Basic and advanced concepts of statistical analysis in various problems of Software Engineering (SE). The target is the student
to learn a basic classification of several statistical methods, how to choose the right one, how to run the procedure in a
statistical software and how to interpret the results. Topics: SE data and types of variables. Descriptive statistics and data
visualization. Parametric and non-parametric tests. Correlation analysis. Prediction models. Multivariate methods. All topics
will be demonstrated with examples using real data sets and statistical software.
Structure: Presentation, interactive case studies, exercises with laptops
Required Skills: Bachelor in Computer Science or Mathematics/Statistics. Basic knowledge of probability (especially distributions) and statistics. Basic knowledge of a statistical or mathematical software is helpful.
Required Equipment: Computer for each student with a statistical package. If possible, each student should have an access to SPSS as we could focus better on the statistical concepts.
Maximal number of participants: 20
Professor, Head of the School of Informatics and leader of the STAINS (Statistics and Information Systems) research group. Mathematician with PhD in Statistics. He has published more than 180 papers in journals, conference proceedings and book chapters. His research is focused on the development and application of statistical methods for Information Systems and Software Engineering.