Course detail

Statistics and Probability

FSI-CS1Acad. year: 2023/2024

The subject is aimed at introduce of students to descriptive statistics, random events, probability, random variables and vectors, probability distributions, random sample, parameters estimation, tests of hypotheses, and linear regression analysis. The practices include problems and applications in mechanical engineering. A part of exercises will solving by means of statistical software.

Language of instruction


Number of ECTS credits


Mode of study

Not applicable.

Entry knowledge

Rudiments of the differential and integral calculus.

Rules for evaluation and completion of the course

Seminar credit conditions: active attendance in practices, encompassment of complete subject, classification sufficient or better of written exam and admission of semester assignment. Examination (written form): practical part (2 examples from theory of probability: probability and its properties, random variable, distribution Bi, H, Po, N and discrete random vector; 2 examples from mathematical statistics: point and interval estimates of parameters, tests of hypotheses of distribution and parameters, linear regression model) with own summary of formula; theoretical part (4 questions to basic notions, their properties, sense and practical use); evaluation: each example 0 as far as 20 points and every theoretical question 0 as far as 5 points; classification according to of the total sum of points: excellent (90 - 100 points), very good (80 - 89 points), good (70 - 79 points), satisfactory (60 - 69 points), sufficient (50 - 59 points), failed (0 - 49 points).

Attendance at seminars is controlled and the teacher decides on the compensation for absences.


Acquaint of students with basic notions, methods and progresses of probability theory, descriptive statistics and mathematical statistics. Formalization of stochastic way thinking for modeling of real phenomenon and processes in an engineering enclosures.
Students obtain needed knowledge from the probability theory, descriptive statistics and mathematical statistics, which them will enable understand and apply stochastic models of technical phenomenon and suits, based upon these methods.

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Sprinthall, R. C.: Basic Statistical Analysis. Boston : Allyn and Bacon, 1997.
Montgomery, D. C. - Renger, G.: Probability and Statistics. New York : John Wiley & Sons, 2017.
Anděl, J.: Statistické metody. Praha : Matfyzpress, 1993.

Recommended reading

Karpíšek, Z.: Matematika IV. Statistika a pravděpodobnost. Brno : FSI VUT v CERM, 2003.
Cyhelský, L. - Kahounová, J. - Hindls, R.: Elementární statistická analýza. Praha : Management Press, 1996.
Seger, J. - Hindls, R.: Statistické metody v tržním hospodářství. Praha : Victoria Publishing, 1995.


Classification of course in study plans

  • Programme B-ENE-P Bachelor's, 2. year of study, winter semester, compulsory

  • Programme B-STR-P Bachelor's

    specialization AIŘ , 2. year of study, winter semester, compulsory
    specialization SSZ , 2. year of study, winter semester, compulsory
    specialization STG , 2. year of study, winter semester, compulsory
    specialization KSB , 2. year of study, winter semester, compulsory

Type of course unit



26 hours, optionally

Teacher / Lecturer


1. Random events and their probability.
2. Conditioned probability, independent events.
3. Random variable, types, functional characteristics.
4. Numerical characteristics of random variables.
5. Basic discrete distributions Bi, H, Po (properties and use).
6. Basic continuous distributions R, N (properties and use).
7. Two-dimensional discrete random vector, types, functional and numerical characteristics.
8. Random sample, sample characteristics (properties, sample from N).
9. Parameters estimation (point and interval estimates of parameters N and Bi).
10. Testing statistical hypotheses (types, basic notions, test).
11. Testing hypotheses of parameters of N, Bi, and tests of fit.
12. Elements of regression analysis.
13. Linear model, estimations and testing hypotheses.

Computer-assisted exercise

26 hours, compulsory

Teacher / Lecturer


1. Introduction to Statistical Software
2. Descriptive statistics
3. Probability
4. Random variable
5. Random vector
6. Probability distributions (Bi, H, Po, N).
7. Point and interval estimates of parameters N and Bi.
8. Testing hypotheses of parameters N and Bi. Tests of fit.
9. Linear regression (straight line), estimates, tests and plot.