Probability and statistics Books

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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.

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

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

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Book 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 *

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

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Book 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.

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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 *

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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.

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

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Book 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

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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

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

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Book 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

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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.

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

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Book 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

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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.

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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

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

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Book SynopsisExplains numerous informetric regularities based on a decreasing power law as size-frequency function, i.e. Lotka's law. It revives the historical formulation of Alfred Lotka of 1926 and shows the power of this power law, both in classical aspects of informetrics (libraries, bibliographies) and in 'new' applications such as social networks.Table of ContentsIntroduction. Chapter I. Lotkaian Informetrics: An Introduction. Informetrics. What is Lotkaian informetrics? Why Lotkaian informetrics? Practical Examples of Lotkaian Informetrics. Chapetr II. Basic Theory of Lotkaian Informetrics. General Informetrics Theory. Theory of Lotkaian Informetrics. Extension of the General Informetrics Theory: The Dual Size-Frequency Function H. The Place of the Law of Zipf in Lotkaian Informetrics. Chapter III. Three-dimensional Lotkaian Informetrics. Linear Three-Dimensional Lotkaian Informetrics. Chapter IV. Lotkaian Concentration Theory. Introduction. Discrete Concentration Theory. Continuous Concentration Theory. Concentration Theory of Linear Three-Dimensional Informetrics. Chapter V. Lotkaian Fractal Complexity Theory. Introduction. Elements of Fractal Theory. Interpretation of Lotkaian IPPs as Self-Similar Fractals. Chapter VI. Lotkaian Informetrics of Systems in which Items can have Multiple Sources. Introduction. Crediting Systems and Counting Procedures for Sources and "Super Sources" in IPPs Where Items Can Have Multiple Sources. Construction of Fractional Size-Frequency Functions Based on Two Dual Lotka laws. Chapter VII. Further Applications in Lotkaian Informetrics. Introduction. Explaining "Regularities". Probabilistic Explanation of the Relationship Between Citation Age and Journal Productivity. Chapter VII. General and Lotkaian Theory of the Distribution of Author Ranks in Multi-Authored Papers. The First-Citation Distribution in Lotkaian Informetrics. Zipfian Theory of N-grams and of N-word Phrases: the Cartesian Product of IPPs. Appendix. Appendix I. Appendix II. Appendix III Statistical Determination of the Parameters in the Law of Lotka. Bibliography. Subject Index.

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Book SynopsisTrade Review"Data mining practitioners, here is your bible, the complete "driver's manual" for data mining. From starting the engine to handling the curves, this book covers the gamut of data mining techniques - including predictive analytics and text mining - illustrating how to achieve maximal value across business, scientific, engineering, and medical applications. What are the best practices through each phase of a data mining project? How can you avoid the most treacherous pitfalls? The answers are in here. "Going beyond its responsibility as a reference book, the heavily-updated second edition also provides all-new, detailed tutorials with step-by-step instructions to drive established data mining software tools across real world applications. This way, newcomers start their engines immediately and experience hands-on success. "What's more, this edition drills down on hot topics across seven new chapters, including deep learning and how to avert "b---s---" results. If you want to roll-up your sleeves and execute on predictive analytics, this is your definite, go-to resource. To put it lightly, if this book isn't on your shelf, you're not a data miner." --Eric Siegel, Ph.D., founder of Predictive Analytics World and author of "Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die" "Great introduction to the real-world process of data mining. The overviews, practical advice, tutorials, and extra CD material make this book an invaluable resource for both new and experienced data miners." --Karl Rexer, PhD (President and Founder of Rexer Analytics, Boston, Massachusetts)Table of ContentsPart 1: History Of Phases Of Data Analysis, Basic Theory, And The Data Mining Process 1. The Background for Data Mining Practice 2. Theoretical Considerations for Data Mining 3. The Data Mining and Predictive Analytic Process 4. Data Understanding and Preparation 5. Feature Selection 6. Accessory Tools for Doing Data Mining Part 2: The Algorithms And Methods In Data Mining And Predictive Analytics And Some Domain Areas 7. Basic Algorithms for Data Mining: A Brief Overview 8. Advanced Algorithms for Data Mining 9. Classification 10. Numerical Prediction 11. Model Evaluation and Enhancement 12. Predictive Analytics for Population Health and Care 13. Big Data in Education: New Efficiencies for Recruitment, Learning, and Retention of Students and Donors 14. Customer Response Modeling 15. Fraud Detection Part 3: Tutorials And Case Studies Tutorial A Example of Data Mining Recipes Using Windows 10 and Statistica 13 Tutorial B Using the Statistica Data Mining Workspace Method for Analysis of Hurricane Data (Hurrdata.sta) Tutorial C Case Study—Using SPSS Modeler and STATISTICA to Predict Student Success at High-Stakes Nursing Examinations (NCLEX) Tutorial D Constructing a Histogram in KNIME Using MidWest Company Personality Data Tutorial E Feature Selection in KNIME Tutorial F Medical/Business Tutorial Tutorial G A KNIME Exercise, Using Alzheimer’s Training Data of Tutorial F Tutorial H Data Prep 1-1: Merging Data Sources Tutorial I Data Prep 1–2: Data Description Tutorial J Data Prep 2-1: Data Cleaning and Recoding Tutorial K Data Prep 2-2: Dummy Coding Category Variables Tutorial L Data Prep 2-3: Outlier Handling Tutorial M Data Prep 3-1: Filling Missing Values With Constants Tutorial N Data Prep 3-2: Filling Missing Values With Formulas Tutorial O Data Prep 3-3: Filling Missing Values With a Model Tutorial P City of Chicago Crime Map: A Case Study Predicting Certain Kinds of Crime Using Statistica Data Miner and Text Miner Tutorial Q Using Customer Churn Data to Develop and Select a Best Predictive Model for Client Defection Using STATISTICA Data Miner 13 64-bit for Windows 10 Tutorial R Example With C&RT to Predict and Display Possible Structural Relationships Tutorial S Clinical Psychology: Making Decisions About Best Therapy for a Client Part 4: Model Ensembles, Model Complexity; Using the Right Model for the Right Use, Significance, Ethics, and the Future, and Advanced Processes 16. The Apparent Paradox of Complexity in Ensemble Modeling 17. The "Right Model" for the "Right Purpose": When Less Is Good Enough 18. A Data Preparation Cookbook 19. Deep Learning 20. Significance versus Luck in the Age of Mining: The Issues of P-Value "Significance" and "Ways to Test Significance of Our Predictive Analytic Models" 21. Ethics and Data Analytics 22. IBM Watson

