Subjects

Your browser does not support JavaScript, or its support is disabled. Some features may not be available.

Mathematical Statistics - NMST701

Title:	Matematická statistika
Guaranteed by:	Department of Probability and Mathematical Statistics (32-KPMS)
Faculty:	Faculty of Mathematics and Physics
Actual:	from 2015
Semester:	summer
E-Credits:	2
Hours per week, examination:	summer s.:2/0, Ex [HT]
Capacity:	unlimited
Min. number of students:	unlimited
4EU+:	no
Virtual mobility / capacity:	no
State of the course:	taught
Language:	Czech
Teaching methods:	full-time

Guarantor:	RNDr. Jitka Zichová, Dr.
Teacher(s):	RNDr. Jitka Zichová, Dr.
Classification:	Mathematics > External Subjects, Probability and Statistics

Annotation -

An introductory course of mathematical statistics for students of chemistry at Charles University.

Last update: G_M (07.05.2014)

Aim of the course -

Learn basic principles of probability theory and mathematical

statistics.

Last update: G_M (07.05.2014)

Course completion requirements -

Written exam.

Last update: Zichová Jitka, RNDr., Dr. (20.05.2025)

Literature - Czech

Jiří Anděl: Statistické metody. Matfyzpress, Praha, 2007.

Jiří Anděl: Matematika náhody. Matfyzpress, Praha, 2000.

Karel Zvára, Josef Štěpán: Pravděpodobnost a matematická statistika.

Matfyzpress, Praha, 2002.

Karel Zvára: Biostatistika. Karolinum, Praha, 2008.

Karel Zvára: Základy statistiky v prostředí R. Karolinum, Praha, 2013.

Last update: Zichová Jitka, RNDr., Dr. (27.01.2016)

Teaching methods -

Lecture.

Last update: G_M (07.05.2014)

Requirements to the exam -

Written exam corresponding to the syllabus. Theory, basic ideas of hypothesis testing and linear regression, solving simple practical problems.

Last update: Zichová Jitka, RNDr., Dr. (23.05.2025)

Syllabus -

1) Introduction.

2) Descriptive statistics.

3) Basics of probability theory (random events, the definition of probability, conditional probability, independent events).

4) Random variable and its distribution. Characteristics of random variable. Examples of probability distributions.

5) Random vectors. Independent random variables, correlation.

6) Random sample. The law of large numbers. The central limit theorem.

7) Probabilistic and statistical approach in exploring real world. Estimates of the random variable characteristics.

8) Estimation theory. Hypothesis testing. Mathematical statistics as a basic tool for drawing conclusions from a scientific experimental work.

9) Selected statistical tests (one sample test, two sample test, paired test, some nonparametric tests, independence testing in contingency table.).

10) Linear regression model.

Last update: T_KPMS (02.06.2016)

Entry requirements -

Basics of calculus and linear algebra.

Last update: Zichová Jitka, RNDr., Dr. (20.05.2025)