These may not sound like typical questions for an economist to ask. But Steven D. Levitt is not a typical economist. He is a much-heralded scholar who studies the riddles of everyday life—from cheating and crime to sports and child-rearing—and whose conclusions turn conventional wisdom on its head.
Freakonomics is a groundbreaking collaboration between Levitt and Stephen J. Dubner, an award-winning author and journalist. They usually begin with a mountain of data and a simple question. Some of these questions concern life-and-death issues; others have an admittedly freakish quality. Thus the new field of study contained in this book: Freakonomics.
Through forceful storytelling and wry insight, Levitt and Dubner show that economics is, at root, the study of incentives—how people get what they want, or need, especially when other people want or need the same thing. In Freakonomics, they explore the hidden side of . . . well, everything. The inner workings of a crack gang. The truth about real-estate agents. The myths of campaign finance. The telltale marks of a cheating schoolteacher. The secrets of the Ku Klux Klan.
What unites all these stories is a belief that the modern world, despite a great deal of complexity and downright deceit, is not impenetrable, is not unknowable, and—if the right questions are asked—is even more intriguing than we think. All it takes is a new way of looking.
Freakonomics establishes this unconventional premise: If morality represents how we would like the world to work, then economics represents how it actually does work. It is true that readers of this book will be armed with enough riddles and stories to last a thousand cocktail parties. But Freakonomics can provide more than that. It will literally redefine the way we view the modern world.
Bonus material added to the revised and expanded 2006 editionThe original New York Times Magazine article about Steven D. Levitt by Stephen J. Dubner, which led to the creation of this book.Seven “Freakonomics” columns written for the New York Times Magazine, published between August 2005 and April 2006.Selected entries from the Freakonomics blog, posted between April 2005 and May 2006 at http://www.freakonomics.com/blog/.
Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you’ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research.Develop a naïve Bayesian classifier to determine if an email is spam, based only on its textUse linear regression to predict the number of page views for the top 1,000 websitesLearn optimization techniques by attempting to break a simple letter cipherCompare and contrast U.S. Senators statistically, based on their voting recordsBuild a “whom to follow” recommendation system from Twitter data
For those who slept through Stats 101, this book is a lifesaver. Wheelan strips away the arcane and technical details and focuses on the underlying intuition that drives statistical analysis. He clarifies key concepts such as inference, correlation, and regression analysis, reveals how biased or careless parties can manipulate or misrepresent data, and shows us how brilliant and creative researchers are exploiting the valuable data from natural experiments to tackle thorny questions.
And in Wheelan’s trademark style, there’s not a dull page in sight. You’ll encounter clever Schlitz Beer marketers leveraging basic probability, an International Sausage Festival illuminating the tenets of the central limit theorem, and a head-scratching choice from the famous game show Let’s Make a Deal—and you’ll come away with insights each time. With the wit, accessibility, and sheer fun that turned Naked Economics into a bestseller, Wheelan defies the odds yet again by bringing another essential, formerly unglamorous discipline to life.
The first part provides an introduction to basic procedures for handling and operating with text strings. Then, it reviews major mathematical modeling approaches. Statistical and geometrical models are also described along with main dimensionality reduction methods. Finally, it presents some specific applications such as document clustering, classification, search and terminology extraction.
All descriptions presented are supported with practical examples that are fully reproducible. Further reading, as well as additional exercises and projects, are proposed at the end of each chapter for those readers interested in conducting further experimentation.
This second edition presents new developments and discoveries that have been made in the field. Parsing techniques have grown considerably in importance, both in computational linguistics where such parsers are the only option, and computer science, where advanced compilers often use general CF parsers. Parsing techniques provide a solid basis for compiler construction and contribute to all existing software: enabling Web browsers to analyze HTML pages and PostScript printers to analyze PostScript. Some of the more advanced techniques are used in code generation in compilers and in data compression.
In linguistics, the importance of formal grammars was recognized early on, but only recently have the corresponding parsing techniques been applied. Also their importance as general pattern recognizers is slowly being acknowledged. This text Parsing Techniques explores new developments, such as generalized deterministic parsing, linear-time substring parsing, parallel parsing, parsing as intersection, non-canonical methods, and non-Chomsky systems.
