihood Approaches to Meta-analysis 125 Kerrie Mengersen & Christopher H. Schmid 11.Bayesian Meta-analysis 145 Christopher H. Schmid & Kerrie Mengersen 12.Software for Statistical Meta-analysis 174 Christopher H. Schmid, Gavin B. Stewart, Hannah R. Rothstein, Marc J. Lajeunesse, & Jessica Gurevitch SECTION IV: Statistical Issues & Problems 13.Recovering Missing or Partial Data from Studies: A Survey of Conversions & Imputations for Meta-analysis 195 Marc J. Lajeunesse 14.Publication & Related Biases 207 Michael D. Jennions, Christopher J. Lortie, Michael S. Rosenberg, & Hannah R. Rothstein 15.Temporal Trends in Effect Sizes: Causes, Detection, & Implications 237 Julia Koricheva, Michael D. Jennions, & Joseph Lau 16.Statistical Models for the Meta-analysis of Nonindependent Data 255 Kerrie Mengersen, Michael D. Jennions, & Christopher H. Schmid 17.Phylogenetic Nonindependence & Meta-analysis 284 Marc J. Lajeunesse, Michael S. Rosenberg, & Michael D. Jennions 18.Meta-analysis of Primary Data 300 Kerrie Mengersen, Jessica Gurevitch, & Christopher H. Schmid 19.Meta-analysis of Results from Multisite Studies 313 Jessica Gurevitch SECTION V: Presentation & Interpretation of Results 20.Quality St&ards for Research Syntheses 323 Hannah R. Rothstein, Christopher J. Lortie, Gavin B. Stewart, Julia Koricheva, & Jessica Gurevitch 21.Graphical Presentation of Results 339 Christopher J. Lortie, Joseph Lau, & Marc J. Lajeunesse 22.Power Statistics for Meta-analysis: Tests for Mean Effects & Homogeneity 348 Marc J. Lajeunesse 23.Role of Meta-analysis in Interpreting the Scientific Literature 364 Michael D. Jennions, Christopher J. Lortie, & Julia Koricheva 24.Using Meta-analysis to Test Ecological & Evolutionary Theory 381 Michael D. Jennions, Christopher J. Lortie, & Julia Koricheva SECTION VI: Contributions of Meta-analysis in Ecology & Evolution 25.History & Progress of Meta-analysis 407 Joseph Lau, Hannah R. Rothstein, & Gavin B. Stewart 26.Contributions of Meta-analysis to Conservation & Management 420 Isabelle M. Cote & Gavin B. Stewart 27.Conclusions: Past, Present, & Future of Meta-analysis in Ecology & Evolution 426 Jessica Gurevitch & Julia Koricheva Glossary 433 Frequently Asked Questions 441 References 447 List of Contributors 487 Subject Index 489

Read more less