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Twenty-four million people wager nearly $3 billion on college basketball pools each year, but few are aware that winning strategies have been developed by researchers at Harvard, Yale, and other universities over the past two decades. Bad advice from media sources and even our own psychological inclinations are often a bigger obstacle to winning than our pool opponents. Profit opportunities are missed and most brackets submitted to pools don’t have a breakeven chance to win money before the tournament begins. Improving Your NCAA® Bracket with Statistics is both an easy-to-use tip sheet to improve your winning odds and an intellectual history of how statistical reasoning has been applied to the bracket pool using standard and innovative methods. It covers bracket improvement methods ranging from those that require only the information in the seeded bracket to sophisticated estimation techniques available via online simulations. Included are: Prominently displayed bracket improvement tips based on the published research A history of the origins of the bracket pool A history of bracket improvement methods and their results in play Historical sketches and background information on the mathematical and statistical methods that have been used in bracket analysis A source list of good bracket pool advice available each year that seeks to be comprehensive Warnings about common bad advice that will hurt your chances Tom Adams’ work presenting bracket improvement methods has been featured in the New York Times, Sports Illustrated, and SmartMoney magazine.
Twenty-four million people wager nearly $3 billion on college basketball pools each year, but few are aware that winning strategies have been developed by researchers at Harvard, Yale, and other universities over the past two decades. Bad advice from media sources and even our own psychological inclinations are often a bigger obstacle to winning than our pool opponents. Profit opportunities are missed and most brackets submitted to pools don’t have a breakeven chance to win money before the tournament begins. Improving Your NCAA® Bracket with Statistics is both an easy-to-use tip sheet to improve your winning odds and an intellectual history of how statistical reasoning has been applied to the bracket pool using standard and innovative methods. It covers bracket improvement methods ranging from those that require only the information in the seeded bracket to sophisticated estimation techniques available via online simulations. Included are: Prominently displayed bracket improvement tips based on the published research A history of the origins of the bracket pool A history of bracket improvement methods and their results in play Historical sketches and background information on the mathematical and statistical methods that have been used in bracket analysis A source list of good bracket pool advice available each year that seeks to be comprehensive Warnings about common bad advice that will hurt your chances Tom Adams’ work presenting bracket improvement methods has been featured in the New York Times, Sports Illustrated, and SmartMoney magazine.
Twenty-four million people wager nearly $3 billion on college basketball pools each year, but few are aware that winning strategies have been developed by researchers at Harvard, Yale, and other universities over the past two decades. Bad advice from media sources and even our own psychological inclinations are often a bigger obstacle to winning than our pool opponents. Profit opportunities are missed and most brackets submitted to pools don't have a breakeven chance to win money before the tournament begins. Improving Your NCAA® Bracket with Statistics is both an easy-to-use tip sheet to improve your winning odds and an intellectual history of how statistical reasoning has been applied to the bracket pool using standard and innovative methods. It covers bracket improvement methods ranging from those that require only the information in the seeded bracket to sophisticated estimation techniques available via online simulations. Included are: Prominently displayed bracket improvement tips based on the published research A history of the origins of the bracket pool A history of bracket improvement methods and their results in play Historical sketches and background information on the mathematical and statistical methods that have been used in bracket analysis A source list of good bracket pool advice available each year that seeks to be comprehensive Warnings about common bad advice that will hurt your chances Tom Adams' work presenting bracket improvement methods has been featured in the New York Times, Sports Illustrated, and SmartMoney magazine.
How do you learn about what’s going on in the world? Did a news headline grab your attention? Did a news story report on recent research? What do you need to know to be a critical consumer of the news you read? If you are looking to start developing your data self-defense and critical news consumption skills, this book is for you! It reflects a long-term collaboration between a statistician and a journalist to shed light on the statistics behind the stories and the stories behind the statistics. The only prerequisite for enjoying this book is an interest in developing the skills and insights for better understanding news stories that incorporate quantitative information. Chapters in Statistics Behind the Headlines kick off with a news story headline and a summary of the story itself. The meat of each chapter consists of an exploration of the statistical and journalism concepts needed to understand the data analyzed and reported in the story. The chapters are organized around these sections: What ideas will you encounter in this chapter? What is claimed? Is it appropriate? Who is claiming this? Why is it claimed? What makes this a story worth telling? Is this a good measure of impact? How is the claim supported? What evidence is reported? What is the quality/strength of the evidence? Does the claim seem reasonable? How does this claim fit with what is already known? How much does this matter? Considering the coverage Chapters close with connections to the Stats + Stories podcast.
