The book will begin by describing the feature set of WSUS, and the benefits it provides to system administrators. Next, the reader will learn the steps that must be taken to configure their servers and workstations to make the compatible with WSUS. A special section then follows to help readers migrate from Microsoft’s earlier update service, Software Update Service (SUS) to WSUS. The next chapters will then address the particular needs and complexities of managing WSUS on an enterprise network. Although WSUS is designed to streamline the update process, this service can still be a challenge for administrators to use effectively. To address these issues, the next chapters deal specifically with common problems that occur and the reader is provides with invaluable troubleshooting information. One of the other primary objectives of WSUS is to improve the overall security of Windows networks by ensuring that all systems have the most recent security updates and patches. To help achieve this goal, the next sections cover securing WSUS itself, so that critical security patches are always applied and cannot be compromised by malicious hackers.
* Only book available on Microsoft's brand new, Windows Server Update Services
* Employs Syngress' proven "How to Cheat" methodology providing readers with everything they need and nothing they don't
* WSUS works with every Microsoft product, meaning any system administrator running a Windows-based network is a potential customer for this book
Ebola, SARS, Hendra, AIDS, and countless other deadly viruses all have one thing in common: the bugs that transmit these diseases all originate in wild animals and pass to humans by a process called spillover. In this gripping account, David Quammen takes the reader along on this astonishing quest to learn how, where from, and why these diseases emerge and asks the terrifying question: What might the next big one be?
New York Times Bestseller
“Not so different in spirit from the way public intellectuals like John Kenneth Galbraith once shaped discussions of economic policy and public figures like Walter Cronkite helped sway opinion on the Vietnam War…could turn out to be one of the more momentous books of the decade.”
—New York Times Book Review
"Nate Silver's The Signal and the Noise is The Soul of a New Machine for the 21st century."
—Rachel Maddow, author of Drift
"A serious treatise about the craft of prediction—without academic mathematics—cheerily aimed at lay readers. Silver's coverage is polymathic, ranging from poker and earthquakes to climate change and terrorism."
—New York Review of Books
Nate Silver built an innovative system for predicting baseball performance, predicted the 2008 election within a hair’s breadth, and became a national sensation as a blogger—all by the time he was thirty. He solidified his standing as the nation's foremost political forecaster with his near perfect prediction of the 2012 election. Silver is the founder and editor in chief of FiveThirtyEight.com.
Drawing on his own groundbreaking work, Silver examines the world of prediction, investigating how we can distinguish a true signal from a universe of noisy data. Most predictions fail, often at great cost to society, because most of us have a poor understanding of probability and uncertainty. Both experts and laypeople mistake more confident predictions for more accurate ones. But overconfidence is often the reason for failure. If our appreciation of uncertainty improves, our predictions can get better too. This is the “prediction paradox”: The more humility we have about our ability to make predictions, the more successful we can be in planning for the future.
In keeping with his own aim to seek truth from data, Silver visits the most successful forecasters in a range of areas, from hurricanes to baseball, from the poker table to the stock market, from Capitol Hill to the NBA. He explains and evaluates how these forecasters think and what bonds they share. What lies behind their success? Are they good—or just lucky? What patterns have they unraveled? And are their forecasts really right? He explores unanticipated commonalities and exposes unexpected juxtapositions. And sometimes, it is not so much how good a prediction is in an absolute sense that matters but how good it is relative to the competition. In other cases, prediction is still a very rudimentary—and dangerous—science.
Silver observes that the most accurate forecasters tend to have a superior command of probability, and they tend to be both humble and hardworking. They distinguish the predictable from the unpredictable, and they notice a thousand little details that lead them closer to the truth. Because of their appreciation of probability, they can distinguish the signal from the noise.
With everything from the health of the global economy to our ability to fight terrorism dependent on the quality of our predictions, Nate Silver’s insights are an essential read.
From the Trade Paperback edition.
