Bias and Causation

Author: Dr. Herbert I. Weisberg
Publisher: John Wiley & Sons
ISBN: 9781118058206
Format: PDF, Mobi
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A one-of-a-kind resource on identifying and dealing with bias in statistical research on causal effects Do cell phones cause cancer? Can a new curriculum increase student achievement? Determining what the real causes of such problems are, and how powerful their effects may be, are central issues in research across various fields of study. Some researchers are highly skeptical of drawing causal conclusions except in tightly controlled randomized experiments, while others discount the threats posed by different sources of bias, even in less rigorous observational studies. Bias and Causation presents a complete treatment of the subject, organizing and clarifying the diverse types of biases into a conceptual framework. The book treats various sources of bias in comparative studies—both randomized and observational—and offers guidance on how they should be addressed by researchers. Utilizing a relatively simple mathematical approach, the author develops a theory of bias that outlines the essential nature of the problem and identifies the various sources of bias that are encountered in modern research. The book begins with an introduction to the study of causal inference and the related concepts and terminology. Next, an overview is provided of the methodological issues at the core of the difficulties posed by bias. Subsequent chapters explain the concepts of selection bias, confounding, intermediate causal factors, and information bias along with the distortion of a causal effect that can result when the exposure and/or the outcome is measured with error. The book concludes with a new classification of twenty general sources of bias and practical advice on how mathematical modeling and expert judgment can be combined to achieve the most credible causal conclusions. Throughout the book, examples from the fields of medicine, public policy, and education are incorporated into the presentation of various topics. In addition, six detailed case studies illustrate concrete examples of the significance of biases in everyday research. Requiring only a basic understanding of statistics and probability theory, Bias and Causation is an excellent supplement for courses on research methods and applied statistics at the upper-undergraduate and graduate level. It is also a valuable reference for practicing researchers and methodologists in various fields of study who work with statistical data. This book was selected as the 2011 Ziegel Prize Winner in Technometrics for the best book reviewed by the journal. It is also the winner of the 2010 PROSE Award for Mathematics from The American Publishers Awards for Professional and Scholarly Excellence

Bias and Causation

Author: Herbert I. Weisberg
Publisher: Wiley
ISBN: 0470631090
Format: PDF, ePub
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A one-of-a-kind resource on identifying and dealing with bias in statistical research on causal effects Do cell phones cause cancer? Can a new curriculum increase student achievement? Determining what the real causes of such problems are, and how powerful their effects may be, are central issues in research across various fields of study. Some researchers are highly skeptical of drawing causal conclusions except in tightly controlled randomized experiments, while others discount the threats posed by different sources of bias, even in less rigorous observational studies. Bias and Causation presents a complete treatment of the subject, organizing and clarifying the diverse types of biases into a conceptual framework. The book treats various sources of bias in comparative studies—both randomized and observational—and offers guidance on how they should be addressed by researchers. Utilizing a relatively simple mathematical approach, the author develops a theory of bias that outlines the essential nature of the problem and identifies the various sources of bias that are encountered in modern research. The book begins with an introduction to the study of causal inference and the related concepts and terminology. Next, an overview is provided of the methodological issues at the core of the difficulties posed by bias. Subsequent chapters explain the concepts of selection bias, confounding, intermediate causal factors, and information bias along with the distortion of a causal effect that can result when the exposure and/or the outcome is measured with error. The book concludes with a new classification of twenty general sources of bias and practical advice on how mathematical modeling and expert judgment can be combined to achieve the most credible causal conclusions. Throughout the book, examples from the fields of medicine, public policy, and education are incorporated into the presentation of various topics. In addition, six detailed case studies illustrate concrete examples of the significance of biases in everyday research. Requiring only a basic understanding of statistics and probability theory, Bias and Causation is an excellent supplement for courses on research methods and applied statistics at the upper-undergraduate and graduate level. It is also a valuable reference for practicing researchers and methodologists in various fields of study who work with statistical data. This book was selected as the 2011 Ziegel Prize Winner in Technometrics for the best book reviewed by the journal. It is also the winner of the 2010 PROSE Award for Mathematics from The American Publishers Awards for Professional and Scholarly Excellence

An Introduction to Probability and Statistics

Author: Vijay K. Rohatgi
Publisher: John Wiley & Sons
ISBN: 1118799682
Format: PDF
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A well-balanced introduction to probability theory and mathematical statistics Featuring updated material, An Introduction to Probability and Statistics, Third Edition remains a solid overview to probability theory and mathematical statistics. Divided intothree parts, the Third Edition begins by presenting the fundamentals and foundationsof probability. The second part addresses statistical inference, and the remainingchapters focus on special topics. An Introduction to Probability and Statistics, Third Edition includes: A new section on regression analysis to include multiple regression, logistic regression, and Poisson regression A reorganized chapter on large sample theory to emphasize the growing role of asymptotic statistics Additional topical coverage on bootstrapping, estimation procedures, and resampling Discussions on invariance, ancillary statistics, conjugate prior distributions, and invariant confidence intervals Over 550 problems and answers to most problems, as well as 350 worked out examples and 200 remarks Numerous figures to further illustrate examples and proofs throughout An Introduction to Probability and Statistics, Third Edition is an ideal reference and resource for scientists and engineers in the fields of statistics, mathematics, physics, industrial management, and engineering. The book is also an excellent text for upper-undergraduate and graduate-level students majoring in probability and statistics.

