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"Doing Statistical Analysis looks at three kinds of statistical research questions - descriptive, associational and inferential - and shows students how to conduct statistical analyses and interpret the results. Keeping equations to a minimum, it uses a conversational style and relatable examples such as football, covid-19 and tourism, to aid understanding. Each chapter contains practice exercises, and a section showing students how to reproduce the statistical results in the book using Stata and SPSS. Digital supplements consist of data sets in Stata, SPSS and Excel, practical videos explaining how to do basic analysis, and a test bank for instructors. Its accessible approach means this is the ideal textbook for undergraduate students across the social and behavioural sciences needing to build their confidence with statistical analysis"-- Provided by publisher

• The stakes
• Social physics and l'homme moyen
• The statistics of the deviant
• Data, intelligence, and policy
• Data's mathematical baptism
• Data at war
• Intelligence without data
• Volume, variety, and velocity
• Machines, learning
• The science of data
• The battle for data ethics
• Persuasion, ads, and venture capital
• Solutions beyond solutionism

"From facial recognition--capable of checking people into flights or identifying undocumented residents--to automated decision systems that inform who gets loans and who receives bail, each of us moves through a world determined by data-empowered algorithms. But these technologies didn't just appear: they are part of a history that goes back centuries, from the census enshrined in the US Constitution to the birth of eugenics in Victorian Britain to the development of Google search. Expanding on the popular course they created at Columbia University, Chris Wiggins and Matthew L. Jones illuminate the ways in which data has long been used as a tool and a weapon in arguing for what is true, as well as a means of rearranging or defending power. They explore how data was created and curated, as well as how new mathematical and computational techniques developed to contend with that data serve to shape people, ideas, society, military operations, and economies. Although technology and mathematics are at its heart, the story of data ultimately concerns an unstable game among states, corporations, and people. How were new technical and scientific capabilities developed; who supported, advanced, or funded these capabilities or transitions; and how did they change who could do what, from what, and to whom? Wiggins and Jones focus on these questions as they trace data's historical arc, and look to the future. By understanding the trajectory of data--where it has been and where it might yet go--Wiggins and Jones argue that we can understand how to bend it to ends that we collectively choose, with intentionality and purpose"-- Provided by publisher

• The stakes
• Social physics and l'homme moyen
• The statistics of the deviant
• Data, intelligence, and policy
• Data's mathematical baptism
• Data at war
• Intelligence without data
• Volume, variety, and velocity
• Machines, learning
• The science of data
• The battle for data ethics
• Persuasion, ads, and venture capital
• Solutions beyond solutionism

"From facial recognition--capable of checking people into flights or identifying undocumented residents--to automated decision systems that inform who gets loans and who receives bail, each of us moves through a world determined by data-empowered algorithms. But these technologies didn't just appear: they are part of a history that goes back centuries, from the census enshrined in the US Constitution to the birth of eugenics in Victorian Britain to the development of Google search. Expanding on the popular course they created at Columbia University, Chris Wiggins and Matthew L. Jones illuminate the ways in which data has long been used as a tool and a weapon in arguing for what is true, as well as a means of rearranging or defending power. They explore how data was created and curated, as well as how new mathematical and computational techniques developed to contend with that data serve to shape people, ideas, society, military operations, and economies. Although technology and mathematics are at its heart, the story of data ultimately concerns an unstable game among states, corporations, and people. How were new technical and scientific capabilities developed; who supported, advanced, or funded these capabilities or transitions; and how did they change who could do what, from what, and to whom? Wiggins and Jones focus on these questions as they trace data's historical arc, and look to the future. By understanding the trajectory of data--where it has been and where it might yet go--Wiggins and Jones argue that we can understand how to bend it to ends that we collectively choose, with intentionality and purpose."-- Publisher marketing

