PřF:MUC51 Probability and Statistics - Course Information
MUC51 Probability and StatisticsFaculty of Science
- Extent and Intensity
- 2/2/0. 4 credit(s). Type of Completion: zk (examination).
- Guaranteed by
- RNDr. Marie Budíková, Dr.
Department of Mathematics and Statistics - Departments - Faculty of Science
Supplier department: Department of Mathematics and Statistics - Departments - Faculty of Science
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- there are 7 fields of study the course is directly associated with, display
- Course objectives
- The aim of the subject is:
to acquaint students with the basic concepts of descriptive statistics and probability;
to show students interesting examples that they can later use in their teaching practice;
to teach students to use the STATISTICA systém.
- Learning outcomes
- After completing this course, students
- can obtain information from the data file in the form of tables, graphs and numerical characteristics;
- understand the basic concepts of probability, such as classical, geometric and conditional probability;
- are able to use important discrete and continuous probability distributions in appropriate situations;
- can calculate the mean, variance, covariance and correlation coefficient of discrete and continuous random variables;
- will have a good knowledge of STATISTICA system.
- Descriptive statistics. Basic and sample file, scalar and vector variables, functional and numerical characteristics of these variables.
- Theory of probability. Empirical law of large numbers, axiomatic definition of probability, basic properties of probability, classical, geometrical and conditional probability, stochastic independet events.
- Random variables and random vectors, discrete and continuous distributions. Transformations of random variables. Quantil, expected value, variance, covariance, correlation coefficient. Law of large numbers, central limit theorem.
- required literature
- BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Pavel OSECKÝ. Popisná statistika (Descriptive Statistics). 3., doplněné vyd. Brno: Masarykova univerzita, 1998. 52 pp. ISBN 80-210-1831-3. info
- BUDÍKOVÁ, Marie, Štěpán MIKOLÁŠ and Pavel OSECKÝ. Teorie pravděpodobnosti a matematická statistika. Sbírka příkladů. (Probability Theory and Mathematical Statistics. Collection of Tasks.). 3. vyd. Brno: Masarykova univerzita, 2004. 127 pp. ISBN 80-210-3313-4. info
- Teaching methods
- The weekly class schedule consists of 2 hour lecture and 2 hours of class exercises with special statistical software in computer classroom.
- Assessment methods
- During the semester, students write two tests. The examination is written with "open book". It consists of four examples. The examination is scored 100 points. To successfully pass the exam, 51 points will suffice.
- Language of instruction
- Follow-Up Courses
- Further Comments
- The course is taught annually.
The course is taught: every week.
- Enrolment Statistics (recent)
- Permalink: https://is.muni.cz/course/sci/autumn2019/MUC51