To provide readers with low-threshold access to the full field of parsing techniques, this new edition uses a two-tiered structure. The basic ideas behind the dozen or so existing parsing techniques are explained in an intuitive and narrative style, and problems are presented at the conclusion of each chapter, allowing the reader to step outside the bounds of the covered material and explore parsing techniques at various levels. The reader is also provided with an extensive annotated bibliography as well as hints and partial solutions to a number of problems. In the bibliography, hundreds of realizations and improvements of parsing techniques are explained in a much terser, yet still informal, style, improving its readability and usability.
The reader should have an understanding of algorithmic thinking, especially recursion; however, knowledge of any particular programming language is not required.
The Essentials For Dummies Series
Dummies is proud to present our new series, The Essentials For Dummies. Now students who are prepping for exams, preparing to study new material, or who just need a refresher can have a concise, easy-to-understand review guide that covers an entire course by concentrating solely on the most important concepts. From algebra and chemistry to grammar and Spanish, our expert authors focus on the skills students most need to succeed in a subject.
Digital information is a powerful tool that spreads unbelievably rapidly, infects all corners of society, and is all but impossible to control—even when that information is actually a lie. In Virtual Unreality, Charles Seife uses the skepticism, wit, and sharp facility for analysis that captivated readers in Proofiness and Zero to take us deep into the Internet information jungle and cut a path through the trickery, fakery, and cyber skullduggery that the online world enables.
Taking on everything from breaking news coverage and online dating to program trading and that eccentric and unreliable source that is Wikipedia, Seife arms his readers with actual tools—or weapons—for discerning truth from fiction online.
But if you're serious about your profession, intuition isn't enough. Perl Best Practices author Damian Conway explains that rules, conventions, standards, and practices not only help programmers communicate and coordinate with one another, they also provide a reliable framework for thinking about problems, and a common language for expressing solutions. This is especially critical in Perl, because the language is designed to offer many ways to accomplish the same task, and consequently it supports many incompatible dialects.
With a good dose of Aussie humor, Dr. Conway (familiar to many in the Perl community) offers 256 guidelines on the art of coding to help you write better Perl code--in fact, the best Perl code you possibly can. The guidelines cover code layout, naming conventions, choice of data and control structures, program decomposition, interface design and implementation, modularity, object orientation, error handling, testing, and debugging.
They're designed to work together to produce code that is clear, robust, efficient, maintainable, and concise, but Dr. Conway doesn't pretend that this is the one true universal and unequivocal set of best practices. Instead, Perl Best Practices offers coherent and widely applicable suggestions based on real-world experience of how code is actually written, rather than on someone's ivory-tower theories on howsoftware ought to be created.
Most of all, Perl Best Practices offers guidelines that actually work, and that many developers around the world are already using. Much like Perl itself, these guidelines are about helping you to get your job done, without getting in the way.
Praise for Perl Best Practices from Perl community members:
"As a manager of a large Perl project, I'd ensure that every member of my team has a copy of Perl Best Practices on their desk, and use it as the basis for an in-house style guide."-- Randal Schwartz
"There are no more excuses for writing bad Perl programs. All levels of Perl programmer will be more productive after reading this book."-- Peter Scott
"Perl Best Practices will be the next big important book in the evolution of Perl. The ideas and practices Damian lays down will help bring Perl out from under the embarrassing heading of "scripting languages". Many of us have known Perl is a real programming language, worthy of all the tasks normally delegated to Java and C++. With Perl Best Practices, Damian shows specifically how and why, so everyone else can see, too."-- Andy Lester
"Damian's done what many thought impossible: show how to build large, maintainable Perl applications, while still letting Perl be the powerful, expressive language that programmers have loved for years."-- Bill Odom
"Finally, a means to bring lasting order to the process and product of real Perl development teams."-- Andrew Sundstrom"Perl Best Practices provides a valuable education in how to write robust, maintainable Perl, and is a definitive citation source when coaching other programmers."-- Bennett Todd"I've been teaching Perl for years, and find the same question keeps being asked: Where can I find a reference for writing reusable, maintainable Perl code? Finally I have a decent answer."-- Paul Fenwick"At last a well researched, well thought-out, comprehensive guide to Perl style. Instead of each of us developing our own, we can learn good practices from one of Perl's most prolific and experienced authors. I recommend this book to anyone who prefers getting on with the job rather than going back and fixing errors caused by syntax and poor style issues."-- Jacinta Richardson"If you care about programming in any language read this book. Even if you don't intend to follow all of the practices, thinking through your style will improve it."-- Steven Lembark"The Perl community's best author is back with another outstanding book. There has never been a comprehensive reference on high quality Perl coding and style until Perl Best Practices. This book fills a large gap in every Perl bookshelf."-- Uri Guttman
Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.
