Muutke küpsiste eelistusi

Understanding Elections through Statistics: Polling, Prediction, and Testing [Kõva köide]

Teised raamatud teemal:
  • Kõva köide
  • Hind: 219,25 €*
  • * saadame teile pakkumise kasutatud raamatule, mille hind võib erineda kodulehel olevast hinnast
  • See raamat on trükist otsas, kuid me saadame teile pakkumise kasutatud raamatule.
  • Kogus:
  • Lisa ostukorvi
  • Tasuta tarne
  • Lisa soovinimekirja
Teised raamatud teemal:
"Written for those with only a brief introduction to statistics, this book takes you on a statistical journey from how polls are taken to how they can-and should-be used to estimate current popular opinion. Once an understanding of the election process is built, we turn towards testing elections for evidence of unfairness. While holding elections has become the de facto proof of government legitimacy, those electoral processes may hide the dirty little secret of the government illicitly ensuring a favorable election outcome"--

Elections are random events. From individuals deciding whether to vote, to individuals deciding for whom to vote, to election authorities deciding what to count, the outcomes of competitive democratic elections are rarely known until election day… or beyond. Understanding Elections Through Statistics: Polling Prediction, and Testing explores this random phenomenon from two points of view: predicting the election outcome using opinion polls and testing the election outcome using government-reported data.

Written for those with only a brief introduction to statistics, this book takes you on a statistical journey from how polls are taken to how they can—and should—be used to estimate current popular opinion. Once an understanding of the election process is built, we turn towards testing elections for evidence of unfairness. While holding elections has become the de facto proof of government legitimacy, those electoral processes may hide the dirty little secret of the government illicitly ensuring a favorable election outcome.

This book includes these features designed to make your statistical journey more enjoyable:

  • vignettes of elections, including maps, starting each chapter to motivate the material;
  • in-chapter cues to help one avoid the heavy math—or focus on it;
  • end-of-chapter problems designed to review and extend that which was covered in the chapter; and
  • many opportunities to turn the power of the R Statistical Environment to the enclosed election data files, as well as to those you find interesting.

From these features, it is clear that the audience for this book is quite diverse. It provides the mathematics for those interested in mathematics, but also provides detours for those who just want a good read and a deeper understanding of elections.

Ole J. Forsberg holds PhD degrees both in Political Science and in Statistics. He currently teaches mathematics and statistics in the Department of Mathematics at Knox College in Galesburg, IL.

Arvustused

"This unique book, by an author who is both a Statistician and Political Scientist, discusses the statistical theory of two important aspects of elections. The first half is an in-depth introduction to the classical statistical theory of polling, including estimators, confidence intervals, and stratified sampling. It comes complete with snippets of R code and many concrete examples, including two cases that challenged pollsters: the 2016 US presidential election and the 2016 Brexit vote. The second half concerns statistical methods for after the fact detection of fraudulent elections. It includes an in-depth treatment of methods based on the Benford distribution, but also methods based on classical regression analysis. Again numerous pieces of R code and concrete examples are provided." - E. Arthur Robinson, Jr., Professor of Mathematics, George Washington University

"This book has multiple layers that provides flexibility in its use. It makes polling and the statistical issues understandable for those who have little knowledge of statistics beyond the elementary course material. It includes enough of the mathematical underpinnings so that a student wishing to delve deeper into the material has that opportunity. It treats the material with cleverness and wryness that transforms the topic, usually thought of as "dry" by many people, into an interesting and compelling read. The use of maps and real-world examples help make the issues relevant and practical. It should be required reading for anyone studying political science and polling/elections, or anyone with a methodological background wishing to understand these topics at a greater depth." - Mark Payton, Rocky Vista University

"The book contains a list of 145 most recent references and a detailed index. Many exercises and appendices with mathematical derivations are given at the end of chapters. Numerous R scripts are presented throughout the whole monograph, providing ready-to-run or make-it-yourself tools for practical implementation of all the techniques. The book can be interesting not only to students in political sciences and statistical methods but to a wider audience interested to understand the results and checkup fairness of elections." - Stan Lipovetsky, Technometrics January 2021