Over the past fifty years, more than three hundred infectious diseases have either newly emerged or reemerged, appearing in territories where they’ve never been seen before. Ninety percent of epidemiologists expect that one of them will cause a deadly pandemic sometime in the next two generations. It could be Ebola, avian flu, a drug-resistant superbug, or something completely new. While we can’t know which pathogen will cause the next pandemic, by unraveling the story of how pathogens have caused pandemics in the past, we can make predictions about the future. In Pandemic: Tracking Contagions, from Cholera to Ebola and Beyond, the prizewinning journalist Sonia Shah—whose book on malaria, The Fever, was called a “tour-de-force history” (The New York Times) and “revelatory” (The New Republic)—interweaves history, original reportage, and personal narrative to explore the origins of contagions, drawing parallels between cholera, one of history’s most deadly and disruptive pandemic-causing pathogens, and the new diseases that stalk humankind today.
To reveal how a new pandemic might develop, Sonia Shah tracks each stage of cholera’s dramatic journey, from its emergence in the South Asian hinterlands as a harmless microbe to its rapid dispersal across the nineteenth-century world, all the way to its latest beachhead in Haiti. Along the way she reports on the pathogens now following in cholera’s footsteps, from the MRSA bacterium that besieges her own family to the never-before-seen killers coming out of China’s wet markets, the surgical wards of New Delhi, and the suburban backyards of the East Coast.
By delving into the convoluted science, strange politics, and checkered history of one of the world’s deadliest diseases, Pandemic reveals what the next global contagion might look like— and what we can do to prevent it.
Features of the Fourth Edition include:New material on sample size calculations for chance-corrected agreement coefficients, as well as for intraclass correlation coefficients. The researcher will be able to determine the optimal number raters, subjects, and trials per subject.The chapter entitled “Benchmarking Inter-Rater Reliability Coefficients” has been entirely rewritten.The introductory chapter has been substantially expanded to explore possible definitions of the notion of inter-rater reliability.All chapters have been revised to a large extent to improve their readability.
". . . [this book] should be on the shelf of everyone interested in . . . longitudinal data analysis."
—Journal of the American Statistical Association
Features newly developed topics and applications of the analysis of longitudinal data
Applied Longitudinal Analysis, Second Edition presents modern methods for analyzing data from longitudinal studies and now features the latest state-of-the-art techniques. The book emphasizes practical, rather than theoretical, aspects of methods for the analysis of diverse types of longitudinal data that can be applied across various fields of study, from the health and medical sciences to the social and behavioral sciences.
The authors incorporate their extensive academic and research experience along with various updates that have been made in response to reader feedback. The Second Edition features six newly added chapters that explore topics currently evolving in the field, including:Fixed effects and mixed effects models Marginal models and generalized estimating equations Approximate methods for generalized linear mixed effects models Multiple imputation and inverse probability weighted methods Smoothing methods for longitudinal data Sample size and power
Each chapter presents methods in the setting of applications to data sets drawn from the health sciences. New problem sets have been added to many chapters, and a related website features sample programs and computer output using SAS, Stata, and R, as well as data sets and supplemental slides to facilitate a complete understanding of the material.
With its strong emphasis on multidisciplinary applications and the interpretation of results, Applied Longitudinal Analysis, Second Edition is an excellent book for courses on statistics in the health and medical sciences at the upper-undergraduate and graduate levels. The book also serves as a valuable reference for researchers and professionals in the medical, public health, and pharmaceutical fields as well as those in social and behavioral sciences who would like to learn more about analyzing longitudinal data.
This thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables.
Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-the-art techniques for building, interpreting, and assessing the performance of LR models. New and updated features include:A chapter on the analysis of correlated outcome data A wealth of additional material for topics ranging from Bayesian methods to assessing model fit Rich data sets from real-world studies that demonstrate each method under discussion Detailed examples and interpretation of the presented results as well as exercises throughout
Applied Logistic Regression, Third Edition is a must-have guide for professionals and researchers who need to model nominal or ordinal scaled outcome variables in public health, medicine, and the social sciences as well as a wide range of other fields and disciplines.
