Statistics and Results Evaluation
Code  Completion  Credits  Range  Language 

F7PMLSVV  Z,ZK  4  2P+2C  Czech 
 Relations:
 In order to register for the course F7PMLAS, the student must have successfully completed or received credit for and not exhausted all examination dates for the course F7PMLSVV.
 Course guarantor:
 Michaela Mrázková, Marek Piorecký
 Lecturer:
 Marek Piorecký, Jan Štrobl
 Tutor:
 Michaela Mrázková, Tomáš Nagy, Marek Piorecký
 Supervisor:
 Department of Biomedical Technology
 Synopsis:

The aim of the course is to get acquainted with the basic concepts of probability theory and mathematical statistics. The student is introduced to the probability model, the basic definitions of Kolmogorov's theory of probability and inductive statistics. They can apply these definitions to practical problems that arise in other areas of professional work and can explain them sufficiently (for example, doctors), they are familiar with the basic methods of inductive statistics and they can choose a suitable method for standard statistical problems.
 Requirements:

The form of verification of study results: the exam is oral – 2 open questions focused on the issues discussed in the lectures.
Student requirements: for credit (credit is a necessary condition for taking the exam):
• obtaining at least 75% of the total number of points in 3 continuous written papers including typical examples from exercises;
• 3 absences per semester are allowed;
• active participation in exercises and homework is required.
 Syllabus of lectures:

1. Motivational lecture. Determinism and randomness.
2. Random variable and its distribution function.
3. Discrete distribution.
4. Continuous distributions.
5. Random vectors, conditioning and independence.
6. Random vectors, numerical characteristics, functions of random variables.
7. The role of mathematical statistics.
8. Parameter estimates. Point estimates of basic characteristics, interval estimates for normal distribution.
9. Methods of construction of point estimates, method of moments, method of maximum likelihood. Introduction to Bayesian Statistics.
10. Tests of hypotheses in the normal distribution (one or two samples).
11. Analysis of variance (single and double sorting).
12. Tests about the type of distribution, normality testing.
13. Nonparametric tests.
14. Repetition.
 Syllabus of tutorials:

1. Classical and geometric probability.
2. Combinatorial tasks.
3. Discrete quantity.
4. Continuous quantity.
5. A quantity with a normal distribution.
6. Conditional and marginal distribution.
7. Bayes theorem.
8. Point estimation of parameters.
9. Interval parameter estimation.
10. Onesample hypothesis test.
11. OneSample Hypothesis Test of the Mean Versus the Estimation Interval.
12. Twosample and paired hypothesis test about the mean.
13. Nonparametric tests. Chisquare hypothesis tests.
14. Repetition.
 Study Objective:
 Study materials:

Probability and statistics EBook [online]. USA, University of California, 2005 [cit. 20190316] Poslední aktualizace [20140309]. Dostupné z: http://wiki.stat.ucla.edu/socr/index.php/EBook
 Note:
 Timetable for winter semester 2024/2025:

06:00–08:0008:00–10:0010:00–12:0012:00–14:0014:00–16:0016:00–18:0018:00–20:0020:00–22:0022:00–24:00
Mon Tue Wed Thu Fri  Timetable for summer semester 2024/2025:
 Timetable is not available yet
 The course is a part of the following study plans:

 Biomedical Laboratory Methods (compulsory course)