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Last update: Zikmundová Markéta Mgr. Ph.D. (03.06.2019)
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Last update: Kubová Petra Ing. (22.01.2018)
Students will: 1. master fundamental statistical and probability concepts 2. have working knowledge of elementary statistical methods 3. be able to solve elementary statistical problems arising in applications |
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Last update: Šnupárková Jana Mgr. Ph.D. (09.05.2019)
R: S.M. Ross: Introduction to Probability and Statistics for Engineers and Scientists (2014, Elsevier) R: J.I. Barragués: Probability and Statistics – A didactic Introduction (2014, Taylor & Francis) R: B. Bowerman, R.T. O'Counel: Applied Statistics (1997, IRWIN Inc Company) |
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Last update: Šnupárková Jana Mgr. Ph.D. (18.09.2020)
https://e-learning.vscht.cz/course/view.php?id=581 |
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Last update: Šnupárková Jana Mgr. Ph.D. (18.09.2020)
The rules for granting the credit are determined by the teachers. As a rule, it is necessary to actively participate in seminars and solve individual tasks, or successfully pass an additional comprehensive test. Attendance at seminars is mandatory.
The obtained credit is a necessary condition for passing the exam. The exam is oral. |
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Last update: Šnupárková Jana Mgr. Ph.D. (09.05.2019)
1. Random events, probability and its properties, independence of random events, conditional probability 2. Random variables, their probability distribution and characteristics 3. Fundamental types of probability distributions (especially normal distribution) 4. Random vectors and their distributions, correlation and independence of random variables 5. Sum of large number of random variables — Central Limit Theorem, Law of Large Numbers 6. Random sample, point estimate of expectation and variance, Maximum Likelihood and Bayesian estimators 7. Confidence intervals — calculation and interpretation 8. Testing of statistical hypotheses — basic principle, type I and II errors, interpretation of results (p-value), basic parametric and nonparametric tests 9. ANOVA 10. Test of independence of quantitative random variables (correlation test) 11. Goodness-of-fit testing, test of independence in contingency tables 12. Fundaments of regression analysis — linear, multiple, nonlinear |
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Last update: Řehák Karel doc. Ing. CSc. (07.03.2023)
Students are expected to have either completed the prerequisite course Mathematics B or possess the equivalent knowledge prior to enrolling in the course.
Basic knowledge of calculus (derivatives and integrals, ideally also for functions of two variables), basic elements of set logic (Venn diagrams) and combinatorics are recommended. |
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Last update: Borská Lucie RNDr. Ph.D. (03.05.2019)
Mathematics A |
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Last update: Šnupárková Jana Mgr. Ph.D. (18.09.2020)
Credit for controlled individual work. Oral exam. |
Teaching methods | ||||
Activity | Credits | Hours | ||
Účast na přednáškách | 0.5 | 14 | ||
Příprava na přednášky, semináře, laboratoře, exkurzi nebo praxi | 1 | 28 | ||
Příprava na zkoušku a její absolvování | 1.5 | 42 | ||
Účast na seminářích | 1 | 28 | ||
4 / 4 | 112 / 112 |