Learn both the theory and practice of pairs trading, why it is consistently profitable, and how you can apply the strategies in your own trading with this valuable guide. Author Douglas Ehrman covers pairs trading involving stocks, options on stocks, and futures contracts, and explains how this type of trading allows you to profit from the changing price relationship of securities. In addition to a comprehensive discussion of the theories involved, he also includes practical examples that will to help you put what you've learned into practice. Douglas S. Ehrman is a hedge fund manager and a leading authority on pairs trading. He is one of the founders and the Chief Executive Officer of AlphAmerica Asset Management LLC in Chicago. He also served as the chief executive officer of AlphAmerica Financial, Inc., the company that operated PairsTrading.com prior to its merger with PairTrader.com.

The thoroughly revised and updated Third Edition of the acclaimed Modern Epidemiology reflects both the conceptual development of this evolving science and the increasingly focal role that epidemiology plays in dealing with public health and medical problems. Coauthored by three leading epidemiologists, with sixteen additional contributors, this Third Edition is the most comprehensive and cohesive text on the principles and methods of epidemiologic research. The book covers a broad range of concepts and methods, such as basic measures of disease frequency and associations, study design, field methods, threats to validity, and assessing precision. It also covers advanced topics in data analysis such as Bayesian analysis, bias analysis, and hierarchical regression. Chapters examine specific areas of research such as disease surveillance, ecologic studies, social epidemiology, infectious disease epidemiology, genetic and molecular epidemiology, nutritional epidemiology, environmental epidemiology, reproductive epidemiology, and clinical epidemiology.

The fourth edition of An Introduction to Statistical Problem Solving in Geography continues its standing as the definitive introduction to statistics and quantitative analysis in geography. Assuming no reader background in statistics, the authors lay out the proper role of statistical analysis and methods in human and physical geography. They delve into the calculation of descriptive summaries and graphics to explain geographic patterns and use inferential statistics (parametric and nonparametric) to test for differences (t-tests, ANOVA), relationships (regression and correlation), and spatial statistics (point and area patterns, spatial autocorrelation). This edition introduces more advanced topics, including logistic regression, two-factor ANOVA, and spatial estimation (inverse distance weighting, Kriging). Many chapters also include thought-provoking discussions of statistical concepts as they relate to the COVID-19 pandemic. Maintaining an exploratory and investigative approach throughout, the authors provide readers with real-world geographic issues and more than 50 map examples. Concepts are explained clearly and narratively without oversimplification. Each chapter concludes with a list of major goals and objectives. An epilogue offers over 150 open-ended geographic situations, inviting students to apply their new statistical skills to solve problems currently affecting our world.

This book provides an elementary-level introduction to R, targeting both non-statistician scientists in various fields and students of statistics. The main mode of presentation is via code examples with liberal commenting of the code and the output, from the computational as well as the statistical viewpoint. Brief sections introduce the statistical methods before they are used. A supplementary R package can be downloaded and contains the data sets. All examples are directly runnable and all graphics in the text are generated from the examples. The statistical methodology covered includes statistical standard distributions, one- and two-sample tests with continuous data, regression analysis, one-and two-way analysis of variance, regression analysis, analysis of tabular data, and sample size calculations. In addition, the last four chapters contain introductions to multiple linear regression analysis, linear models in general, logistic regression, and survival analysis.

An observational study infers the effects caused by a treatment, policy, program, intervention, or exposure in a context in which randomized experimentation is unethical or impractical. One task in an observational study is to adjust for visible pretreatment differences between the treated and control groups. Multivariate matching and weighting are two modern forms of adjustment. This handbook provides a comprehensive survey of the most recent methods of adjustment by matching, weighting, machine learning and their combinations. Three additional chapters introduce the steps from association to causation that follow after adjustments are complete. When used alone, matching and weighting do not use outcome information, so they are part of the design of an observational study. When used in conjunction with models for the outcome, matching and weighting may enhance the robustness of model-based adjustments. The book is for researchers in medicine, economics, public health, psychology, epidemiology, public program evaluation, and statistics who examine evidence of the effects on human beings of treatments, policies or exposures.

Core Statistical Concepts with Excel® connects statistical concepts to applications with Excel® using practical research examples. The text jointly promotes an understanding of Excel® and a deeper knowledge of core concepts through practice. Authors Gregory J. Privitera and Darryl Mayeaux provide students step-by-step instruction for using Excel® software as a useful tool not only to manage but also analyze data—all through the use of key themes, features, and pedagogy: an emphasis on student learning, a focus on current research, and integration of Excel® to introduce statistical concepts.

Over the past several years, privately run, publicly funded charter schools have been sold to the American public as an education alternative promising better student achievement, greater parent satisfaction, and more vibrant school communities. But are charter schools delivering on their promise? Or are they just hype as critics contend, a costly experiment that is bleeding tax dollars from public schools? In this book, Jack Buckley and Mark Schneider tackle these questions about one of the thorniest policy reforms in the nation today. Using an exceptionally rigorous research approach, the authors investigate charter schools in Washington, D.C., carefully examining school data going back more than a decade, interpreting scores of interviews with parents, students, and teachers, and meticulously measuring how charter schools perform compared to traditional public schools. Their conclusions are sobering. Buckley and Schneider show that charter-school students are not outperforming students in traditional public schools, that the quality of charter-school education varies widely from school to school, and that parent enthusiasm for charter schools starts out strong but fades over time. And they argue that while charter schools may meet the most basic test of sound public policy--they do no harm--the evidence suggests they all too often fall short of advocates' claims. With the future of charter schools--and perhaps public education as a whole--hanging in the balance, this book supports the case for holding charter schools more accountable and brings us considerably nearer to resolving this contentious debate.

Written for undergraduate geography majors and entry-level graduate students with limited backgrounds in statistical analysis and methods, McGrew and Monroe provide a comprehensive and understandable introduction to statistical methods in a problem-solving framework. Engaging examples and problems are drawn from a variety of topical areas in both human and physical geography and are fully integrated into the text. Without compromising statistical rigor or oversimplifying, the authors stress the importance of written narratives that explain each statistical technique. After introducing basic statistical concepts and terminology, the authors focus on nonspatial and spatial descriptive statistics. They transition to inferential problem solving, including probability, sampling, and estimation, before delving deeper into inferential statistics for geographic problem solving. The final chapters examine the related techniques of correlation and regression. A list of major goals and objectives is included at the end of each chapter, allowing students to monitor their own progress and mastery of geographic statistical materials. An epilogue, offering over 150 geographic situations, gives students a chance to figure out which statistical technique should be used for a particular situation.

Following in the footsteps of its bestselling predecessors, the Handbook of Parametric and Nonparametric Statistical Procedures, Fifth Edition provides researchers, teachers, and students with an all-inclusive reference on univariate, bivariate, and multivariate statistical procedures.New in the Fifth Edition:Substantial updates and new material th

The Second Edition of the award-winning Research Methods for the Behavioral Sciences employs a problem-focused approach that fully integrates the decision tree—from choosing a research design to conducting statistical analysis and communicating results. With a conversational, student-friendly writing style, Gregory J. Privitera shows how methods and analysis work together and enable the testing of hypotheses through use of the scientific method. Outstanding pedagogy, current examples, and robust resources empower students to approach their study and application of research methods with confidence.