Statistical inference 1100-1BF22

http://brain.fuw.edu.pl/edu/index.php/Wnioskowanie_Statystyczne_-_wykład

1. Probability: definitions and basic distributions
2. Central limit theorem
3. Statistics and estimators
4. Statistical hypothesis testing
5. Nonparametric tests
6. Maximum likelihood method
7. Resampling statistics
8. Permutation tests

Course coordinators

Monika Szukałowicz

Mode

Classroom

Prerequisites (description)

Required credits from Information and Communication Technologies (Technologie informacyjne i komunikacyjne), with 45 hours of practical exercises in Python programming

Learning outcomes

Lecture:
understanding basic statistical methods underlying widely used statistical hypothesis testing schemes.

Exercises:
Implementing basic statistical techniques for hypotheses testing, using freely available Python interpreters with proper libraries

Assessment criteria

If the session will be held remotely: written exam at kampus-egzaminy.ckc.uw.edu.pl (with parallel video connection via google meet) plus oral exam via google meet (or another platform, authorized by the University).

Otherwise: written exam in the lecture hall: open questions and single choice test.

Activity during lectures and attention to ev. homeworks will be also taken into account.

Positive marks from the lecture (as above) AND practical exercises are required to pass.

Practical placement

Bibliography

Lecture:
http://brain.fuw.edu.pl/edu/STAT:Wnioskowanie_statystyczne

Lab:
http://brain.fuw.edu.pl/edu/STATLAB:%C4%86wiczenia_ze_statystyki