Statistics Today: Everyday Applications, Research Questions, Insights, and Challenges (Society, Environment and Statistics)

Preface
	Preparation of the English Version
	Acknowledgment
Contents
Contributors
Part I Human Life, Medicine, and Genetics
	1 Season of Birth and Human Longevity: A New Theory Why Children Born in November Live Longer
		1.1 The Date of Birth Matters
		1.2 A New Look from Switzerland
		1.3 In Search of the Reasons
		1.4 Data Limitations
		1.5 Further Reading
	2 Where Do Drugs Work in the Body? A Systematic Statistical Data Analysis
		2.1 Pharmacokinetics and the Pre-clinical Phase
		2.2 Standard Procedure for Statistical Data Analysis
		2.3 The Distribution of Drugs in the Body
			2.3.1 Business Understanding
			2.3.2 Data Understanding
			2.3.3 Data Preparation
			2.3.4 Modeling
			2.3.5 Evaluation
			2.3.6 Deployment
		2.4 Further Reading
	3 Drug Studies: Using Statistics to Achieve the Optimal Dose
		3.1 The Three Clinical Test Phases
		3.2 The Optimization of Phase 2
		3.3 It is All About the Experimental Design
		3.4 Towards Practical Application
		3.5 Further Reading
	4 Statistical Alarm Systems in Intensive Care Medicine
		4.1 Alarms in Acute Medical Care
		4.2 Smoothing as Part of Data Preprocessing
		4.3 Joint Analysis of Characteristics
		4.4 Validation of the Results
		4.5 Further Reading
	5 Personalized Medicine: How Statistics Helps Not to Drown in the Flood of Data
		5.1 Genetic Decision Support in Medicine
		5.2 Efficacy and Side Effects of Therapies
		5.3 In Search for Genetic Patterns
		5.4 Statistical Tricks
		5.5 Medical Application
		5.6 Models for Estimating Disease Progression
		5.7 Summary
		5.8 Further Reading
	6 Modulating Genetic Effects on Bladder Cancer Risk in an Area of Coal, Iron, and Steel Industries
		6.1 Environment, Genes, and Urinary Bladder Cancer
		6.2 Epidemiology and Genetics
		6.3 Gene-Environment Interactions
		6.4 Conclusion
		6.5 Further Reading
	7 Statistics and the Maximum Human Lifespan
		7.1 Background
		7.2 From the Average to Extreme Value Theory
		7.3 Challenges in Working with Demographic Data
		7.4 Results
		7.5 Conclusion
		7.6 Further Reading
Part II Sports and Entertainment
	8 Statistics and Soccer
		8.1 More Goals by Means of Statistics
		8.2 A Statistical Model for Predicting Goals
		8.3 Influential Variables
		8.4 Conclusion
		8.5 Further Reading
	9 The Players\' Anxiety at the Penalty Kick: Who Is the Best Penalty Taker, Who the Best Goalkeeper?
		9.1 Penalties in Soccer
		9.2 The Penalty Data Set
		9.3 Factors Associated with the Outcome of the Penalty
			9.3.1 Modeling Penalty Probabilities
			9.3.2 Fixed and Random Effects
			9.3.3 Important Factors
		9.4 Rankings of Goalkeepers and Penalty Takers
			9.4.1 Penalty Takers\' Influence as Random Effect
			9.4.2 Leaderboards
		9.5 Conclusion and Outlook
		9.6 Further Reading
	10 Music Data Analysis
		10.1 What Is Music?
		10.2 Music Data
		10.3 The Studies
			10.3.1 Classification
			10.3.2 Pitch Identification
			10.3.3 Instrument Recognition
			10.3.4 Onset Detection
			10.3.5 Automatic Transcription
			10.3.6 Genres
		10.4 Further Reading
	11 Statistics and Horse Race Betting: Favorites vs. Longshots
		11.1 Horse Race Betting
		11.2 Betting Payouts
		11.3 Empirical Explanations for the Favorite-Longshot Bias
		11.4 Favorite-Longshot-Bias Caused by Subjective Estimates
		11.5 Conclusion
		11.6 Further Reading
	12 The Statistics of the German 6/49 Lotto
		12.1 Lotto as an Investment
		12.2 Optimizing the Payout
		12.3 Further Reading
Part III Money and Business
	13 Statistics at the Stock Exchange
		13.1 Beware of Dependencies
		13.2 Investing in Stocks
		13.3 Time-Varying Dependencies
		13.4 The Not So Normal Normal Distribution
		13.5 Cointegration
		13.6 Further Reading
	14 Statistics in the Risk Assessment of Bank Portfolios
		14.1 The Problem
		14.2 Expected Shortfall Compared to Value-at-Risk
		14.3 Estimation of Risk Measures
		14.4 Validation of Risk Models
		14.5 Further Reading
	15 On Rating the Raters: Statistics in the Rating Industry
