Non- mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics. Dr. Kuhn is a Director of Non-Clinical Statistics at Pfizer Global R&D in Groton Connecticut. He has been applying predictive models in the pharmaceutical and diagnostic industries for over 15 years and is the author of a number of R packages. Dr. Johnson has more than a decade of statistical consulting and predictive modeling experience in pharmaceutical research and development. He is a co-founder of Arbor Analytics, a firm specializing in predictive modeling and is a former Director of Statistics at Pfizer Global R&D. His scholarly work centers on the application and development of statistical methodology and learning algorithms. Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. Addressing practical concerns extends beyond model fitting to topics such as handling class imbalance, selecting predictors, and pinpointing causes of poor model performance-all of which are problems that occur frequently in practice. The text illustrates all parts of the modeling process through many hands-on, real-life examples. And every chapter contains extensive R code f

    It introduces first year Ph.D. students to standard graduate econometrics material from a modern perspective. It covers all the standard material necessary for understanding the principal techniques of econometrics from ordinary least squares through cointegration. The book is also distinctive in developing both time-series and cross-section analysis fully, giving the reader a unified framework for understanding and integrating results.Econometrics has many useful features and covers all the important topics in econometrics in a succinct manner. All the estimation techniques that could possibly be taught in a first-year graduate course, except maximum likelihood, are treated as special cases of GMM (generalized methods of moments). Maximum likelihood estimators for a variety of models (such as probit and tobit) are collected in a separate chapter. This arrangement enables students to learn various estimation techniques in an efficient manner. Eight of the ten chapters include a serious empirical application drawn from labor economics, industrial organization, domestic and international finance, and macroeconomics. These empirical exercises at the end of each chapter provide students a hands-on experience applying the techniques covered in the chapter. The exposition is rigorous yet accessible to students who have a working knowledge of very basic linear algebra and probability theory. All the results are stated as propositions, so that students can see the points of the discussion and also the conditions under which those results hold. Most propositions are proved in the text.For those who intend to write a thesis on applied topics, the empirical applications of the book are a good way to learn how to conduct empirical research. For the theoretically inclined, the no-compromise treatment of the basic techniques is a good preparation for more advanced theory courses.

    Continuing in the grand tradition of sabermetrics, the authors provide a revolutionary way to think about baseball with principles that can be applied at every level, from high school to the major leagues. Tom Tango, Mitchel Lichtman, and Andrew Dolphin cover topics such as batting and pitching matchups, platooning, the benefits and risks of intentional walks and sacrifices, the legitimacy of alleged clutch hitters, and many of baseball's other theories on hitting, fielding, pitching, and even baserunning. They analyze when a strategy is a good idea and when it's a bad idea, and how to more closely watch the inside game of baseball. Whenever you hear an announcer talk about the unwritten rule or say that so-and-so is going by the book in bringing in a situational substitute, The Book reviews the facts and determines what the real case is. If you want to know what the folks in baseball should be doing, find out in The Book.

    This updated edition of Ross's classic bestseller provides an introduction to elementary probability theory and stochastic processes, and shows 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. With the addition of several new sections relating to actuaries, this text is highly recommended by the Society of Actuaries.This book now contains a new section on compound random variables that can be used to establish a recursive formula for computing probability mass functions for a variety of common compounding distributions; a new section on hiddden Markov chains, including the forward and backward approaches for computing the joint probability mass function of the signals, as well as the Viterbi algorithm for determining the most likely sequence of states; and a simplified approach for analyzing nonhomogeneous Poisson processes. There are also additional results on queues relating to the conditional distribution of the number found by an M/M/1 arrival who spends a time t in the system; inspection paradox for M/M/1 queues; and M/G/1 queue with server breakdown. Furthermore, the book includes new examples and exercises, along with compulsory material for new Exam 3 of the Society of Actuaries.This book is essential reading for professionals and students in actuarial science, engineering, operations research, and other fields in applied probability.

    It also stresses the importance of ethics in research and taking the time to properly design and think through any research endeavor.

