Statistical inference:

• parametric and non-parametric models
• point and interval estimation
• hypothesis testing

Estimation theory:

• plug-in principle
• method of moments
• maximum likelihood

Statistical modelling:

• linear and logistic regression
• MLE fitting and inference
• diagnostics
• model selection
• practical aspects

After completing the course, the students should be familiar with, on one hand, with various inferential procedures, related asymptotic considerations, and mathematical description of statistical models, and on the other hand with practical considerations such as model building, interpretation, and diagnostics. The main goal is to appreciate how mathematical theory shapes statistical modelling.

The course takes place twice a week in the second half of the semester, following the Probability class in the Specialization in Business Mathematics. Active participation is strongly recommended, and it is also necessary to take part in the in-class quizzes (25 % of the grade).

All classes are mixtures of lectures and practicals. The course starts as teacher-centered, and slowly progresses towards a student-centered class. The students are required to familiarize themselves with the material in advance of every respective class (using the lecture notes). In every unit, the material will be discussed first, then short quizzes will be used to verify the student's familiarity with the current topic, and subsequently students may present their solutions to the relevant exercises, and acquire bonus points. In the second half of the course, the students may choose to work on individual projects.

There are three modes of evaluation:

• 25 % in-class quizzes (single choice A/B/C)
• 35 % midterm exam
• 40 % final exam

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