The book is divided into three parts and begins with the basics: models, probability, Bayes’ rule, and the R programming language. The discussion then moves to the fundamentals applied to inferring a binomial probability, before concluding with chapters on the generalized linear model. Topics include metric-predicted variable on one or two groups; metric-predicted variable with one metric predictor; metric-predicted variable with multiple metric predictors; metric-predicted variable with one nominal predictor; and metric-predicted variable with multiple nominal predictors. The exercises found in the text have explicit purposes and guidelines for accomplishment.
This book is intended for first-year graduate students or advanced undergraduates in statistics, data analysis, psychology, cognitive science, social sciences, clinical sciences, and consumer sciences in business.Accessible, including the basics of essential concepts of probability and random samplingExamples with R programming language and JAGS softwareComprehensive coverage of all scenarios addressed by non-Bayesian textbooks: t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chi-square (contingency table analysis)Coverage of experiment planningR and JAGS computer programming code on websiteExercises have explicit purposes and guidelines for accomplishment
Provides step-by-step instructions on how to conduct Bayesian data analyses in the popular and free software R and WinBugs
One is heuristic and nonrigorous, and attempts to develop in students an intuitive feel for the subject that enables him or her to think probabilistically. The other approach attempts a rigorous development of probability by using the tools of measure theory. The first approach is employed in this text.
The book begins by introducing basic concepts of probability theory, such as the random variable, conditional probability, and conditional expectation. This is followed by discussions of stochastic processes, including Markov chains and Poison processes. The remaining chapters cover queuing, reliability theory, Brownian motion, and simulation. Many examples are worked out throughout the text, along with exercises to be solved by students.
This book will be particularly useful to those interested in learning how probability theory can be applied to the study of phenomena in fields such as engineering, computer science, management science, the physical and social sciences, and operations research. Ideally, this text would be used in a one-year course in probability models, or a one-semester course in introductory probability theory or a course in elementary stochastic processes.
New to this Edition:
65% new chapter material including coverage of finite capacity queues, insurance risk models and Markov chainsContains compulsory material for new Exam 3 of the Society of Actuaries containing several sections in the new examsUpdated data, and a list of commonly used notations and equations, a robust ancillary package, including a ISM, SSM, and test bankIncludes SPSS PASW Modeler and SAS JMP software packages which are widely used in the field
Superior writing styleExcellent exercises and examples covering the wide breadth of coverage of probability topics Real-world applications in engineering, science, business and economics
But Hand is no believer in superstitions, prophecies, or the paranormal. His definition of "miracle" is thoroughly rational. No mystical or supernatural explanation is necessary to understand why someone is lucky enough to win the lottery twice, or is destined to be hit by lightning three times and still survive. All we need, Hand argues, is a firm grounding in a powerful set of laws: the laws of inevitability, of truly large numbers, of selection, of the probability lever, and of near enough.
Together, these constitute Hand's groundbreaking Improbability Principle. And together, they explain why we should not be so surprised to bump into a friend in a foreign country, or to come across the same unfamiliar word four times in one day. Hand wrestles with seemingly less explicable questions as well: what the Bible and Shakespeare have in common, why financial crashes are par for the course, and why lightning does strike the same place (and the same person) twice. Along the way, he teaches us how to use the Improbability Principle in our own lives—including how to cash in at a casino and how to recognize when a medicine is truly effective.
An irresistible adventure into the laws behind "chance" moments and a trusty guide for understanding the world and universe we live in, The Improbability Principle will transform how you think about serendipity and luck, whether it's in the world of business and finance or you're merely sitting in your backyard, tossing a ball into the air and wondering where it will land.
By showing us the true nature of chance and revealing the psychological illusions that cause us to misjudge the world around us, Mlodinow gives us the tools we need to make more informed decisions. From the classroom to the courtroom and from financial markets to supermarkets, Mlodinow's intriguing and illuminating look at how randomness, chance, and probability affect our daily lives will intrigue, awe, and inspire.
From the Trade Paperback edition.
The author begins with basic characteristics of financial time series data before covering three main topics:Analysis and application of univariate financial time series The return series of multiple assets Bayesian inference in finance methods
Key features of the new edition include additional coverage of modern day topics such as arbitrage, pair trading, realized volatility, and credit risk modeling; a smooth transition from S-Plus to R; and expanded empirical financial data sets.
