Search results for: statistical-inference-for-everyone

Statistical Inference for Everyone

Author : Brian Blais
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Approaching an introductory statistical inference textbook in a novel way, this book is motivated by the perspective of "probability theory as logic". Targeted to the typical "Statistics 101" college student this book covers the topics typically treated in such a course - but from a fresh angle. This book walks through a simple introduction to probability, and then applies those principles to all problems of inference. Topics include hypothesis testing, data visualization, parameter inference, and model comparison. Statistical Inference for Everyone is freely available under the Creative Commons License, and includes a software library in Python for making calculations and visualizations straightforward.

Statistical Inference For Everyone

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The Logical Foundations of Statistical Inference

Author : Henry E. Kyburg Jr.
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Everyone knows it is easy to lie with statistics. It is important then to be able to tell a statistical lie from a valid statistical inference. It is a relatively widely accepted commonplace that our scientific knowledge is not certain and incorrigible, but merely probable, subject to refinement, modifi cation, and even overthrow. The rankest beginner at a gambling table understands that his decisions must be based on mathematical ex pectations - that is, on utilities weighted by probabilities. It is widely held that the same principles apply almost all the time in the game of life. If we turn to philosophers, or to mathematical statisticians, or to probability theorists for criteria of validity in statistical inference, for the general principles that distinguish well grounded from ill grounded generalizations and laws, or for the interpretation of that probability we must, like the gambler, take as our guide in life, we find disagreement, confusion, and frustration. We might be prepared to find disagreements on a philosophical and theoretical level (although we do not find them in the case of deductive logic) but we do not expect, and we may be surprised to find, that these theoretical disagreements lead to differences in the conclusions that are regarded as 'acceptable' in the practice of science and public affairs, and in the conduct of business.

Comparative Statistical Inference

Author : Vic Barnett
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This fully updated and revised third edition, presents a wide ranging, balanced account of the fundamental issues across the full spectrum of inference and decision-making. Much has happened in this field since the second edition was published: for example, Bayesian inferential procedures have not only gained acceptance but are often the preferred methodology. This book will be welcomed by both the student and practising statistician wishing to study at a fairly elementary level, the basic conceptual and interpretative distinctions between the different approaches, how they interrelate, what assumptions they are based on, and the practical implications of such distinctions. As in earlier editions, the material is set in a historical context to more powerfully illustrate the ideas and concepts. Includes fully updated and revised material from the successful second edition Recent changes in emphasis, principle and methodology are carefully explained and evaluated Discusses all recent major developments Particular attention is given to the nature and importance of basic concepts (probability, utility, likelihood etc) Includes extensive references and bibliography Written by a well-known and respected author, the essence of this successful book remains unchanged providing the reader with a thorough explanation of the many approaches to inference and decision making.

Introduction to Linear Models and Statistical Inference

Author : Steven J. Janke
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A multidisciplinary approach that emphasizes learning by analyzingreal-world data sets This book is the result of the authors' hands-on classroomexperience and is tailored to reflect how students best learn toanalyze linear relationships. The text begins with the introductionof four simple examples of actual data sets. These examples aredeveloped and analyzed throughout the text, and more complicatedexamples of data sets are introduced along the way. Taking amultidisciplinary approach, the book traces the conclusion of theanalyses of data sets taken from geology, biology, economics,psychology, education, sociology, and environmental science. As students learn to analyze the data sets, they masterincreasingly sophisticated linear modeling techniques,including: * Simple linear models * Multivariate models * Model building * Analysis of variance (ANOVA) * Analysis of covariance (ANCOVA) * Logistic regression * Total least squares The basics of statistical analysis are developed and emphasized,particularly in testing the assumptions and drawing inferences fromlinear models. Exercises are included at the end of each chapter totest students' skills before moving on to more advanced techniquesand models. These exercises are marked to indicate whethercalculus, linear algebra, or computer skills are needed. Unlike other texts in the field, the mathematics underlying themodels is carefully explained and accessible to students who maynot have any background in calculus or linear algebra. Mostchapters include an optional final section on linear algebra forstudents interested in developing a deeper understanding. The many data sets that appear in the text are available on thebook's Web site. The MINITAB(r) software program is used toillustrate many of the examples. For students unfamiliar withMINITAB(r), an appendix introduces the key features needed to studylinear models. With its multidisciplinary approach and use of real-world data setsthat bring the subject alive, this is an excellent introduction tolinear models for students in any of the natural or socialsciences.

Probability Statistics and Queueing Theory

Author : Arnold O. Allen
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Probability, Statistics, and Queueing Theory: With Computer Science Applications focuses on the use of statistics and queueing theory for the design and analysis of data communication systems, emphasizing how the theorems and theory can be used to solve practical computer science problems. This book is divided into three parts. The first part discusses the basic concept of probability, probability distributions commonly used in applied probability, and important concept of a stochastic process. Part II covers the discipline of queueing theory, while Part III deals with statistical inference. This publication is designed as a junior-senior level textbook on applied probability and statistics with computer science applications, but is also a self-study book for practicing computer science (data processing) professionals.