• Introduction /
• Chapter 1: The British Government's Approach to Population Data c1945-64 /
• Chapter 2: Government 1964-1979 and the Growth of the Modern Data State /
• Chapter 3: Government Data Systems 1964-1979 /
• Chapter 4: Government 1979-97: Developing and Re-purposing the Data State /
• Chapter 5: The British People, Government Data and the 1984 Data Protection Act /
• Chapter 6: Data Systems 1979-1997 (1), The Electoral Register, The Poll Tax Register and Data Linkage /
• Chapter 7: Data Systems 1979-1997 (2), Driving Licences and ID Cards /
• Chapter 8: Data Systems 1979-1997 (3), The Government Data Network, the Information Society Initiative and government.direct /
• Chapter 9: Government Data Security, the British People and Computers, and the 1998 Data Protection Act / Conclusion.
• (source: Nielsen Book Data)

This political history studies the phenomenal growth of the modern British state's interest in collecting, collating and deploying population data. It dates this biopolitical data turn in British politics to the arrival of the Labour government in 1964. It analyses government's increased desire to know the population, the impact this has had on British political culture and the institutions and systems introduced or modified to achieve this. It probes the political struggles around these initiatives to show that despite setbacks along the way and regardless of party, all British governments since the mid-1960s have accepted that data is the key to modern politics and have pursued it relentlessly.
(source: Nielsen Book Data)

• Chapter 1: Getting Started with Time Series.Chapter Goal: Exploring and analyzing the timeseries data, and preprocessing it, which includes feature engineering for model building.No of pages: 25Sub - Topics 1 Reading time series data2 Data cleaning3 EDA4 Trend5 Noise6 Seasonality7 Cyclicity8 Feature Engineering9 Stationarity
• Chapter 2: Statistical Univariate ModellingChapter Goal: The fundamentals of time series forecasting with the use of statistical modelling methods like AR, MA, ARMA, ARIMA, etc. No of pages: 25Sub - Topics 1 AR2 MA3 ARMA4 ARIMA5 SARIMA6 AUTO ARIMA7 FBProphet
• Chapter 3: Statistical Multivariate ModellingChapter Goal: implementing multivariate modelling techniques like HoltsWinter and SARIMAX.No of pages: 25Sub - Topics: 1 HoltsWinter 2 ARIMAX3 SARIMAX
• Chapter 4: Machine Learning Regression-Based Forecasting.Chapter Goal: Building and comparing multiple classical ML Regression algorithms for timeseries forecasting.No of pages: 25Sub - Topics: 1 Random Forest2 Decision Tree3 Light GBM4 XGBoost5 SVM
• Chapter 5: Forecasting Using Deep Learning.Chapter Goal: Implementing advanced concepts like deep learning for time series forecasting from scratch.No of pages: 25Sub - Topics: 1 LSTM 2 ANN3 MLP.
• (source: Nielsen Book Data)

This book teaches the practical implementation of various concepts for time series analysis and modeling with Python through problem-solution-style recipes, starting with data reading and preprocessing. It begins with the fundamentals of time series forecasting using statistical modeling methods like AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average), and ARIMA (autoregressive integrated moving-average). Next, you'll learn univariate and multivariate modeling using different open-sourced packages like Fbprohet, stats model, and sklearn. You'll also gain insight into classic machine learning-based regression models like randomForest, Xgboost, and LightGBM for forecasting problems. The book concludes by demonstrating the implementation of deep learning models (LSTMs and ANN) for time series forecasting. Each chapter includes several code examples and illustrations. After finishing this book, you will have a foundational understanding of various concepts relating to time series and its implementation in Python. What You Will Learn Implement various techniques in time series analysis using Python. Utilize statistical modeling methods such as AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average) and ARIMA (autoregressive integrated moving-average) for time series forecasting Understand univariate and multivariate modeling for time series forecasting Forecast using machine learning and deep learning techniques such as GBM and LSTM (long short-term memory) Who This Book Is ForData Scientists, Machine Learning Engineers, and software developers interested in time series analysis.
(source: Nielsen Book Data)

Erik Nook, PhD, assistant professor, Princeton University, discusses using experimental methods in statistical research, including research methods commonly used, method-related advice received and given.

