Course catalogue doctoral education - VT24
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| Title | Linear Regression Analysis in Neuroscience: Model Choice, Implementation, Analysis Errors and Interpretation |
|---|---|
| Course number | 5569 |
| Programme | Neurovetenskap |
| Language | English |
| Credits | 3.0 |
| Notes |
The course meets the requirements for a general science course. |
| Date | 2024-01-29 -- 2024-02-19 | Responsible KI department | Institutionen för neurobiologi, vårdvetenskap och samhälle |
| Specific entry requirements | Basic Course in Medical Statistics (for example KI courses in basic statistics for doctoral students) and being familiar with basic R programming. |
| Purpose of the course | The main purpose is for students to understand basic regression analysis principles through a variety of examples with Neuroscience data and help students adapt them to the needs of their research questions using the R language. The course content has been developed to solve regression problems often faced in Neuroscience and all examples will be based on Neuroscience studies. |
| Intended learning outcomes | After the course the student is supposed to:
- Be able to explain how linear regression analysis works and decide when regression should/should not be used depending on the research question. - Be able to implement and assess a linear regression model in a basic statistical programming language from scratch but also with packages (This course is not focused on learning R, basic R programming skills are required). |
| Contents of the course | The course covers both theoretical and practical regression aspects, with a focus on the application and errors of linear regression modelling.
Special focus throughout the course will be given to the: - Regression optimization, warnings and errors produced and their relation to study design, available data (singularity, autocorrelation, small sample sizes, outliers), and model output (heterogeneous residuals etc). - Search for alternative models that can tackle a scientific question more accurately. - Understanding fixed and random effects. The course focuses mainly on the application of linear regression. Thus, the student will learn how/why linear regression works by using simulated and real datasets from the field of Neuroscience (including brain imaging, neuropsychological assessment, fluid markers). The course is divided in two parts: Part one: 1) Statistical hypothesis testing versus statistical modelling (Introduction). 2) Linear regression. 3) Alternative linear regression models (PCR, PLS, lasso/ridge/elastic net, variable selection models). Part two: 4) Polynomial regression. 5) Introduction to random and fixed effects. |
| Teaching and learning activities | Teaching is predominantly in the form of lectures and computer labs. Student activities will include group discussions and presentations, quiz, Q&A sessions, computer labs, and two assignments. The students will receive the time schedule before the course to adjust their working activity accordingly. |
| Compulsory elements | Lectures, student group discussion and labs. The course responsible assesses whether and if so, how absence can be compensated. |
| Examination | Examination of the course's intended learning outcomes is carried out through a written classroom examination. |
| Literature and other teaching material | Recommended course literature:
Faraway, Julian J. Linear models with R. Chapman and Hall/CRC, 2015, ISBN: 978-1-4398-8734-9. |
| Number of students | 10 - 25 |
| Selection of students | Selection will be based on 1) the relevance of the course syllabus for the applicant's doctoral project (according to written motivation), 2) start date of doctoral studies (priority given to earlier start date) |
| More information | The course consists of classroom lectures (10 lectures) in the morning (9:00 - 12:00) and quizzed and tasks in the afternoon (13:00 - 16:00), Monday - Friday on week 1, Thursday - Friday on week 2 and Monday - Wednesday on week 3. Monday (19th of February) there will be a classroom exam from 9:00 to 12:00. The remaining days of the course are free. |
| Additional course leader | |
| Latest course evaluation | Course evaluation report |
| Course responsible |
Konstantinos Poulakis Institutionen för neurobiologi, vårdvetenskap och samhälle konstantinos.poulakis@ki.se Yrkesvägen 34 14143 Huddinge |
| Contact person |
Billy Langlet Institutionen för neurobiologi, vårdvetenskap och samhälle +46762033996 billy.langlet@ki.se Konstantinos Poulakis Institutionen för neurobiologi, vårdvetenskap och samhälle konstantinos.poulakis@ki.se Yrkesvägen 34 14143 Huddinge |