    More people know who Khloe Kardashian is than who Rene Descartes was. Most can't find Delaware on a map, correctly spell the word occurrence, or name the largest ocean on the planet. But how important is it to fill our heads with facts? A few keystrokes can summon almost any information in seconds. Why should we bother learning facts at all? Bestselling author William Poundstone confronts that timely question in Head in the Cloud. He shows that many areas of knowledge correlate with the quality of our lives -- wealth, health, and happiness -- and even with politics and behavior. Combining Big Data survey techniques with eye-opening anecdotes, Poundstone examines what Americans know (and don't know) on topics ranging from quantum physics to pop culture. Head in the Cloud asks why we're okay with spelling errors on menus but not on resumes; why Fox News viewers don't know which party controls Congress; why people who know "trivia" make more money than those who don't; how individuals can navigate clickbait and media spin to stay informed about what really matters. Hilarious, humbling, and wildly entertaining, Head in the Cloud is a must-read for anyone who doesn't know everything.

    It can also be used by English speakers and is a practical, user-friendly book intended as a fast, do-it-yourself guide for those whose English language proficiency is above intermediate. The approach is based on material developed from teaching graduate students at Imperial College London and has been extensively piloted. The book guides the reader through the process of writing science research and will also help with writing a Master's or Doctoral thesis in English.Science writing is much easier than it looks because the structure and language are conventional. The aim of this book is to help the reader discover a template or model for science research writing and then to provide the grammar and vocabulary tools needed to operate that model. There are five units: Introduction, Methodology, Results, Discussion/Conclusion and Abstract. The reader develops a model for each section of the research article through sample texts and exercises; this is followed by a Grammar and Writing Skills section designed to respond to frequently-asked questions as well as a Vocabulary list including examples of how the words and phrases are to be used.

    Bayesian statistics is taught in most first-year statistics classes across the nation, but there is one major problem that many students (and others who are interested in the theorem) face. The theorem is not intuitive for most people, and understanding how it works can be a challenge, especially because it is often taught without visual aids. In this guide, we unpack the various components of the theorem and provide a basic overview of how it works - and with illustrations to help. Three scenarios - the flu, breathalyzer tests, and peacekeeping - are used throughout the booklet to teach how problems involving Bayes Theorem can be approached and solved. Over 60 hand-drawn visuals are included throughout to help you work through each problem as you learn by example. The illustrations are simple, hand-drawn, and in black and white. For those interested, we have also included sections typically not found in other beginner guides to Bayes Rule. These include: A short tutorial on how to understand problem scenarios and find P(B), P(A), and P(B|A). For many people, knowing how to approach scenarios and break them apart can be daunting. In this booklet, we provide a quick step-by-step reference on how to confidently understand scenarios.A few examples of how to think like a Bayesian in everyday life. Bayes Rule might seem somewhat abstract, but it can be applied to many areas of life and help you make better decisions. It is a great tool that can help you with critical thinking, problem-solving, and dealing with the gray areas of life. A concise history of Bayes Rule. Bayes Theorem has a fascinating 200+ year history, and we have summed it up for you in this booklet. From its discovery in the 1700’s to its being used to break the German’s Enigma Code during World War 2, its tale is quite phenomenal.Fascinating real-life stories on how Bayes formula is used in everyday life.From search and rescue to spam filtering and driverless cars, Bayes is used in many areas of modern day life. We have summed up 3 examples for you and provided an example of how Bayes could be used.An expanded definitions, notations, and proof section.We have included an expanded definitions and notations sections at the end of the booklet. In this section we define core terms more concretely, and also cover additional terms you might be confused about. A recommended readings section.From The Theory That Would Not Die to a few other books, there are a number of recommendations we have for further reading. Take a look! If you are a visual learner and like to learn by example, this intuitive booklet might be a good fit for you. Bayesian statistics is an incredibly fascinating topic and likely touches your life every single day. It is a very important tool that is used in data analysis throughout a wide-range of industries - so take an easy dive into the theorem for yourself with a visual approach!If you are looking for a short beginners guide packed with visual examples, this booklet is for you.

    Kill the “Win.” Say goodbye to starting pitchers. And please, please stop bunting. MLB Network anchor and commentator Brian Kenny provides “an excellent, entertaining read for the all-around baseball fan” (Library Journal) and shows how baseball has been revolutionized—not destroyed—by analytical thinking. Most people who resist logical thought in baseball preach “tradition” and “respecting the game.” But many of baseball’s traditions go back to the nineteenth century, when the pitcher’s job was to provide the batter with a ball he could hit and fielders played without gloves. Instead of fearing change, Brian Kenny wants fans to think critically, reject outmoded groupthink, and embrace the changes that have come with the sabermetric era. In his entertaining and enlightening book, Kenny discusses why the pitching win-loss record, the Triple Crown, fielding errors, and so-called battling titles should be ignored. He also points out how fossilized sportswriters have been electing the wrong MVP’s and ignoring legitimate candidates for the Hall of Fame; why managers are hired based on their looks; and how the most important position in baseball may just be “Director of Decision Sciences.” “Prepare to have your brain and your assumptions challenged. Guided by data and a deep love of the game, Brian Kenny takes a cutting-edge look at where baseball is and where it is going” (Tom Verducci, Sports Illustrated). Illustrated with unique anecdotes from those who have reshaped the game, Ahead of the Curve is “a great story about the game in the age of information and technology” (Billy Beane).

