Mathematical Statistics - NMST701
Title: Matematická statistika
Form of teaching: lecture
Guaranteed by: Department of Probability and Mathematical Statistics (32-KPMS)
Faculty: Faculty of Mathematics and Physics
Actual: from 2015
Duration in semesters: 1
Semester: summer
E-Credits: 2
Hours per week, examination: summer s.:2/0, Ex [HT]
Capacity: unlimited
Maximum number of enrolled students: unlimited
Min. number of students: unlimited
4EU+: no
Virtual mobility / capacity: no
State of the course: taught
Language: Czech
Teaching methods: full-time
Repeated enrollment: 2 / 2 / 2 / 2
Guarantor: RNDr. Jitka Zichová, Dr.
Teacher(s): RNDr. Jitka Zichová, Dr.
Classification: Mathematics > External Subjects, Probability and Statistics
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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: Zichová Jitka, RNDr., Dr. (13.05.2026)
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)
Course assessment methods and requirements for successful completion, grading scheme -

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: Zichová Jitka, RNDr., Dr. (13.05.2026)
Course registration requirements -

Basics of calculus and linear algebra.

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