STAT402 Regression for Ph.D. Students

The course builds on STAT200 and expands with an advanced module. The course is conducted in two parts:

• Block Period (STAT200 part, 5 ECTS): Regular teaching, mandatory activities, and a written exam in January.

• Parallel Period (Extended part, 5 ECTS):

• Independent study with analysis and reporting of a practical data example, preferably using own data.

• Supervision service with up to 8 consultation hours.

• Additional syllabus literature and research articles for in-depth study.

• Introductory and mid-term gatherings (full-/half-day seminars).

• Final oral presentation as part of the portfolio assessment.

Knowledge

Upon completing the course, the student will:

• Have advanced knowledge of regression models, including multiple regression and generalized linear models.

• Understand statistical assumptions, diagnostic methods, and model fitting at a high academic level.

• Be familiar with recent statistical methods and current research in regression analysis.

• Have insight into the application of regression models to real-world data from various disciplines.

Skills

Upon completing the course, the student will be able to:

• Apply advanced regression methods to real-world data and correctly interpret the results.

• Evaluate and improve regression models based on diagnostic tests and residual analysis.

• Use statistical software for advanced data analysis.

• Present statistical analyses in writing and orally in a clear and scientific manner.

General Competence

Upon completing the course, the student will:

• Be able to critically assess the application of regression models in scientific studies.

• Be able to communicate statistical results to both specialists and non-specialists.

• Have the ability to work independently with advanced data analysis and reporting.