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Last update: Kubová Petra Ing. (22.01.2018)
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Last update: Kubová Petra Ing. (22.01.2018)
Students will know:
1. Understand basic principles of selected statistical methods for multivariate data analysis
2. Reconcile assumptions of particular methods.
3. Understand the results of the methods.
4. Perform essential calculations with specific data in specialized software (R).
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Last update: Kříž Pavel Mgr. Ing. Ph.D. (30.05.2019)
R: Härdle W. K., Simar L.: Applied Multivariate Statistical Analysis, Springer 2015, ISBN 978-3-662-45171-7.
R: Rencher A. C., Christensen W. F.: Methods of Multivariate Analysis, John Wiley & Sons 2012, ISBN: 978-0-470-17896-6.
A: Meloun M., Militký J., Hill M.: Počítačová analýza vícerozměrných dat v příkladech, Academia, Praha 2012, ISBN: 978-80-200-2071-0.
A: Hendl J.: Přehled statistických metod, Portál, Praha 2015, ISBN: 978-80-262-0981-2.
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Last update: Kříž Pavel Mgr. Ing. Ph.D. (30.05.2019)
Lecture notes on e-learning
Statistická analysa dat v R (lecture notes by Doc. Spiwok, VSCHT, in Czech) http://web.vscht.cz/~spiwokv/statistika/skripta.pdf |
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Last update: Kubová Petra Ing. (22.01.2018)
Lectures and seminars. |
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Last update: Kříž Pavel Mgr. Ing. Ph.D. (30.05.2019)
1. Data vector, data matrix and matrix algebra (multiplication, inverse matrix, eigenvalues and eigenvectors), covariance matrix. 2. Vizualisation of multidimensional data. 3. Exploratory data analysis (EDA). 4. Cluster analysis. 5. Principal component analysis (PCA). 6. Multidimensional scaling. 7. Multidimensional parameter estimation and hypothesis testing. Bayesian statistics. 8. Multivariate analysis of variance (MANOVA). 9. Regression methods 1 - multiple linear regression. 10. Regression methods 2 - principal component regression (PCR), generalized linear models (GLM). 11. Discriminant analysis. 12. Canonical correlation analysis. 13. Factor analysis (FA). |
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Last update: Kříž Pavel Mgr. Ing. Ph.D. (30.05.2019)
Students are expected to have either completed at least one of the prerequisite courses Applied Statistics or Statistical Data Analysis or possess the equivalent knowledge on probability theory and statistics prior to enrolling in the course. |
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Last update: Borská Lucie RNDr. Ph.D. (06.05.2019)
No requirements. |
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Last update: Kříž Pavel Mgr. Ing. Ph.D. (09.02.2018)
Credit for seminar project. Oral exam. |
Teaching methods | ||||
Activity | Credits | Hours | ||
Účast na přednáškách | 1 | 28 | ||
Příprava na přednášky, semináře, laboratoře, exkurzi nebo praxi | 0,5 | 14 | ||
Práce na individuálním projektu | 1 | 28 | ||
Příprava na zkoušku a její absolvování | 1,5 | 42 | ||
Účast na seminářích | 1 | 28 | ||
5 / 5 | 140 / 140 |