SubjectsSubjects(version: 953)
Course, academic year 2021/2022
Applied Statistics - S413004
Title: Applied Statistics
Guaranteed by: Department of Mathematics, Informatics and Cybernetics (446)
Faculty: Faculty of Chemical Engineering
Actual: from 2021 to 2021
Semester: summer
Points: summer s.:4
E-Credits: summer s.:4
Examination process: summer s.:
Hours per week, examination: summer s.:1/2, C+Ex [HT]
Capacity: unknown / unknown (unknown)
Min. number of students: unlimited
State of the course: taught
Language: English
Teaching methods: full-time
Teaching methods: full-time
Is provided by: AB413003
Guarantor: Zikmundová Markéta Mgr. Ph.D.
Kříž Pavel Ing. Mgr. Ph.D.
Is interchangeable with: AB413003
The Elementary Course of Statistics is aimed at bachelor degrees students. Trainees will be able to solve elementary statistical methods and its connection with some probability concepts in necessary range providing to take up them in advanced statistical parts of other subjects.
Last update: TAJ413 (17.12.2013)
Aim of the course

Students are supposed to know:

Soft Competence:

1. To master fundamental statistical and probability concepts

2. Knowledge and acceptance of elementary statistical methods

Specific Competence:

3. To solve elementary statistical tasks with self-reliance

Last update: TAJ413 (17.12.2013)

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)

Last update: Kříž Pavel (11.10.2019)
Learning resources

Last update: PAVLIKJ (16.07.2013)

1. Probability of random events, independence of random events.

2. Conditional probability, law of total probability, Bayes's theorem.

3. Random variable, distribution function, probability function, density.

4. Mean, variance, quantiles, median, critical values, independence and correlation of random variables.

5. Fundamental types of discrete and continuous distributions.

6. Random sample, sample statistics.

7. Point estimates, confidence intervals.

8. Testing of statistical hypotheses, type I and II errors. One-sample tests about mean and variance.

9. Two-sample tests about means and variances.

10. Independence testing.

11. Goodness-of-fit testing.

12. Contingency tables.

13. Fundamentals of regression analysis.

14. Summary, alternatively more specific statistical methods.

Last update: Pavlíková Pavla (13.04.2016)
Registration requirements

Mathematics I

Last update: PAVLIKJ (16.07.2013)