• Understanding the American Community Survey. Some Key Facts about the ACS ; New Opportunities ; New Challenges ; Data Collection Versus Data Reporting ; The ACS Sample ; Geography ; Data Comparability ; Subjects Covered ; Availability of ACS Estimates
• Using the ACS. Differences between the ACS and the Decennial Census ; Residence Rules ; Reference Periods ; Period Estimates ; Deciding Which ACS Estimate to Use ; Margin of Error
• Accessing ACS Data Online
• Part A. Who--age, race/ethnicity, and household structure. Highlights ; Rankings ; Column headings ; Table A-1. States ; Table A-2. Counties with Populations of 20,000 or More ; Table A-3. Metropolitan Areas ; Table A-4. Cities with Populations of 20,000 or More
• Part B. What
• Education, Employment and Income. Highlights ; Rankings ; Column Headings ; Table B-1. States ; Table B-2. Counties with Populations of 20,000 or More ; Table B-3. Metropolitan Areas ; Table B-4. Cities with Populations of 20,000 or More
• Part C. Where
• Migration, Housing, and Transportation. Highlights ; Rankings ; Column Headings ; Table C-1. States ; Table C-2. Counties with Populations of 20,000 or More ; Table C-3. Metropolitan Areas ; Table C-4. Cities with Populations of 20,000 or More
• Appendixes. A: Geographic Concepts and Codes ; B: Source Notes and Explanations ; C: Metropolitan Statistical Areas, Metropolitan Divisions, and Components

"The Who, What, and Where of America is designed to provide a sampling of key demographic information. It covers the United States as a whole, every state, each metropolitan statistical area, and all the counties and cities with a population of 65,000 or more."-- Page 4 of cover

• [1] Synthèse des résultats définitifs
• [2] Chiffres clés des résultats définitifs
• [3] Données clés 2019

