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
Date 2022-09-19 -- 2022-10-07
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 statistical software.
Purpose of the course The course covers both theoretical and practical regression aspects, with a focus on the application and errors of regression modelling in Neuroscience. 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 statistical languages R and/or Python. 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 should be able to:
- Understand how regression analysis works and decide when regression should/should not be used depending on the research question.
- Understand the implementation of regression in a basic statistical programming language from scratch and with packages.

After the course the student should be able to understand:
- Regression optimization, the warnings and errors produced and their relation to study design and available data (singularity, autocorrelation, small sample sizes, heterogeneous residuals, outliers), and model output.

After the course the student should be able to:
- Search for alternative models that can tackle a scientific question more accurately.
- Understand fixed and random effects.

After the course the student should have a good understanding of:
how/why regression works by using simulated and real datasets from the field of Neuroscience (including brain imaging, neuropsychology, proteomics and other fluid markers).
Contents of the course The course is divided in two parts:
Part one:
1) Statistical hypothesis testing versus statistical modelling (Introduction).
2) Linear regression (main course focus).
Part two:
3) Polynomial linear regression (additional focus).
4) Quantile regression (additional focus).
5) Random and fixed effects (additional focus).
Teaching and learning activities Teaching is in the form of lectures. Student activities will consist of written and oral assignments. The students will receive the time schedule before the course to adjust their working activity accordingly.
Compulsory elements Lectures, student group discussion and presentation. The course leader assesses whether and if so, how absence can be compensated.
Examination Written and oral assignments as well as a written summative 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 (or Zoom lectures if required) in the morning (9:00 - 12:00) and quizzed and tasks in the afternoon (13:00 - 16:00), Monday - Friday on week 1, Wednesday - Friday on week 2 and Monday - Tuesday on week 3. Friday on week 3 there will be a classroom exam from 9:00 to 10:30. The remaining days of the course are free.
Additional course leader
Latest course evaluation Not available
Course responsible Billy Langlet
Institutionen för neurobiologi, vårdvetenskap och samhälle
+46762033996
billy.langlet@ki.se
Contact person -