Course type: compulsory
Lecturer: Janez Stare, Ph.D., Full Professor

For the timeline see Curriculum.

Prerequisites:

• Valid inscription to the study, no other prerequisites.
• Condition for taking the exam are positively evaluated home assignments.

Content (Syllabus outline):

• Data; examples.
• Questions which statistics can answer
• Descriptive statistics
• Introduction to statistical inference
• Univariate methods
• 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, …)   

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.

Intended leaning outcomes:
Understanding of what statistical analysis is, its advantages and limitations.