Politics and Uncertainty Theory Models and Applications (1版)

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Uncertainty is an ever-present and ineradicable aspect of politics, affecting all important issues of governance and policy. Rather than treating the uncertainty of politics as a mystery, this book provides an original and direct treatment of political uncertainty as a scientifically-knowable phenomenon. The author shows how probability and mathematical modeling can play a central role in understanding a range of complex and fundamental issues in both domestic and international politics.

Quantitative Social Science: An Introduction

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書名：Quantitative Social Science: An Introduction 作者：Kosuke Imai 出版社：Princeton 出版日期：2017/00/00 ISBN：9780691175461 內容簡介 An introductory textbook on data analysis and statistics written especially for students in the social sciences and allied fields Quantitative analysis is an increasingly essential skill for social science research, yet students in the social sciences and related areas typically receive little training in it—or if they do, they usually end up in statistics classes that offer few insights into their field. This textbook is a practical introduction to data analysis and statistics written especially for undergraduates and beginning graduate students in the social sciences and allied fields, such as economics, sociology, public policy, and data science. Quantitative Social Science engages directly with empirical analysis, showing students how to analyze data using the R programming language and to interpret the results—it encourages hands-on learning, not paper-and-pencil statistics. More than forty data sets taken directly from leading quantitative social science research illustrate how data analysis can be used to answer important questions about society and human behavior. Proven in the classroom, this one-of-a-kind textbook features numerous additional data analysis exercises and interactive R programming exercises, and also comes with supplementary teaching materials for instructors. Written especially for students in the social sciences and allied fields, including economics, sociology, public policy, and data science Provides hands-on instruction using R programming, not paper-and-pencil statistics Includes more than forty data sets from actual research for students to test their skills on Covers data analysis concepts such as causality, measurement, and prediction, as well as probability and statistical tools Features a wealth of supplementary exercises, including additional data analysis exercises and interactive programming exercises Offers a solid foundation for further study Comes with additional course materials online, including notes, sample code, exercises and problem sets with solutions, and lecture slides 目錄 1 Introduction 2 Causality 3 Measurement 4 Prediction 5 Discovery 6 Probability 7 Uncertainty 8 Next