-PDF Download- Predicting Party Sizes EBOOK

Predicting Party Sizes

Author: Rein Taagepera

Publisher: OUP Oxford

Published: 2007-08-23

Total Pages: 336

ISBN-13: 0191537020

For a given electoral system, what average number and sizes of parties and government duration can we expect? Predicting Party Sizes is the first book to make specific predictions that agree with world averages. The basic factors are the numbers of seats in the assembly and in the average electoral district. While previous models tell us only the direction in which to change the electoral system, the present ones also tell us by how much they must be changed so as to obtain the desired change in average number of parties and cabinet duration. Hence, combined with known particularities of a country, they can be used for informed institutional design. The book is useful to three types of readers: political science students learning the basics of electoral systems and their political consequences; practitioners of politics who consider changing the electoral laws; and researchers intent on connecting electoral and party systems. The book is structured accordingly. Chapters start with advice and recipes for practicing politicians, in non-technical language. The main text gives students an overview of electoral systems, worldwide, and supplies evidence for models that tie simple electoral systems (First-Past-The-Post and List Proportional Representation) to the number and sizes of parties and government duration. Chapter appendices present derivations of these models and other more technical issues of interest to researchers.

Predicting Party Sizes

Author: Rein Taagepera

Publisher: Oxford University Press on Demand

Published: 2007-08-23

Total Pages: 337

ISBN-13: 0199287740

DOWNLOAD EBOOK →

Predicting Party Sizes connects party systems and government duration to electoral systems. This book provides an overview of electoral systems, worldwide, and supplies evidence for models that tie simple electoral systems to the number and sizes of parties and government duration.

Votes from Seats

Author: Matthew S. Shugart

Publisher: Cambridge University Press

Published: 2017-10-06

Total Pages: 361

ISBN-13: 1108265731

DOWNLOAD EBOOK →

Take the number of seats in a representative assembly and the number of seats in districts through which this assembly is elected. From just these two numbers, the authors of Votes from Seats show that it is possible to deduce the number of parties in the assembly and in the electorate, as well as the size of the largest party. Inside parties, the vote distributions of individual candidates likewise follow predictable patterns. Four laws of party seats and votes are constructed by logic and tested, using scientific approaches rare in social sciences. Both complex and simple electoral systems are covered, and the book offers a set of 'best practices' for electoral system design. The ability to predict so much from so little, and to apply to countries worldwide, is an advance in the systematic analysis of a core institutional feature found in any democracy, and points the way towards making social sciences more predictive.

Predicting Presidential Elections and Other Things, Second Edition

Author: Ray Fair

Publisher: Stanford University Press

Published: 2011-12-14

Total Pages: 234

ISBN-13: 0804778027

DOWNLOAD EBOOK →

"It's the economy, stupid," as Democratic strategist James Carville would say. After many years of study, Ray C. Fair has found that the state of the economy has a dominant influence on national elections. Just in time for the 2012 presidential election, this new edition of his classic text, Predicting Presidential Elections and Other Things, provides us with a look into the likely future of our nation's political landscape—but Fair doesn't stop there. Fair puts other national issues under the microscope as well—including congressional elections, Federal Reserve behavior, and inflation. In addition he covers topics well beyond today's headlines, as the book takes on questions of more direct, personal interest such as wine quality, predicting football games, and aging effects in baseball. Which of your friends is most likely to have an extramarital affair? How important is class attendance for academic performance in college? How fast can you expect to run a race or perform some physical task at age 55, given your time at age 30? Read Predicting Presidential Elections and Other Things and find out! As Fair works his way through an incredibly broad range of questions and topics, he teaches and delights. The discussion that underlies each chapter topic moves from formulating theories about real world phenomena to lessons on how to analyze data, test theories, and make predictions. At the end of this book, readers will walk away with more than mere predictions. They will have learned a new approach to thinking about many age-old concerns in public and private life, and will have a myriad of fun facts to share.

Predicting Politics

Author: W. Mark Crain

Publisher:

Published: 1990

Total Pages: 328

ISBN-13:

DOWNLOAD EBOOK →

Brings together essays by scholars who have worked in the public choice tradition.

Fundamentals of Clinical Data Science

Author: Pieter Kubben

Publisher: Springer

Published: 2018-12-21

Total Pages: 219

ISBN-13: 3319997130

DOWNLOAD EBOOK →

This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience.

Making Social Sciences More Scientific

Author: Rein Taagepera

Publisher: OUP Oxford

Published: 2008-07-24

Total Pages: 272

ISBN-13: 0191560030

DOWNLOAD EBOOK →

In his challenging new book Rein Taagepera argues that society needs more from social sciences than they have delivered. One reason for falling short is that social sciences have depended excessively on regression and other statistical approaches, neglecting logical model building. Science is not only about the empirical 'What is?' but also very much about the conceptual 'How should it be on logical grounds?' Statistical approaches are essentially descriptive, while quantitatively formulated logical models are predictive in an explanatory way. Why Social Sciences Are Not Scientific Enough contrasts the predominance of statistics in today's social sciences and predominance of quantitatively predictive logical models in physics. It shows how to construct predictive models and gives social science examples. Why Social Sciences Are Not Scientific Enough is useful to students who wish to learn the basics of the scientific method and to all those researchers who look for ways to do better social science.

A Method for Predicting Length of Service

Author: Franklin Robert Cawl

Publisher:

Published: 1926

Total Pages: 166

ISBN-13:

DOWNLOAD EBOOK →

Introduction to Data Science

Author: Rafael A. Irizarry

Publisher: CRC Press

Published: 2019-11-20

Total Pages: 794

ISBN-13: 1000708039

DOWNLOAD EBOOK →

Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert.

Prediction, Learning, and Games

Author: Nicolo Cesa-Bianchi

Publisher: Cambridge University Press

Published: 2006-03-13

Total Pages: 4

ISBN-13: 113945482X

DOWNLOAD EBOOK →

This important text and reference for researchers and students in machine learning, game theory, statistics and information theory offers a comprehensive treatment of the problem of predicting individual sequences. Unlike standard statistical approaches to forecasting, prediction of individual sequences does not impose any probabilistic assumption on the data-generating mechanism. Yet, prediction algorithms can be constructed that work well for all possible sequences, in the sense that their performance is always nearly as good as the best forecasting strategy in a given reference class. The central theme is the model of prediction using expert advice, a general framework within which many related problems can be cast and discussed. Repeated game playing, adaptive data compression, sequential investment in the stock market, sequential pattern analysis, and several other problems are viewed as instances of the experts' framework and analyzed from a common nonstochastic standpoint that often reveals new and intriguing connections.