ECTS credits: 5
Lectures: 2
Exercises: 1

Course objective:

Obtaining knowledge on contemporary mathematical statistics. Obtaining skills in applying statistics in logistics.

Course contents:

INTRODUCTION. Population, random sample, and random occurence. PROBABILITY MODELS. Random events and likelihood. RANDOM VARIABLES. Concept and classification. Discrete variables. Binomial variables. Continuous variables. Normal variables. Two categorical variables – common distribution, marginal and conditional distribution. Two quantitative variables – linear model, correlation and regression. RANDOM SAMPLE. Point estimation. Plug-in model. INFERENTIAL STATISTICS. Interval estimation of expected value. Testing of hypotheses about expected value. Testing of distribution hypotheses. Testing the hypothesis of independence of two variables. Testing the hypothesis on the difference in expectations for two quantitative variables. Linear model – model testing and interval estimation of predictions.


Ability to use statistical concepts and methods.

Learning outcomes:

Having passed the exam, the student will be able to: 1. Identify the population, sample and random occurrence in a given situation (study programme level outcome) 2. Numerically and graphically process data regarding the population and sample using Excel (generic outcome) 3. Identify variables in a given situation and recognize binomial and normal variable. 4. Make an interval estimation on the expected value of a variable. 5. Test a hypothesis on the expected value of a variable. 6. Test a hypothesis on variable distribution. 7. Test a hypothesis on the independence of two variables. 8. Test a hypothesis on the difference in expectation between two quantitative variables. 9. Setup, test and use a linear dependence model for two quantitative variables. 10. Use Excel for statistical analysis (generic outcome). The aforementioned learning outcomes contribute to the learning outcomes of the study programme: - Present the analysis results and proposals for resolving logistics issues. - Analyse relevant management indicators in the logistics system - Apply computer support in resolving logistics issues