Lecturer(s)


Šedivá Blanka, RNDr. Ph.D.

Kobeda Zdeněk, RNDr.

Tomiczková Světlana, RNDr. Ph.D.

Course content

Lectures in English only. 1. Introduction. Probability experiment, random event, the concept of probability, fundamental laws of probability, conditional probability. 2. Independence of events, Bayes theorem. Definition of random variable. Probability distribution of a random variable. Distribution function. 3. Important discrete distributions (binomial, hypergeometric, Poisson). 4. Important continous distributions (exponential, normal). 5. The central limit theorem. Quantiles. Function of random variable. 6. Random vector. Covariance and correlation. 7. Random sample. Descriptive statistics. Point estimate. Confidency intervals. 8. General procedure for testing hypotheses. Testing a claim about a mean. Tests of variances. 9. Chisquare test of goodness of fit. Contingency tables. 10. Correlation. Tests comparing two parameters. Fdistribution. 11. Regression analysis. Coefficient of determination. Multiple regression. 12. Reliability function, failure rate. Weibull distribution. Sum of random variables. Gamma distribution. 13. Uses and abuses of statistics. Review. Conclusion.

Learning activities and teaching methods

Lecture with practical applications, Collaborative instruction, Selfstudy of literature
 Contact hours
 65 hours per semester
 Preparation for formative assessments (220)
 15 hours per semester
 Preparation for comprehensive test (1040)
 26 hours per semester
 Preparation for an examination (3060)
 50 hours per semester

prerequisite 

professional knowledge 

Students should have a basic knowledge of combinatorics (high school level) and basic knowledge of calculus of one real variable. 
learning outcomes 

On completion of this course the student will be able:  to describe random events and to compute their probabilities  to identify and describe continous or discrete random variable  to recognize basic types of discrete or continous distributions of probability  to use methods of descriptive statistics to summarize data  to enumerate point estimates and construct confidence intervals  to formulate statistical hypothesis and to choose an appropriate statistical test for its accception or rejection  to interpretate statistical results  to select an adequate plan for statistical experiments 
teaching methods 

Collaborative instruction 
Selfstudy of literature 
Lecture with practical applications 
assessment methods 

Combined exam 
Test 
Skills demonstration during practicum 
Recommended literature


Bowerman, Bruce L.; O'Connell, Richard T. Applied statistics : improving business processes. Chicago : Irwin, 1997. ISBN 025619386X.

Brase, Charles Henry; Brase, Corrinne Pellillo. Understandable statistics : concepts and methods. Lexington : D.C. Heath, 1987. ISBN 0669121819.

Grimmett, Geoffrey R.; Stirzaker, David R. Probability and Random processes. Oxford : Oxford University Press, 2001. ISBN 0198572220.

Ross, Sheldon. A first course in probability. PrenticeHall, New York, 2001. ISBN 9780130338518.