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Book SynopsisTrade Review"Data Science for Business and Decision Making brings together the key topics required as the foundation for understanding and applying analytics for decision making. The authors have carefully selected the topics, and each one is clearly explained, described, and reinforced with a diverse set of exercises." --Rahul Saxena, Cobot Systems "Data Science for Business and Decision Making provides a thorough essay about statistical methods which are commonly used in business without requiring a strong mathematical background. The presentation is rigorous and accessible thanks to a large number of examples that are developed step-by-step. The illustrations feature various software and the proposed exercises are particularly helpful for students and practitioners." --Francesco Bartolucci, University of PerugiaTable of ContentsPart 1: Foundations of Business Data Analysis 1. Introduction to Data Analysis and Decision Making 2. Type of Variables and Mensuration Scales Part 2: Descriptive Statistics 3. Univariate Descriptive Statistics 4. Bivariate Descriptive Statistics Part 3: Probabilistic Statistics 5. Introduction of Probability 6. Random Variables and Probability Distributions Part 4: Statistical Inference 7. Sampling 8. Estimation 9. Hypothesis Tests 10. Non-parametric Tests Part 5: Multivariate Exploratory Data Analysis 11. Cluster Analysis 12. Principal Components Analysis and Factorial Analysis Part 6: Generalized Linear Models 13. Simple and Multiple Regression Models 14. Binary and Multinomial Logistics Regression Models 15. Regression Models for Count Data: Poisson and Negative Binomial Part 7: Optimization Models and Simulation 16. Introduction to Optimization Models: Business Problems Formulations and Modeling 17. Solution of Linear Programming Problems 18. Network Programming 19. Integer Programming 20. Simulation and Risk Analysis Part 8: Other Topics 21. Design and Experimental Analysis 22. Statistical Process Control 23. Data Mining and Multilevel Modeling

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Book SynopsisTable of Contents1. Planning Studies: From Design to Publication 2. Planning Analysis: Addressing Your Scientific Objective 3. Probability and Relative Frequency 4. Distributions 5. Descriptive Statistics 6. Finding Probabilities 7. Hypothesis Testing: Concept and Practice 8. Confidence Intervals 9. Tests on Categorical Data 10. Risks, Odds, and ROC Curves 11. Tests of Location with Continuous Outcomes 12. Equivalence Testing 13. Tests on Variability and Distributions 14. Measuring Association and Agreement 15. Linear Regression and Correlation 16. Multiple Linear and Curvilinear Regression 17. Logistic Regression for Binary Outcomes 18. Regression Models for Count Outcomes 19. Analysis of Censored Time-To-Event Data 20. Analysis of Repeated Continuous Measures of Time 21. Sample Size Estimation 22. Clinical Trials and Group Sequential Analyses 23. Epidemiology and Alternative Sampling Designs 24. Meta Analyses 25. Bayesian Statistics 26. Questionnaires and Surveys 27. Techniques to Aid Analysis 28. Methods You Might Meet, But Not Every Day

