Probable outcomes continues the Crestmont Research tradition of extensive full-color charts and graphs that enable investors and advisors to differentiate between irrational hope and a rational view of the stock market. This book's empowering insights prepare you to take action during the current period of below -average returns. The unique combination of investment science and investment art explores the market from several perspectives and addresses the significant implications for a broad range of investors. Beyond concepts, Ed Easterling delivers a dramatic analysis of the likely course for the stock market over the 2010 decade. Investors and advisors will benefit from this timely outlook and its message of reasonable expectations and value-added investing. This essential resource offers a compelling understanding of the key fundamental principles that drive the stock market. Derived from years of meticulous research, Probably outcomes provides sensible conclusions that will guide your future investment choices and allow you to invest with confidence, whatever your financial strategy."--

Read this book in order to learn: Why medicines often fail to produce the desired result and how such failures can be avoided How to think about drug product safety and effectiveness How the main participants in a medications use system can improve outcomes and how professional and personal values, attitudes, and ethical reasoning fit into

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.

Biostatistics with R is designed around the dynamic interplay among statistical methods, their applications in biology, and their implementation. The book explains basic statistical concepts with a simple yet rigorous language. The development of ideas is in the context of real applied problems, for which step-by-step instructions for using R and R-Commander are provided. Topics include data exploration, estimation, hypothesis testing, linear regression analysis, and clustering with two appendices on installing and using R and R-Commander. A novel feature of this book is an introduction to Bayesian analysis. This author discusses basic statistical analysis through a series of biological examples using R and R-Commander as computational tools. The book is ideal for instructors of basic statistics for biologists and other health scientists. The step-by-step application of statistical methods discussed in this book allows readers, who are interested in statistics and its application in biology, to use the book as a self-learning text.

This comprehensive book is an introduction to multilevel Bayesian models in R using brms and the Stan programming language. Featuring a series of fully worked analyses of repeated measures data, the focus is placed on active learning through the analyses of the progressively more complicated models presented throughout the book. In this book, the authors offer an introduction to statistics entirely focused on repeated measures data beginning with very simple two-group comparisons and ending with multinomial regression models with many ‘random effects’. Across 13 well-structured chapters, readers are provided with all the code necessary to run all the analyses and make all the plots in the book, as well as useful examples of how to interpret and write up their own analyses. This book provides an accessible introduction for readers in any field, with any level of statistical background. Senior undergraduate students, graduate students, and experienced researchers looking to ‘translate’ their skills with more traditional models to a Bayesian framework will benefit greatly from the lessons in this text.

The new Agency for Health Care Policy and Research in the U.S. Public Health Service is funding a set of multidisciplinary groups called Patient Outcomes Research Teams (PORTs). Their purpose is to assess alternative treatments for medical conditions using a variety of outcome measures. In guiding insurance coverage, these PORTs are expected to wield considerable influence on medical practice and health policy. This book addresses possible threats to their credibility that might be based on real or apparent conflicts of interest, including both financial and other conflicts. It raises points to consider for the new agency, for PORTs and their institutions, for industry, for the health services research community, and for the U.S. Congress in avoiding and managing conflicts of interest.