The overall objective of the book is to provide some knowledge of financial time series, introduce some statistical tools useful for analyzing these series and gain experience in financial applications of various econometric methods.
5 Steps to a 5: 500 AP Statistics Questions to Know by Test Day is tailored to meet your study needs—whether you’ve left it to the last minute to prepare or you have been studying for months. You will benefit from going over the questions written to parallel the topic, format, and degree of difficulty of the questions contained in the AP exam, accompanied by answers with comprehensive explanations.
Features:500 AP-style questions and answers referenced to core AP materials Review explanations for right and wrong answers Additional online practice Close simulations of the real AP exams Updated material reflects the latest tests Online practice exercises
". . . [this book] should be on the shelf of everyone interested in . . . longitudinal data analysis."
—Journal of the American Statistical Association
Features newly developed topics and applications of the analysis of longitudinal data
Applied Longitudinal Analysis, Second Edition presents modern methods for analyzing data from longitudinal studies and now features the latest state-of-the-art techniques. The book emphasizes practical, rather than theoretical, aspects of methods for the analysis of diverse types of longitudinal data that can be applied across various fields of study, from the health and medical sciences to the social and behavioral sciences.
The authors incorporate their extensive academic and research experience along with various updates that have been made in response to reader feedback. The Second Edition features six newly added chapters that explore topics currently evolving in the field, including:Fixed effects and mixed effects models Marginal models and generalized estimating equations Approximate methods for generalized linear mixed effects models Multiple imputation and inverse probability weighted methods Smoothing methods for longitudinal data Sample size and power
Each chapter presents methods in the setting of applications to data sets drawn from the health sciences. New problem sets have been added to many chapters, and a related website features sample programs and computer output using SAS, Stata, and R, as well as data sets and supplemental slides to facilitate a complete understanding of the material.
With its strong emphasis on multidisciplinary applications and the interpretation of results, Applied Longitudinal Analysis, Second Edition is an excellent book for courses on statistics in the health and medical sciences at the upper-undergraduate and graduate levels. The book also serves as a valuable reference for researchers and professionals in the medical, public health, and pharmaceutical fields as well as those in social and behavioral sciences who would like to learn more about analyzing longitudinal data.
Each chapter presents easy-to-follow descriptions, along with graphics, formulas, solved examples, and hands-on exercises. If you want to perform common statistical analyses and learn a wide range of techniques without getting in over your head, this is your book.Learn basic concepts of measurement and probability theory, data management, and research designDiscover basic statistical procedures, including correlation, the t-test, the chi-square and Fisher’s exact tests, and techniques for analyzing nonparametric dataLearn advanced techniques based on the general linear model, including ANOVA, ANCOVA, multiple linear regression, and logistic regressionUse and interpret statistics for business and quality improvement, medical and public health, and education and psychologyCommunicate with statistics and critique statistical information presented by others
This comprehensive, reader-friendly volume offers readers a high-level orientation, discussing the foundations of the field and presenting both the classical work and the most recent results. It covers an extremely rich array of topics including not only syntax and semantics but also phonology and morphology, probabilistic approaches, complexity, learnability, and the analysis of speech and handwriting.
As the first text of its kind, this innovative book will be a valuable tool and reference for those in information science (information retrieval and extraction, search engines) and in natural language technologies (speech recognition, optical character recognition, HCI). Exercises suitable for advanced readers are included as well as suggestions for further reading and an extensive bibliography.
"I'm pleased and impressed. The book is very readable, often entertaining---it tells what the issues are, what they are called, in what health they are, where more meat can be found. Given the enormous amount of material and concepts touched on, and the technical difficulties lying under the surface almost everywhere, the book betrays scholarship in a matter-of-fact way, making due impression on, but without clobbering, the reader. This is a book that invites READING THROUGH...".
Professor Tommaso Toffoli, Boston University, USA
"It is a remarkable achievement, essential reading for every linguist who aspires to be well informed about applications of mathematics in the language sciences."
Professor Geoffrey Pullum, University of Edinburgh, UK
"I really liked this book. First, it is written very well and secondly, the author has taken a rather non-standard but very attractive approach to mathematical linguistics. It is very refreshing."