    Dozens of examples in innumeracy show us how it affects not only personal economics and travel plans, but explains mis-chosen mates, inappropriate drug-testing, and the allure of pseudo-science.

    It requires, in short, an understanding of probability. In this Very Short Introduction, John Haigh introduces the ideas of probability--and the different philosophical approaches to probability--and gives a brief account of the history of development of probability theory, from Galileo and Pascal to Bayes, Laplace, Poisson, and Markov. He describes the basic probability distributions and discusses a wide range of applications in science, economics, and a variety of other contexts such as games and betting. He concludes with an intriguing discussion of coincidences and some curious paradoxes.

    Basketball on Paper doesn’t diagram plays or explain how players get in shape, but instead demonstrates how to interpret player and team performance. Dean Oliver highlights general strategies for teams when they’re winning or losing and what aspects should be the focus in either situation. He describes and quantifies the jobs of team leaders and role players, then discusses the interactions between players and how to achieve the best fit. Oliver conceptualizes the meaning of teamwork and how to quantify the value of different types of players working together. He examines historically successful NBA teams and identifies what made them so successful: individual talent, a system of putting players together, or good coaching. Oliver then uses these statistical tools and case studies to evaluate the best players in history, such as Magic Johnson, Wilt Chamberlain, Bill Russell, and Charles Barkley and how they contributed to their teams’ success. He does the same for some of the NBA’s "oddball" players-Manute Bol, Muggsy Bogues, and Dennis Rodman and for the WNBA’s top players. Basketball on Paper is unique in its incorporation of business and analytical concepts within the context of basketball to measure the value of players in a cooperative setting. Whether you’re looking for strategies or new ideas to throw out while watching the ballgame at a sports bar, Dean Oliver’sBasketball on Paper will give you amazing new insights into teamwork, coaching, and success.

    Human beings have long been both fascinated and appalled by randomness. On the one hand, we love the thrill of a surprise party, the unpredictability of a budding romance, or the freedom of not knowing what tomorrow will bring. We are inexplicably delighted by strange coincidences and striking similarities. But we also hate uncertainty's dark side. From cancer to SARS, diseases strike with no apparent pattern. Terrorists attack, airplanes crash, bridges collapse, and we never know if we'll be that one in a million statistic. We are all constantly faced with situations and choices that involve randomness and uncertainty. A basic understanding of the rules of probability theory, applied to real-life circumstances, can help us to make sense of these situations, to avoid unnecessary fear, to seize the opportunities that randomness presents to us, and to actually enjoy the uncertainties we face. The reality is that when it comes to randomness, you can run, but you can't hide. So many aspects of our lives are governed by events that are simply not in our control. In this entertaining yet sophisticated look at the world of probabilities, author Jeffrey Rosenthal--an improbably talented math professor--explains the mechanics of randomness and teaches us how to develop an informed perspective on probability.

    And yet, we are the safest and healthiest humans in history. Irrational fear seems to be taking over, often with tragic results. For example, in the months after 9/11, when people decided to drive instead of fly—believing they were avoiding risk—road deaths rose by more than 1,500. In this fascinating, lucid, and thoroughly entertaining examination of how humans process risk, journalist Dan Gardner had the exclusive cooperation of Paul Slovic, the world renowned risk-science pioneer, as he reveals how our hunter gatherer brains struggle to make sense of a world utterly unlike the one that made them. Filled with illuminating real world examples, interviews with experts, and fast-paced, lean storytelling, The Science of Fear shows why it is truer than ever that the worst thing we have to fear is fear itself.

    With boundless erudition and in delightfully clear prose, Surowiecki ranges across fields as diverse as popular culture, psychology, ant biology, behavioral economics, artificial intelligence, military history, and politics to show how this simple idea offers important lessons for how we live our lives, select our leaders, run our companies, and think about our world.