• 1. Introduction to Missing Data 1.1 Chapter Overview 1.2 Missing Data Patterns 1.3 Missing Data Mechanisms 1.4 Diagnosing Missing Data Mechanisms 1.5 Auxiliary Variables 1.6 Analysis Example: Preparing for Missing Data Handling 1.7 Older Missing Data Methods 1.8 Comparing Missing Data Methods via Simulation 1.9 Planned Missing Data 1.10 Power Analyses for Planned Missingness Designs 1.11 Summary and Recommended Readings
• 2. Maximum Likelihood Estimation 2.1 Chapter Overview 2.2 Probability Distributions versus Likelihood Functions 2.3 The Univariate Normal Distribution 2.4 Estimating Unknown Parameters 2.5 Getting an Analytic Solution 2.6 Estimating Standard Errors 2.7 Information Matrix and Parameter Covariance Matrix 2.8 Alternative Approaches to Estimating Standard Errors 2.9 Iterative Optimization Algorithms 2.10 Linear Regression 2.11 Significance Tests 2.12 Multivariate Normal Data 2.13 Categorical Outcomes: Logistic and Probit Regression 2.14 Summary and Recommended Readings
• 3. Maximum Likelihood Estimation with Missing Data 3.1 Chapter Overview 3.2 The Multivariate Normal Distribution Revisited 3.3 How Do Incomplete Data Records Help? 3.4 Standard Errors with Incomplete Data 3.5 The Expectation Maximization Algorithm 3.6 Linear Regression 3.7 Significance Testing 3.8 Interaction Effects 3.9 Curvilinear Effects 3.10 Auxiliary Variables 3.11 Categorical Outcomes 3.12 Summary and Recommended Readings
• 4. Bayesian Estimation 4.1 Chapter Overview 4.2 What Makes Bayesian Statistics Different? 4.3 Conceptual Overview of Bayesian Estimation 4.4 Bayes' Theorem 4.5 The Univariate Normal Distribution 4.6 MCMC Estimation with the Gibbs Sampler 4.7 Estimating the Mean and Variance with MCMC 4.8 Linear Regression 4.9 Assessing Convergence of the Gibbs Sampler 4.10 Multivariate Normal Data 4.11 Summary and Recommended Readings
• 5. Bayesian Estimation with Missing Data 5.1 Chapter Overview 5.2 Imputing an Incomplete Outcome Variable 5.3 Linear Regression 5.4 Interaction Effects 5.5 Inspecting Imputations 5.6 The Metropolis-Hastings Algorithm 5.7 Curvilinear Effects 5.8 Auxiliary Variables 5.9 Multivariate Normal Data 5.10 Summary and Recommended Readings
• 6. Bayesian Estimation for Categorical Variables 6.1 Chapter Overview 6.2 Latent Response Formulation for Categorical Variables 6.3 Regression with a Binary Outcome 6.4 Regression with an Ordinal Outcome 6.5 Binary and Ordinal Predictor Variables 6.6 Latent Response Formulation for Nominal Variables 6.7 Regression with a Nominal Outcome 6.8 Nominal Predictor Variables 6.9 Logistic Regression 6.10 Summary and Recommended Readings
• 7. Multiple Imputation 7.1 Chapter Overview 7.2 Agnostic versus Model-Based Multiple Imputation 7.3 Joint Model Imputation 7.4 Fully Conditional Specification 7.5 Analyzing Multiply-Imputed Data Sets 7.6 Pooling Parameter Estimates 7.7 Pooling Standard Errors 7.8 Test Statistic and Confidence Intervals 7.9 When Might Multiple Imputation Give Different Answers? 7.10 Interaction and Curvilinear Effects Revisited 7.11 Model-Based Imputation 7.12 Multivariate Significance Tests 7.13 Summary and Recommended Readings
• 8. Multilevel Missing Data 8.1 Chapter Overview 8.2 Random Intercept Regression Models 8.3 Random Coefficient Models 8.4 Multilevel Interaction Effects 8.5 Three-Level Models 8.6 Multiple Imputation 8.7 Joint Model Imputation 8.8 Fully Conditional Specification Imputation 8.9 Maximum Likelihood Estimation 8.10 Summary and Recommended Readings
• 9. Missing Not at Random Processes 9.1 Chapter Overview 9.2 Missing Not at Random Processes Revisited 9.3 Major Modeling Frameworks 9.4 Selection Models for Multiple Regression 9.5 Model Comparisons and Individual Influence Diagnostics 9.6 Selection Model Analysis Examples 9.7 Pattern Mixture Models for Multiple Regression 9.8 Pattern Mixture Model Analysis Examples 9.9 Longitudinal Data Analyses 9.10 Diggle-Kenward Selection Model 9.11 Shared Parameter (Random Coefficient) Selection Model 9.12 Random Coefficient Pattern Mixture Models 9.13 Longitudinal Data Analysis Examples 9.14 Summary and Recommended Readings
• 10. Special Topics and Applications 10.1 Chapter Overview 10.2 Descriptive Summaries, Correlations, and Subgroups 10.3 Non-Normal Predictor Variables 10.4 Non-Normal Outcome Variables 10.5 Mediation and Indirect Effects 10.6 Structural Equation Models 10.7 Scale Scores and Missing Questionnaire Items 10.8 Interactions with Scales 10.9 Longitudinal Data Analyses 10.10 Regression with a Count Outcome 10.11 Power Analyses for Growth Models with Missing Data 10.12 Summary and Recommended Readings
• 11. Wrap-Up 11.1 Chapter Overview 11.2 Choosing a Missing Data-Handling Procedure 11.3 Software Landscape 11.4 Reporting Results from a Missing Data Analysis 11.5 Final Thoughts and Recommended Readings Appendix. Data Set Descriptions Author Index Subject Index About the Author.
• (source: Nielsen Book Data)