Professor Aravind K. Joshi, University of Pennsylvania, USA
The authors first present an overview of publicly available baseball datasets and a gentle introduction to the type of data structures and exploratory and data management capabilities of R. They also cover the traditional graphics functions in the base package and introduce more sophisticated graphical displays available through the lattice and ggplot2 packages. Much of the book illustrates the use of R through popular sabermetrics topics, including the Pythagorean formula, runs expectancy, career trajectories, simulation of games and seasons, patterns of streaky behavior of players, and fielding measures. Each chapter contains exercises that encourage readers to perform their own analyses using R. All of the datasets and R code used in the text are available online.
This book helps readers answer questions about baseball teams, players, and strategy using large, publically available datasets. It offers detailed instructions on downloading the datasets and putting them into formats that simplify data exploration and analysis. Through the book’s various examples, readers will learn about modern sabermetrics and be able to conduct their own baseball analyses.
The roller coaster of romance is hard to quantify; defining how lovers might feel from a set of simple equations is impossible. But that doesn’t mean that mathematics isn’t a crucial tool for understanding love.
Love, like most things in life, is full of patterns. And mathematics is ultimately the study of patterns—from predicting the weather to the fluctuations of the stock market, the movement of planets or the growth of cities. These patterns twist and turn and warp and evolve just as the rituals of love do.
In The Mathematics of Love, Dr. Hannah Fry takes the reader on a fascinating journey through the patterns that define our love lives, applying mathematical formulas to the most common yet complex questions pertaining to love: What’s the chance of finding love? What’s the probability that it will last? How do online dating algorithms work, exactly? Can game theory help us decide who to approach in a bar? At what point in your dating life should you settle down?
From evaluating the best strategies for online dating to defining the nebulous concept of beauty, Dr. Fry proves—with great insight, wit, and fun—that math is a surprisingly useful tool to negotiate the complicated, often baffling, sometimes infuriating, always interesting, mysteries of love.
This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for ``wide'' data (p bigger than n), including multiple testing and false discovery rates.
Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.
"It is, as far as I'm concerned, among the best books in math ever written....if you are a mathematician and want to have the top reference in probability, this is it." (Amazon.com, January 2006)
A complete and comprehensive classic in probability and measure theory
Probability and Measure, Anniversary Edition by Patrick Billingsley celebrates the achievements and advancements that have made this book a classic in its field for the past 35 years. Now re-issued in a new style and format, but with the reliable content that the third edition was revered for, this Anniversary Edition builds on its strong foundation of measure theory and probability with Billingsley's unique writing style. In recognition of 35 years of publication, impacting tens of thousands of readers, this Anniversary Edition has been completely redesigned in a new, open and user-friendly way in order to appeal to university-level students.
This book adds a new foreward by Steve Lally of the Statistics Department at The University of Chicago in order to underscore the many years of successful publication and world-wide popularity and emphasize the educational value of this book. The Anniversary Edition contains features including:An improved treatment of Brownian motion Replacement of queuing theory with ergodic theory Theory and applications used to illustrate real-life situations Over 300 problems with corresponding, intensive notes and solutions Updated bibliography An extensive supplement of additional notes on the problems and chapter commentaries
Patrick Billingsley was a first-class, world-renowned authority in probability and measure theory at a leading U.S. institution of higher education. He continued to be an influential probability theorist until his unfortunate death in 2011. Billingsley earned his Bachelor's Degree in Engineering from the U.S. Naval Academy where he served as an officer. he went on to receive his Master's Degree and doctorate in Mathematics from Princeton University.Among his many professional awards was the Mathematical Association of America's Lester R. Ford Award for mathematical exposition. His achievements through his long and esteemed career have solidified Patrick Billingsley's place as a leading authority in the field and been a large reason for his books being regarded as classics.
This Anniversary Edition of Probability and Measure offers advanced students, scientists, and engineers an integrated introduction to measure theory and probability. Like the previous editions, this Anniversary Edition is a key resource for students of mathematics, statistics, economics, and a wide variety of disciplines that require a solid understanding of probability theory.
Machine Learning: Hands-On for Developers and Technical Professionals provides hands-on instruction and fully-coded working examples for the most common machine learning techniques used by developers and technical professionals. The book contains a breakdown of each ML variant, explaining how it works and how it is used within certain industries, allowing readers to incorporate the presented techniques into their own work as they follow along. A core tenant of machine learning is a strong focus on data preparation, and a full exploration of the various types of learning algorithms illustrates how the proper tools can help any developer extract information and insights from existing data. The book includes a full complement of Instructor's Materials to facilitate use in the classroom, making this resource useful for students and as a professional reference.