Foundations of Linear and Generalized Linear Models

Author: Alan Agresti
Publisher: John Wiley & Sons
ISBN: 1118730054
Format: PDF
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A valuable overview of the most important ideas and results in statistical modeling Written by a highly-experienced author, Foundations of Linear and Generalized Linear Models is a clear and comprehensive guide to the key concepts and results of linearstatistical models. The book presents a broad, in-depth overview of the most commonly usedstatistical models by discussing the theory underlying the models, R software applications,and examples with crafted models to elucidate key ideas and promote practical modelbuilding. The book begins by illustrating the fundamentals of linear models, such as how the model-fitting projects the data onto a model vector subspace and how orthogonal decompositions of the data yield information about the effects of explanatory variables. Subsequently, the book covers the most popular generalized linear models, which include binomial and multinomial logistic regression for categorical data, and Poisson and negative binomial loglinear models for count data. Focusing on the theoretical underpinnings of these models, Foundations ofLinear and Generalized Linear Models also features: An introduction to quasi-likelihood methods that require weaker distributional assumptions, such as generalized estimating equation methods An overview of linear mixed models and generalized linear mixed models with random effects for clustered correlated data, Bayesian modeling, and extensions to handle problematic cases such as high dimensional problems Numerous examples that use R software for all text data analyses More than 400 exercises for readers to practice and extend the theory, methods, and data analysis A supplementary website with datasets for the examples and exercises An invaluable textbook for upper-undergraduate and graduate-level students in statistics and biostatistics courses, Foundations of Linear and Generalized Linear Models is also an excellent reference for practicing statisticians and biostatisticians, as well as anyone who is interested in learning about the most important statistical models for analyzing data.

Statistical Shape Analysis

Author: Ian L. Dryden
Publisher: John Wiley & Sons
ISBN: 1119072506
Format: PDF
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A thoroughly revised and updated edition of this introduction to modern statistical methods for shape analysis Shape analysis is an important tool in the many disciplines where objects are compared using geometrical features. Examples include comparing brain shape in schizophrenia; investigating protein molecules in bioinformatics; and describing growth of organisms in biology. This book is a significant update of the highly-regarded `Statistical Shape Analysis’ by the same authors. The new edition lays the foundations of landmark shape analysis, including geometrical concepts and statistical techniques, and extends to include analysis of curves, surfaces, images and other types of object data. Key definitions and concepts are discussed throughout, and the relative merits of different approaches are presented. The authors have included substantial new material on recent statistical developments and offer numerous examples throughout the text. Concepts are introduced in an accessible manner, while retaining sufficient detail for more specialist statisticians to appreciate the challenges and opportunities of this new field. Computer code has been included for instructional use, along with exercises to enable readers to implement the applications themselves in R and to follow the key ideas by hands-on analysis. Statistical Shape Analysis: with Applications in R will offer a valuable introduction to this fast-moving research area for statisticians and other applied scientists working in diverse areas, including archaeology, bioinformatics, biology, chemistry, computer science, medicine, morphometics and image analysis .

Mathe Magie

Author: Arthur Benjamin
Publisher: Heyne Verlag
ISBN: 3641148472
Format: PDF, Docs
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Zaubern mit Zahlen – wer dieses Buch gelesen hat, muss PISA nicht mehr fürchten Wer glaubt, Mathematik sei eine trockene Angelegenheit und Kopfrechnen eine unnötige Quälerei, der irrt sich gewaltig. Denn nach der Lektüre dieses Buches ist es für jeden ein Leichtes, Rechenoperationen mit vier- und fünfstelligen Zahlen in Sekundenschnelle im Kopf auszuführen. Und was wie Zauberei wirkt, ist letztendlich nichts anderes als mathematische Logik, die jedermann beherrschen kann und die dazu noch richtig Spaß macht. • So wird Kopfrechnen kinderleicht! • Mit zahlreichen Übungen und Lösungen

Grounded theory

Author: Anselm L. Strauss
Publisher:
ISBN: 9783621272650
Format: PDF, Kindle
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Studierende und Forscher verschiedener Disziplinen, die am Entwickeln einer Theorie interessiert sind, stellen sich nach der Datenerhebung oft die Frage: Wie komme ich zu einer Theorie, die sich auf die empirische Realität gründet? Die Autoren beantworten diese und andere Fragen, die sich bei der qualitativen Interpretation von Daten ergeben. Auf klare und einfache Art geschrieben vermittelt das Buch Schritt für Schritt die grundlegenden Kenntnisse und Verfahrensweisen der "grounded theory" (datenbasierte Theorie), so daß es besonders für Personen interessant ist, die sich zum ersten Mal mit der Theorienbildung anhand qualitativer Datenanalyse beschäftigen. Das Buch gliedert sich in drei Teile. Teil I bietet einen Überblick über die Denkweise, die der "grounded theory" zugrunde liegt. Teil II stellt die speziellen Techniken und Verfahrensweisen genau dar, wie z.B. verschiedene Kodierungsarten. In Teil III werden zusätzliche Verfahrensweisen erklärt und Evaluationskriterien genannt.

Bauchentscheidungen

Author: Gerd Gigerenzer
Publisher: C. Bertelsmann Verlag
ISBN: 3641171334
Format: PDF, ePub, Mobi
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„Das Herz hat seine Gründe, die der Verstand nicht kennt.“ Viele Menschen treffen Entscheidungen „aus dem Bauch heraus“, was auf den ersten Blick aller Vernunft zu widersprechen scheint. Gerd Gigerenzer, Professor für Psychologie und Direktor am Berliner Max-Planck-Institut für Bildungsforschung, erkundet anhand zahlreicher Beispiele, woher unsere Bauchgefühle oder Intuitionen kommen und welcher spezifischen Logik unsere unbewusste Intelligenz folgt. • Das Geheimnis des gefilterten Wissens – Ausgezeichnet als „Wissenschaftsbuch des Jahres 2007“. • Der Bestseller von Gerd Gigerenzer, einem der profiliertesten deutschen Psychologen der Gegenwart.