*The most accessible guide to a tough methodological issue, fully updated: more than 85% new material includes 5 new chapters plus new topics. *Includes the latest developments, a retooled organization, and dozens of new datasets. *Translates cutting-edge procedures into language accessible to anyone doing behavioral or social research. *For graduate students and applied researchers; of special interest in the U.S., the Netherlands, Japan, South Korea, and China.
(source: Nielsen Book Data)

'Essential reading ... An incisive and urgently needed book' Tim Harford '[An] entertaining introduction to the uses (and misuses) of data ... a penetrating analysis of why statistical literacy matters to our politics and our daily lives' Professor Jonathan Portes Our politicians make vital decisions and declarations every day that rely on official data. But should all statistics be trusted? In BAD DATA, House of Commons Library statistician Georgina Sturge draws back the curtain on how governments of the past and present have been led astray by figures littered with inconsistency, guesswork and uncertainty. Discover how a Hungarian businessman's bright idea caused half a million people to go missing from UK migration statistics. Find out why it's possible for two politicians to disagree over whether poverty has gone up or down, using the same official numbers, and for both to be right at the same time. And hear about how policies like ID cards, super-casinos and stopping ex-convicts from reoffending failed to live up to their promise because they were based on shaky data. With stories that range from the troubling to the empowering to the downright absurd, BAD DATA reveals secrets from the usually closed-off world of policy-making. It also suggests how - once we understand the human story behind the numbers - we can make more informed choices about who to trust, and when.
(source: Nielsen Book Data)

• Consideraciones preliminares
• Contextualización de la división político administrativa en Bolivia
• Documentos que constituyen fuentes de datos poblaciones en Bolivia
• Estrategias para el poblamiento en Bolivia : siglo XIX y XX
• Instituto Nacional de Estadística de Bolivia
• Censos poblaciones en Bolivia
• Datos censales a nivel nacional en Bolivia.

• Pt. 1. Comptabilité marchande, secret des affaires et besoins statistiques de l'état sous l'ancien régime
• Chiffres publics, chiffres privés. Quelle pertinence de telles catégories pour l'économie d'Ancien Régime
• Comptabilité marchande et secret des affaires au XVe siècle. Entre privé et public
• Compter, vérifier, prouver. Usages et publicité des chiffres privés dans le monde de la pêche (Dieppe, XVe siècle)
• Publication et publicité de la propriété privée. Le rôle du cadastre sarde dans l'élaboration d'une nouvelle politique fiscale (Savoie, XVe siècle)
• Le chiffre aux n Isles". Les recensements de population sous l'Ancien Régime dans les sociétés coloniales et esclavagistes
• Pt. 2. -- L'essor des statistiques publiques à partir de données privées (XIXe-premier XXe siècle)
• Vers une hégémonie des statistiques publiques ?
• La longue marche des chiffres privés aux statistiques publiques. L'invention de la Centrale des risques bancaires à la française (années 1930-années 1990)
• Des statistiques privées aux statistiques publiques. Brève histoire de l'indice des prix à la consommation en Argentine
• Pt. 3. La publicité des statistiques nationales et leur utilisation par des organisations internationales, de l'entre-deux-guerres à la fin de la guerre froide
• Grèves et grévistes dans les statistiques internationales. L'art compilatoire du Bureau international du travail (1920-1939)
• La dé-mesure d'une ambition. Les chiffres socialistes au service de la construction du Bloc
• Pt. 4. Contestations des statistiques publiques et résistances à la privatisation des normes comptables marchandes et publiques
• Contestations et hybridation des chiffres publics
• L'acteur privé dans les mutations de la mesure de l'inflation
• Comptabilités privée et publique
• Le modèle de la double hélice
• La comptabilité-monde : quand le cher occulte le territoire. Une mobilisation civile contre la privatisation contemporaine du chiffre comptable.