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Book SynopsisTable of ContentsPart I: Introduction and Descriptive Statistics 1. Introduction: Defining the Role of Statistics in Business 2. Data Structures: Classifying the Various Types of Data Sets 3. Histograms: Looking at the Distribution of Data 4. Landmark Summaries: Interpreting Typical Values and Percentiles 5. Variability: Dealing with Diversity Part II: Probability 6. Probability: Understanding Random Situations 7. Random Variables: Working with Uncertain Numbers Part III: Statistical Inference 8. Random Sampling: Planning Ahead for Data Gathering 9. Confidence Intervals: Admitting That Estimates Are Not Exact 10. Hypothesis Testing: Deciding Between Reality and Coincidence Part IV: Regression and Time Series 11. Correlation and Regression: Measuring and Predicting Relationships 12. Multiple Regression: Predicting One Variable From Several Others 13. Report Writing: Communicating the Results of a Multiple Regression 14. Time Series: Understanding Changes Over Time Part V: Methods and Applications 15. ANOVA: Testing for Differences Among Many Samples and Much More 16. Recent Developments 17. Chi-Squared Analysis: Testing for Patterns in Qualitative Data 18. Quality Control: Recognizing and Managing Variation 19. Statistical (Machine) Learning: Using Complex Models With Large Data Sets

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Book SynopsisTable of Contents1. Introduction 2. A Foundation for Robust Methods 3. Estimating Measures of Location and Scale 4. Confidence Intervals in the One-Sample Case 5. Comparing Two Groups 6. Some Multivariate Methods 7. One-Way and Higher Designs for Independent Groups 8. Comparing Multiple Dependent Groups 9. Correlation and Tests of Independence 10. Robust Regression 11. More Regression Methods 12. ANCOVA

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Book SynopsisTable of Contents1. An appreciation of baseball and its mathematics 2. Is baseball still the national pastime? 3. Baseball before steroids 4. Bill James and the genesis of sabermetrics 5. Rattling the sabermetrics 6. The annihilation of records: Where have you gone, Babe Ruth? 7. Steroids, etc. 8. Scandal scarred: A discussion of our national pastime’s controversial history 9. The last inning 10. Epilogue: Where have we been? Where do we go from here? A final word from the editor

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Book SynopsisTable of Contents1. Data and statistics 2. Probability and sampling distributions 3. Principles of inference 4. Inferences on a single population 5. Inferences for two populations 6. Inferences for two or more means 7. Linear regression 8. Multiple regression 9. Linear models 10. Factorial experiments 11. Design of experiments 12. Categorical data 13. Generalized linear models 14. Nonparametric methods

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Book SynopsisElliot A. Tanis: Tanis has written 30 articles in probability and statistics, many illustrating applications using the computer. He has authored or co-authored four books in probability and statistics. These include Probability & Statistics Explorations with MAPLE, 2nd edition, with Zaven Karian in 1999 and Probability and Statistical Inference, 7th edition, with Robert V. Hogg in 2006. He was Chairperson (1976-77) and Governor (1989-92) of the Michigan Section of the Mathematical Association of America. He was a winner of the Hope's Outstanding Professor Educator (H.O.P.E.) award in 1989 and received the award for Distinguished College or University Teaching of Mathematics, Michigan Section, MAA, in 1992. Tanis became Professor Emeritus of Mathematics at Hope College in 2000 after teaching there 35 years. Robert V. Hogg: Hogg has written over 70 research articles and coauthored five books, including IntroductioTrade Review"The authors have years of experience analyzing real data, have collected excellent examples to illustrate statistical concepts and anomalies, and are proven writers in the discipline." Professor Charles Sommer, SUNY College at Brockport "The authors have done a wonderful job in writing this book...I must congratulate them for doing a great job and helping in the development of the subject of Statistics." Professor M.L. Aggarwal, The University of MemphisTable of ContentsPreface 1. Probability 1.1 Basic Concepts 1.2 Methods of Enumeration 1.3 Conditional Probability 1.4 Independent Events 1.5 Bayes's Theorem Chapter One Comments 2. Discrete Distributions 2.1 Discrete Probability Distributions 2.2 Expectations 2.3 Special Discrete Distributions 2.4 Estimation 2.5 Linear Functions of Independent Random Variables 2.6 Multivariate Discrete Distributions Chapter Two Comments 3. Continuous Distributions 3.1 Descriptive Statistics and EDA 3.2 Continuous Probability Distributions 3.3 Special Continuous Distributions 3.4 The Normal Distribution 3.5 Estimation in the Continuous Case 3.6 The Central Limit Theorem 3.7 Approximations for Discrete Distributions Chapter Three Comemnts 4. Applications of Statistical Inference 4.1 Summary of Necessary Theoretical Results 4.2 Confidence Intervals Using X2 F,and T 4.3 Confidence Intervals and Tests of Hypotheses 4.4 Basic Tests Concerning One Parameter 4.5 Tests of the Equality of Two Parameters 4.6 Simple Linear Regression 4.7 More on Linear Regression 4.8 One-Factor Analysis of Variance 4.9 Distribution-Free Confidence and Tolerance Intervals 4.10 Chi-Square Goodness of Fit Tests 4.11 Contingency Tables Chapter Four Comments 5. Computer Oriented Techniques 5.1 Computation of Statistics 5.2 Computer Algebra Systems 5.3 Simulation 5.4 Resampling Chapter Five Comments 6. Some Sampling Distribution Theory 6.1 Moment-Generation Function Technique 6.2 M.G.F of Linear Functions 6.3 Limiting Moment-Generating Functions 6.4 Use of Order Statistics in Non-regular Cases Chapter Six Comments