Learn to evaluate and apply statistics in medicine, medical research, and all health-related fields.Emphasis on the basics of biostatistics and epidemiology and the clinical applications in evidence-based medicine and decision-making methods NEW chapter on survey research Expanded discussion of logistic regression, the Cox model, and other multivariate statistical methods Key Concepts in each chapter pinpoint essential information Presenting Problems drawn from studies in the medical literature that illustrate the various statistical methods Downloadable NCSS statistical software, procedures, and data sets from the presenting problems End-of-chapter exercises Multiple-choice final practice exam
These may not sound like typical questions for an econo-mist to ask. But Steven D. Levitt is not a typical economist. He is a much-heralded scholar who studies the riddles of everyday life—from cheating and crime to sports and child-rearing—and whose conclusions turn conventional wisdom on its head.
Freakonomics is a groundbreaking collaboration between Levitt and Stephen J. Dubner, an award-winning author and journalist. They usually begin with a mountain of data and a simple question. Some of these questions concern life-and-death issues; others have an admittedly freakish quality. Thus the new field of study contained in this book: freakonomics.
Through forceful storytelling and wry insight, Levitt and Dubner show that economics is, at root, the study of incentives—how people get what they want, or need, especially when other people want or need the same thing. In Freakonomics, they explore the hidden side of . . . well, everything. The inner workings of a crack gang. The truth about real-estate agents. The myths of campaign finance. The telltale marks of a cheating schoolteacher. The secrets of the Klu Klux Klan.
What unites all these stories is a belief that the modern world, despite a great deal of complexity and downright deceit, is not impenetrable, is not unknowable, and—if the right questions are asked—is even more intriguing than we think. All it takes is a new way of looking.
Freakonomics establishes this unconventional premise: If morality represents how we would like the world to work, then economics represents how it actually does work. It is true that readers of this book will be armed with enough riddles and stories to last a thousand cocktail parties. But Freakonomics can provide more than that. It will literally redefine the way we view the modern world.
Bonus material added to the revised and expanded 2006 editionThe original New York Times Magazine article about Steven D. Levitt by Stephen J. Dubner, which led to the creation of this book.Seven “Freakonomics” columns written for the New York Times Magazine, published between August 2005 and April 2006.Selected entries from the Freakonomics blog, posted between April 2005 and May 2006 at http://www.freakonomics.com/blog/.
For those who slept through Stats 101, this book is a lifesaver. Wheelan strips away the arcane and technical details and focuses on the underlying intuition that drives statistical analysis. He clarifies key concepts such as inference, correlation, and regression analysis, reveals how biased or careless parties can manipulate or misrepresent data, and shows us how brilliant and creative researchers are exploiting the valuable data from natural experiments to tackle thorny questions.
And in Wheelan’s trademark style, there’s not a dull page in sight. You’ll encounter clever Schlitz Beer marketers leveraging basic probability, an International Sausage Festival illuminating the tenets of the central limit theorem, and a head-scratching choice from the famous game show Let’s Make a Deal—and you’ll come away with insights each time. With the wit, accessibility, and sheer fun that turned Naked Economics into a bestseller, Wheelan defies the odds yet again by bringing another essential, formerly unglamorous discipline to life.
Treating these topics together takes advantage of all they have in common. The authors point out the many-shared elements in the methods they present for selecting, estimating, checking, and interpreting each of these models. They also show that these regression methods deal with confounding, mediation, and interaction of causal effects in essentially the same way.
The examples, analyzed using Stata, are drawn from the biomedical context but generalize to other areas of application. While a first course in statistics is assumed, a chapter reviewing basic statistical methods is included. Some advanced topics are covered but the presentation remains intuitive. A brief introduction to regression analysis of complex surveys and notes for further reading are provided. For many students and researchers learning to use these methods, this one book may be all they need to conduct and interpret multipredictor regression analyses.
