MUC51 Probability and Statistics

Faculty of Science
Autumn 2024
Extent and Intensity
2/2/0. 4 credit(s). Type of Completion: zk (examination).
In-person direct teaching
Teacher(s)
RNDr. Marie Budíková, Dr. (lecturer)
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
Timetable
Mon 12:00–13:50 M1,01017
  • Timetable of Seminar Groups:
MUC51/01: Mon 14:00–15:50 M4,01024, Mon 15:00–15:50 MP1,01014, M. Budíková
MUC51/02: Wed 10:00–11:50 M6,01011, Wed 11:00–11:50 MP1,01014, M. Budíková
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
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.
Syllabus
  • 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.
Literature
    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.). 3rd ed. Brno: Masarykova univerzita, 2004, 127 pp. ISBN 80-210-3313-4. info
    recommended literature
  • BUDÍKOVÁ, Marie, Maria KRÁLOVÁ and Bohumil MAROŠ. Průvodce základními statistickými metodami (Guide to basic statistical methods). vydání první. Praha: Grada Publishing, a.s., 2010, 272 pp. edice Expert. ISBN 978-80-247-3243-5. URL 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 one test. The examination is written with "open book". It consists of 8 - 10 examples. The examination is scored 100 points. To successfully pass the exam, 51 points will suffice.
Language of instruction
Czech
Follow-Up Courses
Further Comments
Study Materials
The course is taught annually.
Listed among pre-requisites of other courses
The course is also listed under the following terms Autumn 2019, autumn 2021, Autumn 2022, Autumn 2023.
  • Enrolment Statistics (recent)
  • Permalink: https://is.muni.cz/course/sci/autumn2024/MUC51