At its core, machine learning is a mathematical, algorithm-based technology that forms the basis of historical data mining and modern big data science. Scientific analysis of big data requires a working knowledge of machine learning, which forms predictions based on known properties learned from training data. Machine Learning is an accessible, comprehensive guide for the non-mathematician, providing clear guidance that allows readers to:Learn the languages of machine learning including Hadoop, Mahout, and Weka Understand decision trees, Bayesian networks, and artificial neural networks Implement Association Rule, Real Time, and Batch learning Develop a strategic plan for safe, effective, and efficient machine learning
By learning to construct a system that can learn from data, readers can increase their utility across industries. Machine learning sits at the core of deep dive data analysis and visualization, which is increasingly in demand as companies discover the goldmine hiding in their existing data. For the tech professional involved in data science, Machine Learning: Hands-On for Developers and Technical Professionals provides the skills and techniques required to dig deeper.
The authors present the material in an accessible style and motivate concepts using real-world examples. Throughout, they use stories to uncover connections between the fundamental distributions in statistics and conditioning to reduce complicated problems to manageable pieces.
The book includes many intuitive explanations, diagrams, and practice problems. Each chapter ends with a section showing how to perform relevant simulations and calculations in R, a free statistical software environment.
Recent advances in the field, particularly Parrondo's paradox, have triggered a surge of interest in the statistical and mathematical theory behind gambling. This interest was acknowledge in the motion picture, "21," inspired by the true story of the MIT students who mastered the art of card counting to reap millions from the Vegas casinos. Richard Epstein's classic book on gambling and its mathematical analysis covers the full range of games from penny matching to blackjack, from Tic-Tac-Toe to the stock market (including Edward Thorp's warrant-hedging analysis). He even considers whether statistical inference can shed light on the study of paranormal phenomena. Epstein is witty and insightful, a pleasure to dip into and read and rewarding to study. The book is written at a fairly sophisticated mathematical level; this is not "Gambling for Dummies" or "How To Beat The Odds Without Really Trying." A background in upper-level undergraduate mathematics is helpful for understanding this work.
o Comprehensive and exciting analysis of all major casino games and variants o Covers a wide range of interesting topics not covered in other books on the subject o Depth and breadth of its material is unique compared to other books of this nature
Richard Epstein's website: www.gamblingtheory.net
• Construct and interpret statistical charts and tables with Excel or OpenOffice.org Calc 3
• Work with mean, median, mode, standard deviation, Z scores, skewness, and other descriptive statistics
• Use probability and probability distributions
• Work with sampling distributions and confidence intervals
• Test hypotheses with Z, t, chi-square, ANOVA, and other techniques
• Perform powerful regression analysis and modeling
• Use multiple regression to develop models that contain several independent variables
• Master specific statistical techniques for quality and Six Sigma programs
About the Web Site
Download practice files, templates, data sets, and sample spreadsheet models—including ready-to-use solutions for your own work! www.ftpress.com/youcanlearnstatistics2e
Across various industries, compensation professionals work to organize and analyze aspects of employment that deal with elements of pay, such as deciding base salary, bonus, and commission provided by an employer to its employees for work performed. Acknowledging the numerous quantitative analyses of data that are a part of this everyday work, Statistics for Compensation provides a comprehensive guide to the key statistical tools and techniques needed to perform those analyses and to help organizations make fully informed compensation decisions.
This self-contained book is the first of its kind to explore the use of various quantitative methods—from basic notions about percents to multiple linear regression—that are used in the management, design, and implementation of powerful compensation strategies. Drawing upon his extensive experience as a consultant, practitioner, and teacher of both statistics and compensation, the author focuses on the usefulness of the techniques and their immediate application to everyday compensation work, thoroughly explaining major areas such as:
Frequency distributions and histograms
Measures of location and variability
Exponential curve models
Maturity curve models
Market models and salary survey analysis
Linear and exponential integrated market models
Job pricing market models
Throughout the book, rigorous definitions and step-by-step procedures clearly explain and demonstrate how to apply the presented statistical techniques. Each chapter concludes with a set of exercises, and various case studies showcase the topic's real-world relevance. The book also features an extensive glossary of key statistical terms and an appendix with technical details. Data for the examples and practice problems are available in the book and on a related FTP site.
