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May contain lies : how stories, statistics, and studies exploit our biases - and what we can do about it
\"Our lives are minefields of misinformation. It ripples through our social media feeds, our daily headlines, and the pronouncements of politicians, executives, and authors. Stories, statistics, and studies are everywhere, allowing people to find evidence to support whatever position they want. Many of these sources are flawed, yet by playing on our emotions and preying on our biases, they can gain widespread acceptance, warp our views, and distort our decisions. In this eye-opening book, renowned economist Alex Edmans teaches us how to separate fact from fiction. Using colorful examples--from a wellness guru's tragic but fabricated backstory to the blunders that led to the Deepwater Horizon disaster to the diet that ensnared millions yet hastened its founder's death--Edmans highlights the biases that cause us to mistake statements for facts, facts for data, data for evidence, and evidence for proof. Armed with the knowledge of what to guard against, he then provides a practical guide to combat this tide of misinformation. Going beyond simply checking the facts and explaining individual statistics, Edmans explores the relationships between statistics--the science of cause and effect--ultimately training us to think smarter, sharper, and more critically. May Contain Lies is an essential read for anyone who wants to make better sense of the world and better decisions\"-- Provided by publisher.
Book
Medical statistics
Holistic approach to understanding medical statistics. This hands-on guide is much more than a basic medical statistics introduction. It equips you with the statistical tools required for evidence-based clinical research. Each chapter provides a clear step-by-step guide to each statistical test with practical instructions on how to generate and interpret the numbers, and present the results as scientific tables or graphs. Showing you how to:.:.; analyse data with the help of data set examples (Click here to download datasets).; select the correct statistics and report results for publication o.
eBook
Coming to Terms with Chance
The application of probability and statistics to an ever-widening number of life-decisions serves to reproduce, reinforce, and widen disparities in the quality of life that different groups of people can enjoy. As a critical technology assessment, the ways in which bad luck early in life increase the probability that hardship and loss will accumulate across the life course are illustrated. Analysis shows the ways in which individual decisions, informed by statistical models, shape the opportunities people face in both market and non-market environments. Ultimately, this book challenges the actuarial logic and instrumental rationalism that drives public policy and emphasizes the role that the mass media play in justifying its expanded use. Although its arguments and examples take as their primary emphasis the ways in which these decision systems affect the life chances of African-Americans, the findings are also applicable to a broad range of groups burdened by discrimination.
eBook
Handbook of statistical analysis and data mining applications
The Handbook of Statistical Analysis and Data Mining Applications is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers (both academic and industrial) through all stages of data analysis, model building and implementation. The Handbook helps one discern the technical and business problem, understand the strengths and weaknesses of modern data mining algorithms, and employ the right statistical methods for practical application. Use this book to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques, and discusses their application to real problems, in ways accessible and beneficial to practitioners across industries - from science and engineering, to medicine, academia and commerce. This handbook brings together, in a single resource, all the information a beginner will need to understand the tools and issues in data mining to build successful data mining solutions.Written \"By Practitioners for Practitioners\" Non-technical explanations build understanding without jargon and equations Tutorials in numerous fields of study provide step-by-step instruction on how to use supplied tools to build models Practical advice from successful real-world implementations Includes extensive case studies, examples, MS PowerPoint slides and datasets CD-DVD with valuable fully-working 90-day software included: \"Complete Data Miner - QC-Miner - Text Miner\" bound with book
eBook
Business statistics
Shows how statistical ideas, techniques, formulas, and calculations apply to the world of global business and economics. You'll get an introduction on sampling and graphs, and discover how statistics are used in daily life.
Book
An Introduction to Model-Based Survey Sampling with Applications
This book is an introduction to the model-based approach to survey sampling. It consists of three parts, with Part I focusing on estimation of population totals. Chapters 1 and 2 introduce survey sampling, and the model-based approach, respectively. Chapter 3 considers the simplest possible model, the homogenous population model, which is then extended to stratified populations in Chapter 4. Chapter 5 discusses simple linear regression models for populations, and Chapter 6 considers clustered populations. The general linear population model is then used to integrate these results in Chapter 7. Part II of this book considers the properties of estimators based on incorrectly specified models. Chapter 8 develops robust sample designs that lead to unbiased predictors under model misspecification, and shows how flexible modelling methods like non-parametric regression can be used in survey sampling. Chapter 9 extends this development to misspecfication robust prediction variance
estimators and Chapter 10 completes Part II of the book with an exploration of outlier robust sample survey estimation. Chapters 11 to 17 constitute Part III of the book and show how model-based methods can be used in a variety of problem areas of modern survey sampling. They cover (in order) prediction of non-linear population quantities, sub-sampling approaches to prediction variance estimation, design and estimation for multipurpose surveys, prediction for domains, small area estimation, efficient prediction of population distribution functions and the use of transformations in survey inference. The book is designed to be accessible to undergraduate and graduate level students with a good grounding in statistics and applied survey statisticians seeking an introduction to model-based survey design and estimation.
eBook
Spatial analysis for radar remote sensing of tropical forests
\"This book is based on authors' extensive involvement in large Synthetic Aperture Radar (SAR) mapping projects, targeting the health of an important earth ecosystem, the tropical forests. It highlights past achievements, explains the underlying physics that allow the radar practitioners to understand what radars image, and can't yet image, and paves the way for future developments including wavelet-based techniques to estimate tropical forest structural measures combined with InSAR and Lidar techniques. As first book on this topic, this composite approach makes it appealing for students, learning through important case studies ; and for researchers finding new ideas for future studies\"-- Provided by publisher.
Book
The Theory That Would Not Die
Bayes' rule appears to be a straightforward, one-line theorem: by updating our initial beliefs with objective new information, we get a new and improved belief. To its adherents, it is an elegant statement about learning from experience. To its opponents, it is subjectivity run amok.
In the first-ever account of Bayes' rule for general readers, Sharon Bertsch McGrayne explores this controversial theorem and the human obsessions surrounding it. She traces its discovery by an amateur mathematician in the 1740s through its development into roughly its modern form by French scientist Pierre Simon Laplace. She reveals why respected statisticians rendered it professionally taboo for 150 years-at the same time that practitioners relied on it to solve crises involving great uncertainty and scanty information (Alan Turing's role in breaking Germany's Enigma code during World War II), and explains how the advent of off-the-shelf computer technology in the 1980s proved to be a game-changer. Today, Bayes' rule is used everywhere from DNA de-coding to Homeland Security.
Drawing on primary source material and interviews with statisticians and other scientists,The Theory That Would Not Dieis the riveting account of how a seemingly simple theorem ignited one of the greatest controversies of all time.
eBook