 |
| Detailinformationen |
| Quellcurriculum |
Bachelorstudium Artificial Intelligence 2019W |
| Ziele |
(*)Students will become familiar with the methods presented in the corresponding lecture and can use R for solving statistical problems
|
| Lehrinhalte |
(*)- Descriptive statistics: one and two dimensional characteristics
- Correlation and linear regression
- Basic concept of probability theorie including Bayes' theorem
- Theory of random variables
- Special distributions, test distribution
- Estimating parameters: point estimation and confidence interval estimation
- Statistical hypothesis testing
- Introduction into R
|
| Beurteilungskriterien |
(*)Homework assignments and presentations
|
| Lehrmethoden |
(*)Student presentations
|
| Abhaltungssprache |
Englisch |
| Literatur |
(*)- Slides provided by instructor
- Bortz, J.: Statistik für Human- und Sozialwissenschaftler. Springer, Heidelberg, in der aktuellen Auflage.
- Fahrmeir, L.; Künstler, R.; Pigeot, I.: Statistik – Der Weg zur Datenanalyse. Springer, Berlin, in der aktuellen Auflage.
- Hartung, J.; Elpelt, B.; Klösener, K.-H.: Statistik: Lehr- und Handbuch der angewandten Statistik. Oldenbourg, München, in der aktuellen Auflage.
- or any other introductory book to statistics
|
| Lehrinhalte wechselnd? |
Nein |
| Sonstige Informationen |
(*)Continuative courses:
- Statistics 2 (focus on parametric and non-parametric tests)
- Special Topics: Computer Assisted Statistics with SPSS (introduction into SPSS)
- Special Topics: Biostatistics in Clinical Research (introduction into biostatisics)
- Special Topics: Applied Biostatistics
- Special Topics: Statistics 3 (parametric and non-parametric analysis of variance, multiple linear and non-linear regression )
|
|