		15.1 Obligations and Obligors
		15.2 How to Judge the Quality of Default Forecasts?
		15.3 A Numerical Example
		15.4 Partial Orderings of Probability Forecasts
		15.5 Scalar Valued Measures of Forecasting Quality
		15.6 Further Reading
	16 Gross Domestic Product, Greenhouse Gas Emissions, and Global Warming
		16.1 Economic Activity and Emissions
		16.2 Statistical Analysis
		16.3 Parameter Estimation in the Presence of Nonlinear Cointegration
		16.4 Interpretation
		16.5 Further Reading
Part IV Nature and Technology
	17 Flood Statistics: Still on the River Bank or Already in the Water?
		17.1 Getting a Grip on Floods
		17.2 What Is a Flood?
		17.3 Flood Risk and Probabilities
		17.4 Robust Estimation
		17.5 Flood Types and Changes Over Time
		17.6 Regionalization
		17.7 Further Reading
	18 How Statistics Helps to Reduce Rejects
		18.1 Defects in Deep Drilling
		18.2 Quality Improvement: Six Sigma
			18.2.1 Define
			18.2.2 Measure
			18.2.3 Analyze
			18.2.4 Improve
			18.2.5 Control
		18.3 Further Reading
	19 Statistics and Reliability of Technical Products
		19.1 Reliability and Randomness
		19.2 Simple Service Lifetime Analysis
		19.3 Lifetime Analysis Under Different Loads
		19.4 Lifetime Analysis for Products with Several Components
		19.5 Prediction Intervals
		19.6 Outlook
		19.7 Further Reading
	20 Durable Machine Components: How Statistical Design of Experiments Optimizes Wear Protection
		20.1 Wear Protection Through Coating
		20.2 Optimization Through Statistical Design of Experiments
		20.3 Challenges in Real Coating Processes
		20.4 Further Reading
Part V Intricacies of Measurement
	21 Measuring the Immeasurable: Statistics, Intelligence, and Education
		21.1 Educational Tests and Education
		21.2 Latent Variables and Their Indicators
		21.3 A Statistical Model for Learning Progress Diagnostics
		21.4 From Data to Latent Variables
		21.5 Further Reading
	22 Uncovering Embarrassing Truths Through Statistics
		22.1 The Method of Indirect Questioning
		22.2 A Modification of the Original Idea
		22.3 Tasks for Future Research
		22.4 Further Reading
	23 Samples and Missing Data
		23.1 Sampling in Theory and Practice
		23.2 Statistical Methods to Compensate for Non-responses
		23.3 Further Reading
Part VI Language Data
	24 Who Is Supposed to Read All This? Automatic Analysis of Text Data
		24.1 Large Text Collections
		24.2 Text Analysis in the Social Sciences
		24.3 Preprocessing of Text Data
		24.4 Topic-Based Classification of Large Text Collections
		24.5 Finding Differences
		24.6 Text Analysis of Election Programs
		24.7 Summary and Outlook
		24.8 Further Reading
	25 Statistical Modeling of Current Linguistic Realities Around the World: The Case of Singapore
		25.1 Singapore and the World Englishes Paradigm
		25.2 Data Collection and Preparation
		25.3 Prediction of Linguistic Characteristics
			25.3.1 Design of Experiments
			25.3.2 Variation in British and Singaporean Englishes
				Past Tense Marking
				Subject Pronoun Realization
				Overall Findings
		25.4 Evaluation and Interpretation
		25.5 Summary and Outlook
		25.6 Further Reading
	26 Linguistic Manifestations of Cultural Differences Across National Varieties of English: A Methodological Survey
		26.1 Introduction
		26.2 Electronic Corpora: Representative Text Collections?
		26.3 Linguistic Forms Representing Cultural Orientations
			26.3.1 Example of a Cultural Dimension: Collectivism
			26.3.2 Example of a Structural Scheme: Recipientless Constructions
		26.4 Statistical Testing
		26.5 Sample Results
		26.6 Summary and Conclusion
		26.7 Further Reading
Part VII From Here to Where?
	27 Is Data Science More Than Statistics? The Bigger Picture
		27.1 Data Science: What Is It Anyway?
		27.2 Data Science: Steps
			27.2.1 General Structure
			27.2.2 Data Acquisition and Enrichment
			27.2.3 Data Exploration
			27.2.4 Modeling: Statistical Data Analysis
			27.2.5 Evaluation: Model Validation and Selection
			27.2.6 Deployment of Results
		27.3 Conclusion
		27.4 Further Reading
Index