Reasoning With Data
Download Reasoning With Data PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Reasoning With Data book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Reasoning with Data
Author: Jeffrey M. Stanton
language: en
Publisher: Guilford Publications
Release Date: 2017-05-19
Engaging and accessible, this book teaches readers how to use inferential statistical thinking to check their assumptions, assess evidence about their beliefs, and avoid overinterpreting results that may look more promising than they really are. It provides step-by-step guidance for using both classical (frequentist) and Bayesian approaches to inference. Statistical techniques covered side by side from both frequentist and Bayesian approaches include hypothesis testing, replication, analysis of variance, calculation of effect sizes, regression, time series analysis, and more. Students also get a complete introduction to the open-source R programming language and its key packages. Throughout the text, simple commands in R demonstrate essential data analysis skills using real-data examples. The companion website provides annotated R code for the book's examples, in-class exercises, supplemental reading lists, and links to online videos, interactive materials, and other resources. ÿ Pedagogical Features *Playful, conversational style and gradual approach; suitable for students without strong math backgrounds. *End-of-chapter exercises based on real data supplied in the free R package. *Technical explanation and equation/output boxes. *Appendices on how to install R and work with the sample datasets.ÿ
Thinking Clearly with Data
Author: Ethan Bueno de Mesquita
language: en
Publisher: Princeton University Press
Release Date: 2021-11-16
An engaging introduction to data science that emphasizes critical thinking over statistical techniques An introduction to data science or statistics shouldn’t involve proving complex theorems or memorizing obscure terms and formulas, but that is exactly what most introductory quantitative textbooks emphasize. In contrast, Thinking Clearly with Data focuses, first and foremost, on critical thinking and conceptual understanding in order to teach students how to be better consumers and analysts of the kinds of quantitative information and arguments that they will encounter throughout their lives. Among much else, the book teaches how to assess whether an observed relationship in data reflects a genuine relationship in the world and, if so, whether it is causal; how to make the most informative comparisons for answering questions; what questions to ask others who are making arguments using quantitative evidence; which statistics are particularly informative or misleading; how quantitative evidence should and shouldn’t influence decision-making; and how to make better decisions by using moral values as well as data. Filled with real-world examples, the book shows how its thinking tools apply to problems in a wide variety of subjects, including elections, civil conflict, crime, terrorism, financial crises, health care, sports, music, and space travel. Above all else, Thinking Clearly with Data demonstrates why, despite the many benefits of our data-driven age, data can never be a substitute for thinking. An ideal textbook for introductory quantitative methods courses in data science, statistics, political science, economics, psychology, sociology, public policy, and other fields Introduces the basic toolkit of data analysis—including sampling, hypothesis testing, Bayesian inference, regression, experiments, instrumental variables, differences in differences, and regression discontinuity Uses real-world examples and data from a wide variety of subjects Includes practice questions and data exercises
Ethical Reasoning in Big Data
This book springs from a multidisciplinary, multi-organizational, and multi-sector conversation about the privacy and ethical implications of research in human affairs using big data. The need to cultivate and enlist the public’s trust in the abilities of particular scientists and scientific institutions constitutes one of this book’s major themes. The advent of the Internet, the mass digitization of research information, and social media brought about, among many other things, the ability to harvest – sometimes implicitly – a wealth of human genomic, biological, behavioral, economic, political, and social data for the purposes of scientific research as well as commerce, government affairs, and social interaction. What type of ethical dilemmas did such changes generate? How should scientists collect, manipulate, and disseminate this information? The effects of this revolution and its ethical implications are wide-ranging. This book includes the opinions of myriad investigators, practitioners, and stakeholders in big data on human beings who also routinely reflect on the privacy and ethical issues of this phenomenon. Dedicated to the practice of ethical reasoning and reflection in action, the book offers a range of observations, lessons learned, reasoning tools, and suggestions for institutional practice to promote responsible big data research on human affairs. It caters to a broad audience of educators, researchers, and practitioners. Educators can use the volume in courses related to big data handling and processing. Researchers can use it for designing new methods of collecting, processing, and disseminating big data, whether in raw form or as analysis results. Lastly, practitioners can use it to steer future tools or procedures for handling big data. As this topic represents an area of great interest that still remains largely undeveloped, this book is sure to attract significant interest by filling an obvious gap in currently available literature.