Syllabus
1. Basic concepts of probability and statistics: random variable and its distribution, Bayes theorem, correlation
2. Linear regression, contingency tables
3. Data visualization
4. Paradoxes and classical statistical problems: e.g. Von Neumann’s unfair coin, voting paradoxes, German tank problem
5. Practical examples of application and correct interpretation of statistical models from disciplines including medicine, industrial production, sport, criminology, education, etc.
Annotation
Principles of statistical thought in obtaining conclusions under uncertainty will be exposed on selected real examples of decision, learning, and prediction problems.