The authors are on the faculty in the Division of Biostatistics, Department of Epidemiology and Biostatistics, University of California, San Francisco, and are authors or co-authors of more than 200 methodological as well as applied papers in the biological and biomedical sciences. The senior author, Charles E. McCulloch, is head of the Division and author of Generalized Linear Mixed Models (2003), Generalized, Linear, and Mixed Models (2000), and Variance Components (1992).
From the reviews:
"This book provides a unified introduction to the regression methods listed in the title...The methods are well illustrated by data drawn from medical studies...A real strength of this book is the careful discussion of issues common to all of the multipredictor methods covered." Journal of Biopharmaceutical Statistics, 2005
"This book is not just for biostatisticians. It is, in fact, a very good, and relatively nonmathematical, overview of multipredictor regression models. Although the examples are biologically oriented, they are generally easy to understand and follow...I heartily recommend the book" Technometrics, February 2006
"Overall, the text provides an overview of regression methods that is particularly strong in its breadth of coverage and emphasis on insight in place of mathematical detail. As intended, this well-unified approach should appeal to students who learn conceptually and verbally." Journal of the American Statistical Association, March 2006
Christine Murphy has compiled a book that presents the vaccination dilemma from multiple perspectives. It clearly describes the immune system and its workings--and what science does and does not know about them. It offers suggestions and resources for parents whose children are sick, whether from a common childhood illness or from a vaccination reaction. And it makes a case for an alternate view of disease--as a teacher that allows us to develop physically and spiritually, and as a necessary test of strength that we have chosen out of our destiny.
This book will help educate parents about the vaccination dilemma and prepare them to make, in consultation with one or more health professionals, educated vaccination decisions for their children.
The real story of AIDS—how it originated with a virus in a chimpanzee, jumped to one human, and then infected more than 60 million people—is very different from what most of us think we know. Recent research has revealed dark surprises and yielded a radically new scenario of how AIDS began and spread. Excerpted and adapted from the book Spillover, with a new introduction by the author, Quammen's hair-raising investigation tracks the virus from chimp populations in the jungles of southeastern Cameroon to laboratories across the globe, as he unravels the mysteries of when, where, and under what circumstances such a consequential "spillover" can happen. An audacious search for answers amid more than a century of data, The Chimp and the River tells the haunting tale of one of the most devastating pandemics of our time.
“This book will serve to greatly complement the growing number of texts dealing with mixed models, and I highly recommend including it in one’s personal library.”
—Journal of the American Statistical Association
Mixed modeling is a crucial area of statistics, enabling the analysis of clustered and longitudinal data. Mixed Models: Theory and Applications with R, Second Edition fills a gap in existing literature between mathematical and applied statistical books by presenting a powerful examination of mixed model theory and application with special attention given to the implementation in R.
The new edition provides in-depth mathematical coverage of mixed models’ statistical properties and numerical algorithms, as well as nontraditional applications, such as regrowth curves, shapes, and images. The book features the latest topics in statistics including modeling of complex clustered or longitudinal data, modeling data with multiple sources of variation, modeling biological variety and heterogeneity, Healthy Akaike Information Criterion (HAIC), parameter multidimensionality, and statistics of image processing.
Mixed Models: Theory and Applications with R, Second Edition features unique applications of mixed model methodology, as well as:Comprehensive theoretical discussions illustrated by examples and figures Over 300 exercises, end-of-section problems, updated data sets, and R subroutines Problems and extended projects requiring simulations in R intended to reinforce material Summaries of major results and general points of discussion at the end of each chapter Open problems in mixed modeling methodology, which can be used as the basis for research or PhD dissertations
Ideal for graduate-level courses in mixed statistical modeling, the book is also an excellent reference for professionals in a range of fields, including cancer research, computer science, and engineering.
Visualize the most recent topics in cutaneous pathology such as sporothrix and cutaneous t-cell lymphoma as well as classic problems like alopecia and neurofibromatosis, informed by the latest developments in molecular biology and histologic imaging.
See current dermatologic concepts captured in the visually rich Netter artistic tradition via major new contributions from Netter disciple Carlos Machado, MD - making complex concepts easy to understand and remember through the precision, clarity, detail, and realism for which Netter’s work has always been known.