Lunardi delves into the early days of Bracketology, details its growth, and dispels the myths of the process The NCAA Tournament has become one of the most popular sports events in the country, consuming fans for weeks with the run to the Final Four and ultimately the crowning of the champion of college hoops.? Each March, millions of Americans fill out their bracket in the hopes of correctly predicting the future. Yet, there is no true Madness without the oft-debated question about what teams should be seeded where—from the Power-5 Blue Blood with some early season stumbles on their resume to the mid-major that rampaged through their less competitive conference season—and the inventor of Bracketology himself, Joe Lunardi, now reveals the mystery and science behind the legend. While going in depth on his ever-evolving predictive formula, Lunardi compares great teams from different eras with intriguing results, talks to the biggest names in college basketball about their perception of Bracketology (both good and bad), and looks ahead to the future of the sport and how Bracketology will help shape the conversation. This fascinating book is a must-read for college hoops fans and anyone who has aspired to win their yearly office pool.
Statistics and Health Care Fraud: How to Save Billions helps the public to become more informed citizens through discussions of real world health care examples and fraud assessment applications. The author presents statistical and analytical methods used in health care fraud audits without requiring any mathematical background. The public suffers from health care overpayments either directly as patients or indirectly as taxpayers, and fraud analytics provides ways to handle the large size and complexity of these claims. The book starts with a brief overview of global healthcare systems such as U.S. Medicare. This is followed by a discussion of medical overpayments and assessment initiatives using a variety of real world examples. The book covers subjects as: • Description and visualization of medical claims data • Prediction of fraudulent transactions • Detection of excessive billings • Revealing new fraud patterns • Challenges and opportunities with health care fraud analytics Dr. Tahir Ekin is the Brandon Dee Roberts Associate Professor of Quantitative Methods in McCoy College of Business, Texas State University. His previous work experience includes a working as a statistician on health care fraud detection. His scholarly work on health care fraud has been published in a variety of academic journals including International Statistical Review, The American Statistician, and Applied Stochastic Models in Business and Industry. He is a recipient of the Texas State University 2018 Presidential Distinction Award in Scholar Activities and the ASA/NISS y-Bis 2016 Best Paper Awards. He has developed and taught courses in the areas of business statistics, optimization, data mining and analytics. Dr. Ekin also serves as Vice President of the International Society for Business and Industrial Statistics.
At what point does the sacrifice to our personal information outweigh the public good? If public policymakers had access to our personal and confidential data, they could make more evidence-based, data-informed decisions that could accelerate economic recovery and improve COVID-19 vaccine distribution. However, access to personal data comes at a steep privacy cost for contributors, especially underrepresented groups. Protecting Your Privacy in a Data-Driven World is a practical, nontechnical guide that explains the importance of balancing these competing needs and calls for careful consideration of how data are collected and disseminated by our government and the private sector. Not addressing these concerns can harm the same communities policymakers are trying to protect through data privacy and confidentiality legislation.
Bayesian Statistical Methods provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis. This book focuses on Bayesian methods applied routinely in practice including multiple linear regression, mixed effects models and generalized linear models (GLM). The authors include many examples with complete R code and comparisons with analogous frequentist procedures. In addition to the basic concepts of Bayesian inferential methods, the book covers many general topics: Advice on selecting prior distributions Computational methods including Markov chain Monte Carlo (MCMC) Model-comparison and goodness-of-fit measures, including sensitivity to priors Frequentist properties of Bayesian methods Case studies covering advanced topics illustrate the flexibility of the Bayesian approach: Semiparametric regression Handling of missing data using predictive distributions Priors for high-dimensional regression models Computational techniques for large datasets Spatial data analysis The advanced topics are presented with sufficient conceptual depth that the reader will be able to carry out such analysis and argue the relative merits of Bayesian and classical methods. A repository of R code, motivating data sets, and complete data analyses are available on the book’s website. Brian J. Reich, Associate Professor of Statistics at North Carolina State University, is currently the editor-in-chief of the Journal of Agricultural, Biological, and Environmental Statistics and was awarded the LeRoy & Elva Martin Teaching Award. Sujit K. Ghosh, Professor of Statistics at North Carolina State University, has over 22 years of research and teaching experience in conducting Bayesian analyses, received the Cavell Brownie mentoring award, and served as the Deputy Director at the Statistical and Applied Mathematical Sciences Institute.
Demonstrates how modern statistical techniques can measure the impact of counterfactual decisions. Examines the importance of counterfactual reasoning for both modern scholars and historical actors. Combines historical narrative, mathematical precision and data to create a straightforward presentation of both factual and counterfactual military history. Provides an original contribution to the debate over the validity and rigour of works of counterfactual history Written in a manner accessible to readers who have no formal training in History or Statistics.