To acquire advanced knowledge and skills in the application of statistical methods for data analysis in various contexts, with a particular focus on industrial systems.

Upon successful completion of the course, students will be able to independently apply and interpret modern statistical models in practice.

Maximum likelihood estimation. The EM algorithm. Generalized linear models: multiple linear, logistic, Poisson, and gamma regression. Analysis of variance. Applications in the analysis of industrial systems. Survival analysis: an overview of basic models. Time series data: basic models and statistical analysis. Fundamentals of Bayesian inference.

The practical classes fully complement the theoretical course content and aim to illustrate the previously described models using real and artificially generated data in R and Python.

There are no prerequisites.

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Total assigned hours: 90

New material: 30
Elaboration and examples (recapitulation): 0

Auditory exercises: 45
Laboratory exercises: 0
Calculation tasks: 0
Seminar paper: 0
Project: 0
Consultations: 0
Discussion/workshop: 0
Research study work: 0

Review and grading of calculation tasks: 0
Review and grading of lab reports: 0
Review and grading of seminar papers: 0
Review and grading of the project: 0
Test: 5
Test: 5
Final exam: 5

Activity during lectures: 10
Test/test: 40
Laboratory practice: 0
Calculation tasks: 20
Seminar paper: 0
Project: 0
Final exam: 30
Requirement for taking the exam (required number of points): 0

Милошевић, Бојана. Основи статистике. Математички факултет, Београд, 2021; Faraway, Julian J. Extending the linear model with R: generalized linear, mixed effects and nonparametric regression models. Chapman and Hall/CRC, 2016.; Faraway, Julian J. Linear models with Python. Chapman and Hall/CRC, 2021.; Kleinbaum, David G., and Mitchel Klein. Survival analysis a self-learning text. Springer, 1996.; Legrand, Catherine. Advanced survival models. Chapman and Hall/CRC, 2021.