Get complete, integrated visual guidance on the skin, hair, and nails in a single source, from basic sciences and normal anatomy and function through pathologic conditions.
Adeptly navigate current controversies and timely topics in clinical medicine with guidance from the Editor and informed by an experienced international advisory board.
This volume provides formulas and procedures for determination of sample size required not only for testing equality, but also for testing non-inferiority/superiority, and equivalence (similarity) based on both untransformed (raw) data and log-transformed data under a parallel-group design or a crossover design with equal or unequal ratio of treatment allocations. It contains a comprehensive and unified presentation of statistical procedures for sample size calculation that are commonly employed at various phases of clinical development. Each chapter includes, whenever possible, real examples of clinical studies from therapeutic areas such as cardiovascular, central nervous system, anti-infective, oncology, and women's health to demonstrate the clinical and statistical concepts, interpretations, and their relationships and interactions.
The book highlights statistical procedures for sample size calculation and justification that are commonly employed in clinical research and development. It provides clear, illustrated explanations of how the derived formulas and/or statistical procedures can be used.
• Introduces requisite background to using Nonlinear Mixed Effects Modeling (NONMEM), covering data requirements, model building and evaluation, and quality control aspects
• Provides examples of nonlinear modeling concepts and estimation basics with discussion on the model building process and applications of empirical Bayesian estimates in the drug development environment
• Includes detailed chapters on data set structure, developing control streams for modeling and simulation, model applications, interpretation of NONMEM output and results, and quality control
• Has datasets, programming code, and practice exercises with solutions, available on a supplementary website
This new edition of Medical Statistics at a Glance:Presents key facts accompanied by clear and informative tables and diagrams Focuses on illustrative examples which show statistics in action, with an emphasis on the interpretation of computer data analysis rather than complex hand calculations Includes extensive cross-referencing, a comprehensive glossary of terms and flow-charts to make it easier to choose appropriate tests Now provides the learning objectives for each chapter Includes a new chapter on Developing Prognostic Scores Includes new or expanded material on study management, multi-centre studies, sequential trials, bias and different methods to remove confounding in observational studies, multiple comparisons, ROC curves and checking assumptions in a logistic regression analysis The companion website at www.medstatsaag.com contains supplementary material including an extensive reference list and multiple choice questions (MCQs) with interactive answers for self-assessment.
Medical Statistics at a Glance will appeal to all medical students, junior doctors and researchers in biomedical and pharmaceutical disciplines.
Reviews of the previous editions
"The more familiar I have become with this book, the more I appreciate the clear presentation and unthreatening prose. It is now a valuable companion to my formal statistics course."
–International Journal of Epidemiology
"I heartily recommend it, especially to first years, but it's equally appropriate for an intercalated BSc or Postgraduate research. If statistics give you headaches - buy it. If statistics are all you think about - buy it."
"...I unreservedly recommend this book to all medical students, especially those that dislike reading reams of text. This is one book that will not sit on your shelf collecting dust once you have graduated and will also function as a reference book."
–4th Year Medical Student, Barts and the London Chronicle, Spring 2003
Since the third edition, there have been many developments in statistical techniques. The fourth edition provides the medical statistician with an accessible guide to these techniques and to reflect the extent of their usage in medical research.
The new edition takes a much more comprehensive approach to its subject. There has been a radical reorganization of the text to improve the continuity and cohesion of the presentation and to extend the scope by covering many new ideas now being introduced into the analysis of medical research data. The authors have tried to maintain the modest level of mathematical exposition that characterized the earlier editions, essentially confining the mathematics to the statement of algebraic formulae rather than pursuing mathematical proofs.
Received the Highly Commended Certificate in the Public Health Category of the 2002 BMA Books Competition.
