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Escaping flatland -- Micro/macro readings -- Layering and separation -- Small multiples -- Color and information -- Narratives and space and time -- Epilogue.
Display of information for paper and computer screens; principles of information design, design of presentations. Depicting evidence relevant to cause and effect, decision making. Scientific visualization.
How seeing turns into showing, how empirical observations turn into explanation and evidence. How to produce and consume evidence presentations.
The latest edition of this manual provides documentation for users of the statistical software system S.
Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers’ knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today’s model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work. The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation. By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling. Web Resource The book is accompanied by an R package (rethinking) that is available on the author’s website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.
Speculations about the effects of politics on economic life have a long and vital tradition, but few efforts have been made to determine the precise relationship between them. Edward Tufte, a political scientist who covered the 1976 Presidential election for Newsweek, seeks to do just that. His sharp analyses and astute observations lead to an eye-opening view of the impact of political life on the national economy of America and other capitalist democracies. The analysis demonstrates how politicians, political parties, and voters decide who gets what, when, and how in the economic arena. A nation's politics, it is argued, shape the most important aspects of economic life--inflation, unemployment, income redistribution, the growth of government, and the extent of central economic control. Both statistical data and case studies (based on interviews and Presidential documents) are brought to bear on four topics. They are: 1) the political manipulation of the economy in election years, 2) the new international electoral-economic cycle, 3) the decisive role of political leaders and parties in shaping macroeconomic outcomes, and 4) the response of the electorate to changing economic conditions. Finally, the book clarifies a central question in political economy: How can national economic policy be conducted in both a democratic and a competent fashion?
Unique in that it collects, presents, and synthesizes cutting edge research on different aspects of statistical reasoning and applies this research to the teaching of statistics to students at all educational levels, this volume will prove of great value to mathematics and statistics education researchers, statistics educators, statisticians, cognitive psychologists, mathematics teachers, mathematics and statistics curriculum developers, and quantitative literacy experts in education and government.
Designed to teach students to apply statistical methods to real problems (a universal need), Bruce Trumbo's concise new book teaches basic statistical principles through their application to real data. The data sets are chosen from fields to which all students can relate, such as marketing, industrial safety, anthropology, psychology, banking, biology, linguistics, public health, geography, physics, sports, geology, and medicine. Throughout the book, the emphasis is on how statistical ideas and methods can be used to illuminate the data, rather than on how the data can be used to illustrate particular statistical methods. Some of the basic statistical methods that prove to be useful include graphical displays, confidence intervals, one and two-sample t-tests, chi-squared analyses of contingency tables, simple and multiple linear regression, correlation, one-way ANOVAs, and block designs. For each data set, students are guided through some basic procedures, usually using MINITAB(tm), then invited to explore the data more extensively on their own, with answers and possible approaches.
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