Preface ix
Acknowledgments xiii
About the Author xv
Part I Estimating Electoral Support
1(93)
1 Polling 101
3(30)
1.1 Simple Random Sampling
5(1)
1.2 One Estimator of π: The Sample Proportion
6(6)
1.3 Reasonable Values of π
12(9)
1.4 A Second Estimator of π: Agresti-Coull
21(5)
1.5 SRS without Replacement
26(2)
1.6 Conclusion
28(1)
1.7 Extensions
29(1)
1.8
Chapter Appendix
30(3)
2 Polling 399
33(24)
2.1 Stratified Sampling
35(3)
2.2 The Mathematics of Estimating π
38(11)
2.3 Confidence Intervals
49(3)
2.4 Conclusion
52(1)
2.5 Extensions
53(1)
2.6
Chapter Appendix
54(3)
3 Combining Polls
57(26)
3.1 Simple Averaging of Polls
59(2)
3.2 Weighted Averaging of Polls
61(3)
3.3 Averaging of Polls over Time
64(6)
3.4 Looking Ahead
70(3)
3.5 South Korean 2017 Presidential Election
73(4)
3.6 Conclusion
77(1)
3.7 Extensions
78(1)
3.8
Chapter Appendix
79(4)
4 In-Depth Analysis: Brexit 2016
83(11)
4.1 Knowing Your Data
85(4)
4.2 Combining the Polls
89(1)
4.3 Discussion: What Went Wrong?
90(2)
4.4 Conclusion
92(2)
Part II Testing Election Results
94(101)
5 Digit Tests
95(30)
5.1 History
97(2)
5.2 The Benford Test
99(9)
5.3 The Generalized Benford Test
108(4)
5.4 Using the Generalized Benford Distribution
112(7)
5.5 Conclusion
119(1)
5.6 Extensions
120(1)
5.7
Chapter Appendix
121(4)
6 Differential Invalidation
125(24)
6.1 Differential Invalidation
127(5)
6.2 Regression Modeling
132(9)
6.3 Examining Cote d'Ivoire
141(2)
6.4 Conclusion
143(1)
6.5 Extensions
144(1)
6.6
Chapter Appendix
145(4)
7 Considering Geography
149(28)
7.1 Detecting Spatial Correlation
151(7)
7.2 The Spatial Lag Model
158(3)
7.3 Casetti's Spatial Expansion Model (SEM)
161(3)
7.4 Geographically Weighted Regression
164(5)
7.5 The Spatial Lagged Expansion Method
169(3)
7.6 Conclusion
172(1)
7.7 Extensions
173(1)
7.8
Chapter Appendix
174(3)
8 In-Depth Analysis: Sri Lanka Since 1994
177(18)
8.1 Differential Invalidation
179(1)
8.2 Methods and Data
179(3)
8.3 Results by Election
182(10)
8.4 Discussion
192(1)
8.5 Conclusion
193(2)
Bibliography 195(12)
Index 207
Ole J. Forsberg, BS, MAT, MA, MSE, PhDd, is an Assistant Professor of Mathematics-Statistics at Knox College in Galesburg, IL. He received a PhD in Political Science at the University of TennesseeKnoxville in 2006, concentrating in International Relations, War, and Terrorism. After finishing his dissertation, Dr Forsberg began a deeper investigation of the statistical techniques he used. As a result of that embarrassment, Dr Forsberg began statistical studies at the Johns Hopkins University (MSE, 2010) and concluded them with a PhD in Statistics from Oklahoma State University in 2014. His dissertation explored and applied applications of statistical techniques to testing elections for violations of the free and fair democratic claim. His research agenda lies in extending and applying statistical methods to modeling elections and testing the results for evidence of bias in election results.