"A major work of interpretation of medical and social thought . . . this volume is also to be commended for its skillful, absorbing presentation of the background and the effects of this dread disease."—I.B. Cohen, New York Times
"The Cholera Years is a masterful analysis of the moral and social interest attached to epidemic disease, providing generally applicable insights into how the connections between social change, changes in knowledge and changes in technical practice may be conceived."—Steven Shapin, Times Literary Supplement
"In a way that is all too rarely done, Rosenberg has skillfully interwoven medical, social, and intellectual history to show how medicine and society interacted and changed during the 19th century. The history of medicine here takes its rightful place in the tapestry of human history."—John B. Blake, Science
Collecting, analysing and drawing inferences from data is central to research in the medical and social sciences. Unfortunately, it is rarely possible to collect all the intended data. The literature on inference from the resulting incomplete data is now huge, and continues to grow both as methods are developed for large and complex data structures, and as increasing computer power and suitable software enable researchers to apply these methods.
This book focuses on a particular statistical method for analysing and drawing inferences from incomplete data, called Multiple Imputation (MI). MI is attractive because it is both practical and widely applicable. The authors aim is to clarify the issues raised by missing data, describing the rationale for MI, the relationship between the various imputation models and associated algorithms and its application to increasingly complex data structures.
Multiple Imputation and its Application:Discusses the issues raised by the analysis of partially observed data, and the assumptions on which analyses rest. Presents a practical guide to the issues to consider when analysing incomplete data from both observational studies and randomized trials. Provides a detailed discussion of the practical use of MI with real-world examples drawn from medical and social statistics. Explores handling non-linear relationships and interactions with multiple imputation, survival analysis, multilevel multiple imputation, sensitivity analysis via multiple imputation, using non-response weights with multiple imputation and doubly robust multiple imputation.
Multiple Imputation and its Application is aimed at quantitative researchers and students in the medical and social sciences with the aim of clarifying the issues raised by the analysis of incomplete data data, outlining the rationale for MI and describing how to consider and address the issues that arise in its application.
The aim of this book is to show how R can be used as the software tool in the development of Six Sigma projects. The book includes a gentle introduction to Six Sigma and a variety of examples showing how to use R within real situations. It has been conceived as a self contained piece. Therefore, it is addressed not only to Six Sigma practitioners, but also to professionals trying to initiate themselves in this management methodology. The book may be used as a text book as well.
This book examines and explains these health changes and considers likely future patterns and changes. While the overall picture charted is one of progress and improvement, certain unfortunate regressions and stubbornly persistent health inequalities are equally shown to be part of the evolving patterns of global health.
The chapters of the book are organized in three major parts:The first part introduces readers to the principal concepts of global health, and to the idea of populations having distinctive health profiles. In particular, it explores how those profiles can be measured, and how they change, using the umbrella concepts and theories of epidemiological and health transition. Building on the first section, the second part focuses on the evolution of health states, as well as paying particular attention to the reasons for the many subnational inequalities in global health. It also examines health challenges such as the continuing infectious disease burden and current emerging ‘epidemics’. The final part transports readers from the current health scene to future possible and probable health scenarios, acknowledging the challenges presented by global environmental change, as well as issues centred around geopolitics and human security.
Using clear and original explanations of complex issues, this text makes extensive use of boxed case studies and international examples, with thought-provoking discussion questions posed for readers at the end of each chapter. Global Health is essential reading for students of global health, public health and development studies.
This Book:Surveys basic statistical methods used in the genetics and epidemiology literature, including maximum likelihood and least squares. Introduces methods, such as permutation testing and bootstrapping, that are becoming more widely used in both genetic and epidemiological research. Is illustrated throughout with simple examples to clarify the statistical methodology. Explains Bayes’ theorem pictorially. Features exercises, with answers to alternate questions, enabling use as a course text.
Written at an elementary mathematical level so that readers with high school mathematics will find the content accessible. Graduate students studying genetic epidemiology, researchers and practitioners from genetics, epidemiology, biology, medical research and statistics will find this an invaluable introduction to statistics.
