Subject description
- Data; examples
- Questions which statistics can answer
- Descriptive statistics
- Introduction to statistical inference
- Sampling
- Univariate methods
- Introduction to maximum likelihood estimation
- Introduction to regression
- Introduction to multivariate methods
- Selected topics (design of experiments, longitudinal research, non-parametric methods, event history analysis, …)
The subject is taught in programs
Objectives and competences
The main objective is to familiarize the students with basic statistical subjects, without going into theoretical detail.
A wide spectrum of problems, which can be solved using statistics, will be presented.
The core of the course is introduction to statistical inference, overview of basic univariate and bivariate statistical methods, method of maximum likelihood and basic ideas of regression and some multivariate methods.
Further topics will be chosen based on availability of real examples. R software will be used in lab exercises.
Teaching and learning methods
Lectures, labs, homework.
Expected study results
Knowledge and understanding:
Understanding of what statistical analysis is, its advantages and limitations.
| Part of the pedagogical process will be carried out with the help of ICT technologies and the opportunities they offer. |
Basic sources and literature
- Freedman D, Pisani R, Purves R: Statistics. New York: W.W. Norton & Company, 2007.
- Agresti A, Finlay B. Statistical Methods for the Social Sciences. New Jersey: Prentice Hall, Pearson Education, 2008.
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University of Ljubljana, Faculty of Electrical Engineering Tržaška cesta 25, 1000 Ljubljana