STAT402 Regression for Ph.D. Students

The course is made up of two parts:

1. Block Period: Regular teaching together with STAT200, mandatory activities, and a written exam in January.

2. Spring parallel:

• 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.

Students who follow this course should only register for STAT402 in Studentweb, and not STAT200.

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.