"Sont étudiées ici, de façon interdisciplinaire, les origines et les finalités des chiffres dits publics et des chiffres dits privés. En analysant leurs conditions, historiquement situées, de production, de diffusion et d'utilisation, leurs définitions s'avèrent instables et leurs frontières poreuses. Des institutions publiques ont besoin de la collaboration de particuliers et d'entreprises pour produire des statistiques rendues publiques. Certains chiffres demeurent confidentiels pendant une certaine période avant leur publicisation. Des acteurs privés utilisent des données publiques pour produire des chiffres à usage privé. À cette privatisation de chiffres publics s'ajoute une reprivatisation de données privées après leur publicité. Si des conflits opposent parfois les producteurs privés et publics de données, les deux catégories de chiffres résultent principalement de compromis, d'emprunts, de transferts et de co-construction. De l'Ancien Régime à nos jours, l'ouvrage aborde, en France, dans son empire et à l'étranger, les secrets des affaires et des statistiques, les recensements, les indices des prix, le cadastre, le contrôle du bilan des entreprises, la privatisation des normes comptables publiques et privées, et le développement d'acteurs privés de la statistique. Ce volume met ainsi en évidence, sur la longue durée, la singularité de notre époque qui apparaît comme une ère nouvelle : celle du chiffre hybride, avec ses antagonismes d'usages et de légitimités."--Page 4 of cover.

• Every body matters
• The birth of death as we know it
• The math of after
• The power of a name
• The temple of time

The global doubling of human life expectancy between 1850 and 1950 is arguably one of the most consequential developments in human history, undergirding massive improvements in human life and lifestyles. In 1850, Americans died at an average age of 30. Today, the average is almost 80. This story is typically told as a series of medical breakthroughs Jenner and vaccination, Lister and antisepsis, Snow and germ theory, Fleming and penicillin, but the lion's share of the credit belongs to the men and women who dedicated their lives to collecting good data. Examining the development of death registration systems in the United States-from the first mortality census in 1850 to the development of the death certificate at the turn of the century-Count the Dead argues that mortality data transformed life on Earth, proving critical to the systemization of public health, casualty reporting, and human rights. Stephen Berry shows how a network of coroners, court officials, and state and federal authorities developed methods to track and reveal patterns of dying. These officials harnessed these records to turn the collective dead into informants and in so doing allowed the dead to shape life and death as we know it today.
(source: Nielsen Book Data)

• Stories in the data
• The question men
• Names and negotiations
• Partners
• Counting with friends
• Silences and white supremacy
• Uncle Sam v. Senator Tobey
• The inventory and the arsenal
• The data's depths

"A lesson in reading between the lines of the U.S. census to uncover the stories behind the data"-- Provided by publisher

• Contents: Introduction: Spatial analysis and the social sciences in a rapidly changing landscape / Sergio J. Rey and Rachel S. Franklin
• Part 1. Theory, frameworks and foundations
• 1. Giscience through the looking glass / Barbara P. Buttenfield
• Locating spatial data in the social sciences / Jonathan Reades
• Analytical environments / Roger Bivand
• Complexity / Li An
• Linking spatial patterns to processes / Colin Robertson and Jed Long
• Part 2. Methods. Spatial econometrics / Luc Anselin
• Local modeling in a regression framework / Mehak Sachdeva, Taylor Oshan and A. Stewart Fotheringham
• Simulating geographical systems using cellular automata and agent-based models / Alison Heppenstall, Andrew Crooks, Ed Manley and Nick Malleson
• Microsimulation / Nik Lomax
• Multilevel models / Richard Harris
• Context-dependent movement analysis / Somayeh Dodge
• Spatial interaction modeling / Taylor Oshan
• Spatial optimization / Alan T. Murray
• Cluster identification / Edward Helderop and Tony H. Grubesic
• Spatial point patterns / Stuart Sweeney and Sophia Arabadjis
• Spatial dynamics / Wei Kang
• Geoai in social science / Wenwen Li
• Exploratory spatial data analysis / Ran Wei
• Geovisualization and geovisual analysis / Alasdair Rae
• Immersive virtual reality and spatial analysis / Trevor M. Harris
• Spatiotemporal data mining / Arun Sharma, Zhe Jiang and Shashi Shekhar
• Part 3. Applications. Neighborhood change / Elizabeth Delmelle
• The spatial analysis of gentrification: Formalizing geography in models of a multidimensional urban process / Elijah Knaap
• Social networks in space / Clio Andris and Dipto Sarkar
• Analysing the dynamics of inter-regional inequality: The case of Canada / Sébastien Breau
• Spatial approaches to energy poverty / Caitlin Robinson
• The shape of bias: Understanding the relationship between compactness and bias in U.S. Elections / Levi John Wolf
• Space and new urbanism / Emily Talen
• Space for wellbeing / Victoria Houlden
• Urban analytics: History, trajectory and critique / Geoff Boeing, Michael Batty, Shan Jiang and Lisa Schweitzer
• Part 4. Emerging challenges and issues. Reproducibility and replicability in spatial science / Michael F. Goodchild
• An image library: The potential of imagery in (quantitative) social sciences / Daniel Arribas-Bel, Francisco Rowe, Meixu Chen and Sam Comber
• Uncertainty / David C. Folch
• Index