Statistics for Compensation is an excellent reference for compensation professionals, human resources professionals, and other practitioners responsible for any aspect of base pay, incentive pay, sales compensation, and executive compensation in their organizations. It can also serve as a supplement for compensation courses at the upper-undergraduate and graduate levels.
In The Lady Tasting Tea, readers will encounter not only Ronald Fisher's theories (and their repercussions), but the ideas of dozens of men and women whose revolutionary work affects our everyday lives. Writing with verve and wit, author David Salsburg traces the rise and fall of Karl Pearson's theories, explores W. Edwards Deming's statistical methods of quality control (which rebuilt postwar Japan's economy), and relates the story of Stella Cunliff's early work on the capacity of small beer casks at the Guinness brewing factory.
The Lady Tasting Tea is not a book of dry facts and figures, but the history of great individuals who dared to look at the world in a new way.
Addressing the highly competitive and risky environments of current-day financial and sports gambling markets, Forecasting in Financial and Sports Gambling Markets details the dynamic process of constructing effective forecasting rules based on both graphical patterns and adaptive drift modeling (ADM) of cointegrated time series. The book uniquely identifies periods of inefficiency that these markets oscillate through and develops profitable forecasting models that capitalize on irrational behavior exhibited during these periods.
Providing valuable insights based on the author's firsthand experience, this book utilizes simple, yet unique, candlestick charts to identify optimal time periods in financial markets and optimal games in sports gambling markets for which forecasting models are likely to provide profitable trading and wagering outcomes. Featuring detailed examples that utilize actual data, the book addresses various topics that promote financial and mathematical literacy, including:
Higher order ARMA processes in financial markets
The effects of gambling shocks in sports gambling markets
Cointegrated time series with model drift
Throughout the book, interesting real-world applications are presented, and numerous graphical procedures illustrate favorable trading and betting opportunities, which are accompanied by mathematical developments in adaptive model forecasting and risk assessment. A related web site features updated reviews in sports and financial forecasting and various links on the topic.
Forecasting in Financial and Sports Gambling Markets is an excellent book for courses on financial economics and time series analysis at the upper-undergraduate and graduate levels. The book is also a valuable reference for researchers and practitioners working in the areas of retail markets, quant funds, hedge funds, and time series. Also, anyone with a general interest in learning about how to profit from the financial and sports gambling markets will find this book to be a valuable resource.
Perl has a strong history of automated tests. A very early release of Perl 1.0 included a comprehensive test suite, and it's only improved from there. Learning how Perl's test tools work and how to put them together to solve all sorts of previously intractable problems can make you a better programmer in general. Besides, it's easy to use the Perl tools described to handle all sorts of testing problems that you may encounter, even in other languages.
Like all titles in O'Reilly's Developer's Notebook series, this "all lab, no lecture" book skips the boring prose and focuses instead on a series of exercises that speak to you instead of at you.
Perl Testing: A Developer's Notebook will help you dive right in and:Write basic Perl tests with ease and interpret the resultsApply special techniques and modules to improve your testsBundle test suites along with projectsTest databases and their dataTest websites and web projectsUse the "Test Anything Protocol" which tests projects written in languages other than Perl
With today's increased workloads and short development cycles, unit tests are more vital to building robust, high-quality software than ever before. Once mastered, these lessons will help you ensure low-level code correctness, reduce software development cycle time, and ease maintenance burdens.
You don't have to be a die-hard free and open source software developer who lives, breathes, and dreams Perl to use this book. You just have to want to do your job a little bit better.
Technical Challenges and Design Issues in Bangla Language Processing addresses the difficulties as well as the overwhelming benefits associated with creating programs and devices that are accessible to the speakers of the Bangla language. Professionals, students, and researchers interested in expanding the fields of computing, information and knowledge management, and communication technologies in the non-English realm will benefit from this comprehensive collection of research.
This book collects contributions from leading researchers in the area of natural language processing technology, describing their recent work and a range of new techniques and results. The book presents a state-of-the-art overview of current research in parsing tehcnologies with a focus on three important themes in the field today: dependency parsing, domain adaptation, and deep parsing.
This book is the fourth in a line of such collections, and its breadth over coverage should make it suitable both as an overview of the state of the field for graduate students, and as a reference for established researchers in Computational Linguistics, Artificial Intelligence, Computer Science, Language Engineering, Information Science, and Cognitive Science. It will also be of interest to designers, developers, and advanced users of nautral language processing systems, including applications such as spoken dialogue, text mining, multimodal human-computer interaction, and semantic web technology.