Statistical Inference

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Introduction to Social Research

Author : Keith F Punch
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`This book offers an excellent description of quantitative and qualitative design and analysis taught in the context of three inquiry pathways: knowledge development, social inquiry, and social research. Furthermore, it provides an excellent overview of both quantitative and qualitative methods with regard to their different epistemologies, methods and techniques. Additionally, Punch provides (a) information guiding students in the use of computers for quantitative and qualitative analysis and (b) a wide range of illustrative examples to give the book a practical flavour.... I would not hesitate to recommend it to my undergraduate students' - Forum: Qualitative Social Research (www.qualitative-research.net) `A carefully crafted introduction to social research methodology. The author guides the reader through the research process in a comprehensive and logical way which is particularly helpful for the new researcher. A great resource for anyone involved in social research - novice or experienced' - Dr Mary Kellett, Open University Building on the success of An Introduction to Social Research, this Second Edition has been fully revised and updated to provide a broader coverage of a range of methodological approaches for third year and postgraduate students across the social sciences. New features include: - Wider coverage of qualitative research methods, including sections on action research and discourse analysis. - More pedagogical features such as student exercises and illustrative examples to ensure this is a practical, student-friendly guide. - More in-depth examination of the ethical issues involved in social research. A comprehensive and accessible introduction to qualitative, quantitative and mixed empirical methods, An Introduction to Social Research is an ideal starting point for all students and researchers in the social sciences.

Introduction to Research Methods in Education

Author : Keith F Punch
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Based on Keith Punch’s bestselling Introduction to Social Research Methods, this book introduces the research process in a range of educational contexts. In this updated second edition, you’ll find guidance on every stage of research, with chapters on developing research questions, doing a literature review, collecting data, analysing your findings and writing it all up. With a new chapter on ethics and additional coverage of Internet research and mixing methods, the second edition contains everything you’ll need if you’re studying on a research methods course or doing a research project for yourself. Introduction to Research Methods in Education provides you with: Balanced coverage of the range of methods employed by educational researchers Practical advice on collecting and analysing qualitative, quantitative and mixed methods data A range of examples from student projects to show how research is done in real-life educational settings A companion website with additional resources for lecturers and students. Keith F Punch is Emeritus Professor in the Graduate School of Education at The University of Western Australia. Alis Oancea is Professor of Philosophy of Education and Research Policy, as well as Director of Research at the University of Oxford, Department of Education.

Ecological Inference

Author : Gary King
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Drawing upon the explosion of research in the field, a diverse group of scholars surveys strategies for solving ecological inference problems, the process of trying to infer individual behavior from aggregate data. The uncertainties and information lost in aggregation make ecological inference one of the most difficult areas of statistical inference, but these inferences are required in many academic fields, as well as by legislatures and the Courts in redistricting, marketing research by business, and policy analysis by governments. This wide-ranging collection of essays, first published in 2004, offers many important contributions to the study of ecological inference.

Probability and Statistical Inference

Author : J.G. Kalbfleisch
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This book is in two volumes, and is intended as a text for introductory courses in probability and statistics at the second or third year university level. It emphasizes applications and logical principles rather than math ematical theory. A good background in freshman calculus is sufficient for most of the material presented. Several starred sections have been included as supplementary material. Nearly 900 problems and exercises of varying difficulty are given, and Appendix A contains answers to about one-third of them. The first volume (Chapters 1-8) deals with probability models and with mathematical methods for describing and manipulating them. It is similar in content and organization to the 1979 edition. Some sections have been rewritten and expanded-for example, the discussions of independent random variables and conditional probability. Many new exercises have been added. In the second volume (Chapters 9-16), probability models are used as the basis for the analysis and interpretation of data. This material has been revised extensively. Chapters 9 and 10 describe the use of the like lihood function in estimation problems, as in the 1979 edition. Chapter 11 then discusses frequency properties of estimation procedures, and in troduces coverage probability and confidence intervals. Chapter 12 de scribes tests of significance, with applications primarily to frequency data.

Confusion

Author : Joseph L. CAMP
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Camp rejects the philosophical conceit that confusion is a kind of ambiguity; his fundamental claim is that confusion is not a mental state. He proposes a novel characterization of confusion, and then demonstrates its fruitfulness with several applications in the history of philosophy and the history of science.