This comprehensive, up-to-date volume aims to define issues and potential solutions to the challenges of antimicrobial resistance. The chapter authors are leading international experts on antimicrobial resistance among a variety of bacteria (Streptococcus pneumoniae, enteroccoci, staphylococci, gram-negative bacilli, mycobacteria species) viruses (HIV, herpesviruses), and fungi (Candida species, fusarium etc.). The chapters will explore the molecular mechanisms of drug resistance, the immunology and epidemiology of resistance strains, clinical implications and implications on research and lack thereof, and prevention and future directions. This volume will also describe the steps that researchers are taking to develop molecular methods for detecting resistance; develop drugs and other means to deal with newly-resistant organisms. A special chapter to address the issues on strategies to limit antimicrobial resistance propagation will be included in this volume.
The New Public Health will help students and practitioners understand factors affecting the reform process of health care organization and delivery. It links the classic public health issues such as environmental sanitation, health education, and epidemiology with the new issues of universal health care, economics, and management of health systems for the new century.Provides a comprehensive overview of public health from a global perspectiveAssesses health systems models of the United States, Russia, the United Kingdom, Germany, Canada, Scandinavian countries, and developing countries including China, Nigeria, and ColombiaAnalyzes critical issues of health economics, including forces associated with escalating costs and the strategies to control those costsDiscusses strategies for dealing with the many ramifications of managed careLinks medicine with the social sciences, technology, and health management issues as they evolve
* import and preprocessing of data from various sources
* statistical modeling of differential gene expression
* biological metadata
* application of graphs and graph rendering
* machine learning for clustering and classification problems
* gene set enrichment analysis
Each chapter of this book describes an analysis of real data using hands-on example driven approaches. Short exercises help in the learning process and invite more advanced considerations of key topics. The book is a dynamic document. All the code shown can be executed on a local computer, and readers are able to reproduce every computation, figure, and table.
The Essentials For Dummies Series
Dummies is proud to present our new series, The Essentials For Dummies. Now students who are prepping for exams, preparing to study new material, or who just need a refresher can have a concise, easy-to-understand review guide that covers an entire course by concentrating solely on the most important concepts. From algebra and chemistry to grammar and Spanish, our expert authors focus on the skills students most need to succeed in a subject.
· Downloadable data sets
· Library of computer programs in SAS, SPSS, Stata, HLM, MLwiN, and more
· Additional material for data analysis
In 1976 a deadly virus emerged from the Congo forest. As swiftly as it came, it disappeared, leaving no trace. Over the four decades since, Ebola has emerged sporadically, each time to devastating effect. It can kill up to 90 percent of its victims. In between these outbreaks, it is untraceable, hiding deep in the jungle. The search is on to find Ebola’s elusive host animal. And until we find it, Ebola will continue to strike. Acclaimed science writer and explorer David Quammen first came near the virus while he was traveling in the jungles of Gabon, accompanied by local men whose village had been devastated by a recent outbreak. Here he tells the story of Ebola—its past, present, and its unknowable future.
Extracted from Spillover by David Quammen, updated and with additional material.
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 explanationsof 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 for each step of the process. The data sets and corresponding code are available in the book’s companion AppliedPredictiveModeling R package, which is freely available on the CRAN archive.
This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package.
Readers and students interested in implementing the methods should have some basic knowledge of R. And a handful of the more advanced topics require some mathematical knowledge.
The fun and easy way to get down to business with statistics
Stymied by statistics? No fear? this friendly guide offers clear, practical explanations of statistical ideas, techniques, formulas, and calculations, with lots of examples that show you how these concepts apply to your everyday life.
Statistics For Dummies shows you how to interpret and critique graphs and charts, determine the odds with probability, guesstimate with confidence using confidence intervals, set up and carry out a hypothesis test, compute statistical formulas, and more.Tracks to a typical first semester statistics course Updated examples resonate with today's students Explanations mirror teaching methods and classroom protocol
Packed with practical advice and real-world problems, Statistics For Dummies gives you everything you need to analyze and interpret data for improved classroom or on-the-job performance.
Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.