"Providing an authoritative assessment of the current landscape of spatial analysis in the social sciences, this cutting-edge Handbook covers the full range of standard and emerging methods across the social science domain areas in which these methods are typically applied. Accessible and comprehensive, it expertly answers the key questions regarding the dynamic intersection of spatial analysis and the social sciences. The chapters are split into insightful sections dedicated to foundational background material, methods, social science applications and the challenges on the horizon, using state-of-the-art coverage of the traditional and novel spatial methods. Leading scholars in the field use a range of applications to illustrate the diverse ways in which spatial analysis methods can inform research in the field of social sciences. Furthermore, the Handbook discusses the key challenges to that research including uncertainty, reproducibility and replicability. This Handbook of Spatial Analysis in the Social Sciences will be an excellent informative resource for scholars in the fields of geography, social sciences and public health. Established and early career researchers of the social sciences alike will appreciate the detailed overview of the methods and applications as well as the ability to expand their methodological knowledge"-- Provided by publisher

• Introduction.- 1 Power and Morality.- 2 On the History of Statistics and Policy Making.- 3 Ethical Norms for Statistical Work.- 4 Censuses in the Soviet Union and Afterwards.- 5 Population Statistics and the Final Solution under National Socialism.- 6 Greece's Reckoning with an Honourable Statistician.- 7 Digitalisation and a Pandemic.- 8 Globalisation and Official Statistics.- 9 Ireland's Miraculous Economic Growth.- 10 Happiness and Happiness Researchers.- 11 National Egoism or International Cooperation.- 12 World-View Statistics.- Conclusions.- Bibliography.
• (source: Nielsen Book Data)

This book describes official statistics as a tool to hold up a mirror to society - but also as an instrument for those who can manipulate this mirror. It addresses the precarious interaction of politics, official statistics, and ethical principles. Three sets of themes can be derived from this relationship, which are the focus of this book: Political systems and guiding principles, official statistics as a science of the state, and ethical issues arising from them. Ultimately, the determining factor is the political system that exists in each case. The book contains eleven chapters. The first three focus on the key concepts of the book: power and morality, official statistics and policy making, and ethical principles for statistical work. Three further chapters focus on episodes that illustrate, as "drastic" examples, the misuse of official statistics over the past hundred years, covering the situation in the Soviet Union, the Third Reich and Greece. The remaining five chapters take up current topics that pose particular challenges to official statistics. These are the phenomena paraphrased by digitalisation, globalisation, happiness research, overpopulation, migration, the Covid-19 pandemic and climate change. The book is primarily aimed at statisticians working in national and international statistical institutions, but also at readers interested in statistics, national accounts, economic and statistics history, and ethical issues.
(source: Nielsen Book Data)