Social statistics without tears

Author : Allan G. Johnson
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Getting it Wrong

Author : William A. Barnett
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A leading economist contends that the recent financial crisis was caused not by the failure of mainstream economics but by corrupted monetary data constructed without reference to economics. Blame for the recent financial crisis and subsequent recession has commonly been assigned to everyone from Wall Street firms to individual homeowners. It has been widely argued that the crisis and recession were caused by “greed” and the failure of mainstream economics. In Getting It Wrong, leading economist William Barnett argues instead that there was too little use of the relevant economics, especially from the literature on economic measurement. Barnett contends that as financial instruments became more complex, the simple-sum monetary aggregation formulas used by central banks, including the U.S. Federal Reserve, became obsolete. Instead, a major increase in public availability of best-practice data was needed. Households, firms, and governments, lacking the requisite information, incorrectly assessed systemic risk and significantly increased their leverage and risk-taking activities. Better financial data, Barnett argues, could have signaled the misperceptions and prevented the erroneous systemic-risk assessments. When extensive, best-practice information is not available from the central bank, increased regulation can constrain the adverse consequences of ill-informed decisions. Instead, there was deregulation. The result, Barnett argues, was a worst-case toxic mix: increasing complexity of financial instruments, inadequate and poor-quality data, and declining regulation. Following his accessible narrative of the deep causes of the crisis and the long history of private and public errors, Barnett provides technical appendixes, containing the mathematical analysis supporting his arguments.

The Nature of Scientific Evidence

Author : Mark L. Taper
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An exploration of the statistical foundations of scientific inference, The Nature of Scientific Evidence asks what constitutes scientific evidence and whether scientific evidence can be quantified statistically. Mark Taper, Subhash Lele, and an esteemed group of contributors explore the relationships among hypotheses, models, data, and inference on which scientific progress rests in an attempt to develop a new quantitative framework for evidence. Informed by interdisciplinary discussions among scientists, philosophers, and statisticians, they propose a new "evidential" approach, which may be more in keeping with the scientific method. The Nature of Scientific Evidence persuasively argues that all scientists should care more about the fine points of statistical philosophy because therein lies the connection between theory and data. Though the book uses ecology as an exemplary science, the interdisciplinary evaluation of the use of statistics in empirical research will be of interest to any reader engaged in the quantification and evaluation of data.

Essentials of Statistics for the Social and Behavioral Sciences

Author : Barry H. Cohen
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Master the essential statistical skills used in social andbehavioral sciences Essentials of Statistics for the Social and Behavioral Sciencesdistills the overwhelming amount of material covered inintroductory statistics courses into a handy, practical resourcefor students and professionals. This accessible guide covers basicto advanced concepts in a clear, concrete, and readablestyle. Essentials of Statistics for the Social and Behavioral Sciencesguides you to a better understanding of basic concepts ofstatistical methods. Numerous practical tips are presented forselecting appropriate statistical procedures. In addition, thisuseful guide demonstrates how to evaluate and interpret statisticaldata, provides numerous formulas for calculating statistics fromtables of summary statistics, and offers a variety of workedexamples. As part of the Essentials of Behavioral Science series, this bookoffers a thorough review of the most relevant statistical conceptsand techniques that will arm you with the tools you'll need forknowledgeable, informed practice. Each concise chapter featuresnumerous callout boxes highlighting key concepts, bulleted points,and extensive illustrative material, as well as "Test Yourself"questions that help you gauge and reinforce your grasp of theinformation covered.

Basic Statistics for Social Research

Author : Robert A. Hanneman
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A core statistics text that emphasizes logical inquiry, notmath Basic Statistics for Social Research teaches core generalstatistical concepts and methods that all social science majorsmust master to understand (and do) social research. Its use ofmathematics and theory are deliberately limited, as the authorsfocus on the use of concepts and tools of statistics in theanalysis of social science data, rather than on the mathematicaland computational aspects. Research questions and applications aretaken from a wide variety of subfields in sociology, and eachchapter is organized around one or more general ideas that areexplained at its beginning and then applied in increasing detail inthe body of the text. Each chapter contains instructive features to aid students inunderstanding and mastering the various statistical approachespresented in the book, including: Learning objectives Check quizzes after many sections and an answer key at the endof the chapter Summary Key terms End-of-chapter exercises SPSS exercises (in select chapters) Ancillary materials for both the student and the instructor areavailable and include a test bank for instructors and downloadablevideo tutorials for students.

Bayesian Methods for Data Analysis Third Edition

Author : Bradley P. Carlin
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Broadening its scope to nonstatisticians, Bayesian Methods for Data Analysis, Third Edition provides an accessible introduction to the foundations and applications of Bayesian analysis. Along with a complete reorganization of the material, this edition concentrates more on hierarchical Bayesian modeling as implemented via Markov chain Monte Carlo (MCMC) methods and related data analytic techniques. New to the Third Edition New data examples, corresponding R and WinBUGS code, and homework problems Explicit descriptions and illustrations of hierarchical modeling—now commonplace in Bayesian data analysis A new chapter on Bayesian design that emphasizes Bayesian clinical trials A completely revised and expanded section on ranking and histogram estimation A new case study on infectious disease modeling and the 1918 flu epidemic A solutions manual for qualifying instructors that contains solutions, computer code, and associated output for every homework problem—available both electronically and in print Ideal for Anyone Performing Statistical Analyses Focusing on applications from biostatistics, epidemiology, and medicine, this text builds on the popularity of its predecessors by making it suitable for even more practitioners and students.

Statistical Inference for Management and Economics

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Elements of Statistical Inference

Author : David V. Huntsberger
File Size : 88.37 MB
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