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Book SynopsisTable of Contents Chapter 1: How to Make a Decision with Statistics Chapter 2: Producing Data Chapter 3: Observation Studies and Experiments Chapter 4 Summarizing Data Graphically Chapter 5 Summarizing Data Numerically Chapter 6 Using Models to Make Decisions Chapter 7: How to Measure Uncertainty with Probability Chapter 8: Sampling Distributions: Measuring the Accuracy of Sample Results Chapter 9: Making Decisions about a Population Proportion with Confidence Chapter 10: Making Decisions about a Population Mean with Confidence Chapter 11: Comparing Two Treatments Chapter 12: Comparing Many Treatments Chapter 13: Relationships Between Quantitative Variables Chapter 14: Analysis of Count Data Chapter 15: What if the Assumptions Don't Hold?

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Book SynopsisLeonard Mlodinow''s The Drunkard''s Walk: How Randomness Rules Our Lives is an exhilarating, eye-opening guide to understanding our random world.Randomness and uncertainty surround everything we do. So why are we so bad at understanding them? The same tools that help us understand the random paths of molecules can be applied to the randomness that governs so many aspects of our everyday lives, from winning the lottery to road safety, and reveals the truth about the success of sporting heroes and film stars, and even how to make sense of a blood test.The Drunkard''s Walk reveals the psychological illusions that prevent us understanding everything from stock-picking to wine-tasting - read it, or risk becoming another victim of chance.''A wonderfully readable guide to how the mathematical laws of randomness affect our lives'' Stephen Hawking, author of A Brief History of TimeTrade Review'Mlodinow writes in a breezy style, interspersing probabilistic mind-benders with portraits of theorists ! The result is a readable crash course in randomness.' New York Times 'If you're strong enough to have some of your favorite assumptions challenged, please read the Drunkard's Walk, a history, explanation, and exaltation of probability theory.' Fortune magazine

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Book SynopsisEveryone wants to succeed in life. But what causes some of us to be more successful than others? Is it really down to skill and strategy - or something altogether more unpredictable? This book intends to change the way you think about business and the world.

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Book SynopsisThe International Bestseller by ''The Galileo of number crunchers'' (Independent)Every time we choose a route to work, decide whether to go on a second date, or set aside money for a rainy day, we are making a prediction about the future. Yet from the financial crisis to ecological disasters, we routinely fail to foresee hugely significant events, often at great cost to society. The rise of ''big data'' has the potential to help us predict the future, yet much of it is misleading, useless or distracting.In The Signal and the Noise, the New York Times political forecaster Nate Silver, who accurately predicted the results of every state in the 2012 US election, reveals how we can all develop better foresight in an uncertain world. From the stock market to the poker table, from earthquakes to the economy, he takes us on an enthralling insider''s tour of the high-stakes world of forecasting, showing how we can all learn to detect the true signals amid a noise of data. ''Remarkable and rewarding'' Matthew D''Ancona, Sunday Telegraph''A lucid explanation of how to think probabilistically'' GuardianTrade ReviewOutstanding... I was hooked -- Tim Harford * Financial Times *One of the more momentous books of the decade * The New York Times Book Review *A lucid explanation of how to think probabilistically * Guardian *The inhabitants of Westminster are speed-reading The Signal and the Noise... They will find the book remarkable and rewarding * Sunday Telegraph *Is there anything now that Nate Silver could tell us that we wouldn't believe? * Jonathan Freedland *Fascinating... our age's Brunel -- Bryan Appleyard * Sunday Times *A surprisingly accessible peek into the world of mathematical probability -- Daily TelegraphThe Galileo of number crunchers * Independent *A 34-year old Delphic Oracle * Daily Beast *

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Book Synopsis

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