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Statistical Methods for Data Analysis - JEM019

Title:	Statistical Methods for Data Analysis
Czech title:	Statistical Methods for Data Analysis
Guaranteed by:	Institute of Economic Studies (23-IES)
Faculty:	Faculty of Social Sciences
Actual:	from 2023
Semester:	summer
E-Credits:	6
Examination process:	summer s.:written
Hours per week, examination:	summer s.:2/2, Ex [HT]
Capacity:	unlimited / unlimited (unknown)
Min. number of students:	unlimited
4EU+:	no
Virtual mobility / capacity:	no
State of the course:	taught
Language:	English
Teaching methods:	full-time
Teaching methods:	full-time
Additional information:	https://dl1.cuni.cz/course/view.php?id=11676
Note:	course can be enrolled in outside the study plan enabled for web enrollment priority enrollment if the course is part of the study plan

Guarantor:	doc. RNDr. Zdeněk Hlávka, Ph.D.
Teacher(s):	doc. RNDr. Zdeněk Hlávka, Ph.D. doc. Ing. Marek Omelka, Ph.D.

Examination dates SS schedule Noticeboard

Annotation -

Last update: doc. RNDr. Zdeněk Hlávka, Ph.D. (22.09.2023)

Introduction to nonparametric, multivariate, and resampling methods. Statistical models, computational aspects, applications.

Literature -

Last update: doc. Ing. Marek Omelka, Ph.D. (08.02.2024)

Härdle, W. and Simar, L. (2015) Applied multivariate statistical analysis. Springer.

Kulich, M. and Omelka M. (2023) NMSA331: Mathematical statistics 1. https://www2.karlin.mff.cuni.cz/~omelka/Soubory/nmsa331/ms1_en.pdf

Mardia, K. V., Kent, J. T., and Bibby, J. M. (1979) Multivariate analysis. Academic Press Inc.

Nagy S.. (2023) NMSA332: Mathematical statistics 2. https://www.karlin.mff.cuni.cz/~nagy/NMSA332/NMSA332.pdf

Omelka, M. (2023) NMST424: Modern statistical methods. https://www.karlin.mff.cuni.cz/~omelka/Soubory/nmst434/nmst434_course-notes.pdf

Wasserman, L. (2006). All of nonparametric statistics. Springer Science & Business Media.

Further recommended reading:

Davison, A. C. and Hinkley, D. V. (1997) Bootstrap methods and their application. Cambridge University Press.

Efron, B. and Tibshirani, R. J. (1993) An Introduction to the bootstrap. Chapman & Hall.

Fan, J. and Gijbels, I. (1996) Local polynomial modelling and Its applications. Chapman & Hall/CRC.

Wand, M. P. and Jones, M. C. (1995) Kernel smoothing. Chapman & Hall.

Requirements to the exam -

Last update: doc. Ing. Marek Omelka, Ph.D. (27.03.2024)

(1) Written assignment (max 40 points) in which the student analyses real data set and solves a theoretical problem. The assignment has to be submitted at least 3 working days before the oral exam and will be graded by the examiner.

(2) Oral exam (max 60 points) focusing on all topics of the course, with an emphasis on the theoretical part and correct understanding. Three slots for oral exams will be available during the examination period May-June.

You need at least 51 points in total to pass the course. The final grade will be awarded based on your total number of points using the official faculty grading system, i.e.:

91– 100 pts Excellent A
81 – 90 pts Very good B
71 – 80 pts Good C
61 – 70 pts Satisfactory D
51 – 60 pts Satisfactory- E
0 – 50 pts Failed F

Syllabus -

Last update: doc. Ing. Marek Omelka, Ph.D. (21.04.2024)

Basic notions, t-tests and rank tests.

Resampling methods.

Nonparametric regression: kernel estimators of densities and regression curves.

Multivariate statistical methods I: random vectors, multivariate normal distribution, Hotelling's test, multiple testing.

Multivariate statistical methods II: principal components, factor analysis, discriminant and cluster analysis, further dimension reduction methods.

Other computational procedures.

Entry requirements

Last update: doc. Ing. Marek Omelka, Ph.D. (21.02.2024)

Basic courses in probability and mathematical statistics.

At least elementary knowledge of R computing environment (https://cran.r-project.org/).