Objectives and competences
Students:
understand concepts and methods of mathematical statistics and theory of random processes,
acquire statistical approach to mass phenomena analysis, especially phenomena from the field of logistics,
learn to apply statistical methods in analysis of logistics processes and logistics systems.
Content (Syllabus outline)
Introduction. Basis of statistics.
Editing and presenting statistical data.
Relative numbers/statistical measures. Mean values. Measures of data variability. Asymmetry and asymmetry measures. Kurtosis and skewness. Concentration measures. Lorenz curve.
Time series, trend, determining linear trend, least squares method.
Random variables. Distribution law. Distribution function.
Discrete random variables, continuous random variables.
Special discrete random variables.
Special continuous random variables.
Functions of random variables. Random patterns. Statistics.
Arithmetic mean of a sample, variance of a sample, distribution of the arithmetic mean of a sample. Central limit theorem.
Statistical estimating. Estimator.
Estimating the arithmetic mean of a population.
Hypotheses testing. The term of statistical hypothesis. Hypothesis test statistics. Critical area of a test. Hypotheses testing process. Arithmetic media testing.
Regression. Simple normal regression. One-tail and two-tail dependance.
Learning and teaching methods
Lectures: students understand the theoretical frameworks of the course (used methods of explanation, demonstration and conversation). Part of the lecture course is in a classroom while the rest is in the form of e-learning (e-lectures may be given via video-conferencing or with the help of specially designed e-material in a virtual electronic learning environment). At e-lectures students are also faced with independent and problem-based learning, where they solve open problems and perform the review and processing of data from statistical databases (SiStat and Eurostat).
Tutorials: Students enhance their theoretical knowledge and are able to apply it. Part of the tutorial is in a classroom while the rest is in the form of e-tutorials (e-tutorials may be given via video-conferencing or with the help of specially designed e-material in a virtual electronic learning environment). Besides the aforementioned methods, students also use research metod and method of learning by doing.
Intended learning outcomes - knowledge and understanding
Knowledge and understanding:
students are familiarised with basic terminology of statistics,
students are familiarised with mathematical basics for statistical analysis of phenomena in logistics systems,
students learn to apply statistical methods in analysis of concrete logistical problems,
students learn to understand and recognise statistical-mathematical interconnection in logistics systems,
students learn basis of linear modelling.
Intended learning outcomes - transferable/key skills and other attributes
Transferable/key skills and other attributes:
The subject generally relates to all logistics subjects, as is in this one students learn statistical methods and statistical modelling procedures. Students gain the ability to apply theoretical knowledge in practical examples, especially in courses, related to management and planning in logistics.
Readings
Kramberger, T. (2015). Osnove modeliranja u logistici. Ekonomski fakultet.
Kovač Striko, E., Fratrović, T., & Ivanković, B. (2008). Vjerojatnost i statistika: s primjerima iz tehnologije prometa. Fakultet prometnih znanosti, Sveučilište u Zagrebu.
Schmuller, J. (2022). Statistical analysis with Excel for dummies (5th ed.). J. Wiley & Sons.
Frost, J. (2019). Introduction to statistics: an intuitive guide for analyzing data and unlocking discoveries (1st ed.). Statistics by Jim.
Košmelj, K. (2007). Uporabna statistika (2. dopolnjena izd.). Biotehniška fakulteta. https://repozitorij.uni-lj.si/IzpisGradiva.php?id=17699
Additional information on implementation and assessment • e-lectures and e-tutorials 20%.
• Written exam 80%
