Project assignment has been published. It is available in the SIS, module "Předměty" (Subjects). The access is limited to the students that are registered for this course.

Exam terms for the oral part will be put in the SIS on request. If there is no convenient exam date for you in the SIS contact me and give a date range when you are going to be ready to take the exam. Project evaluation can be done separately from the oral part (before or after) and will not be scheduled in the SIS. Opportunities to take either part of the exam will be offered throughout the summer till September.


Monday   9:00 - 10:30 K3  
Tuesday 14:00 - 15:30 K4  
Exercise Class
Monday 14:00 - 15:30 K4 Instructor: Arnošt Komárek

Course Materials

Supplementary Course Materials

Course Plan

The course covers methods for regression analysis of data that belong to one or more of the following categories

We will learn to understand some of the common statistical methods that allow fitting regression models to such data.

The lecture focuses on the development, theoretical justification, and interpretation of these methods.

The exercise classes will teach how to apply these methods to real problems but may include some theoretical tasks as well. A new assignment will be given about every 2 weeks.

The course will be concluded by a written data analysis project.


This course assumes mid-level knowledge of linear regression theory and applications. Master students of "Probability, statistics and econometrics" must have completed the course on Linear Regression (NMSA407) before enrolling here.

Requirements for Credit/Exam 


The credit for the exercise class will be awarded to the student who hands in a satisfactory solution to each assignment by the prescribed deadline.


The exam has two parts:

  1. Evaluation of project report (has the assignment been completed in all aspects without major errors?)
  2. Oral part focuses on the ability to propose an acceptable model for a particular practical problem and to demonstrate understanding of the theory underlying the chosen model (incl. derivations and proofs).

To pass the exam, both parts need to be passed.