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 Faculteit Science and Engineering Jaar 2022/23 Vakcode WMCC005-05 Vaknaam Advanced Statistical Modelling Niveau(s) master Voertaal Engels Periode semester I b ECTS 5 Rooster rooster.rug.nl

Leerdoelen At the end of this course, the student is able to:
- summarize the relation between one or more predictors and a measurement from a given dataset using descriptive statistics and/or visualizations in R.
- choose the most appropriate method for analysing a given data set or research question from the statistical techniques covered in the course, and explain why.
- apply the regression analyses that are discussed in the course to a given dataset using R.
- interpret the outcome of a statistical analysis and present the results using visualisations in R and/or words.
- critically evaluate and discuss a statistical model in terms of the method's assumptions and the model's fit to the data.
- reflect on the validity and reliability of the conclusions that can be drawn from the outcome of a statistical analysis.
Omschrijving The aim of this course is that students are able to analyze experimental data, and to draw conclusions about the relation between the recorded measures (dependent variable) and the factors of interest (e.g., predictors capturing the experimental design). We will focus on regression techniques that are commonly used in the field of cognitive science, such as mixed-effects modeling and generalized additive modeling. The course consists of lectures in which the central ideas behind the statistical analysis and regression techniques will be explained, and lab sessions in which students can practice the analysis process (i.e., data visualization, analysis, model criticism, and interpretation) on various real data sets. These lab sessions will make use of the free software environment R for statistical computing.
Uren per week 4
Onderwijsvorm Bijeenkomst (S), Hoorcollege (LC), Opdracht (ASM)
Toetsvorm Opdracht (AST)
(First assignment (a and b part) is PASS/FAIL. If not sufficient, final assignment will not be graded (possibility to hand in one more time after feedback when failed). Assignments 2, 3, and 4: 30% of grade (each 10%). When assignment is skipped, it will be graded as 0. Final assignment counts for 70% of the grade. Students will need at least a 5.5 on the combination of these parts to pass the course. Resit: only possible for final assignment; other assignment grades keep the same.)
Vaksoort master
Coördinator dr. J.C. van Rij-Tange
Docent(en) dr. J.C. van Rij-Tange
Entreevoorwaarden Mandatory: No prior knowledge is assumed. Please note that the student is expected to have a relevant BSc degree.
Advised: Participants in the course should have completed a basic course in statistics (e.g. Statistics), covering topics such as descriptive statistics, hypothesis testing (e.g., t-test, linear regression), and the basics of the R programming language.
Opmerkingen
Opgenomen in
Opleiding Jaar Periode Type
MSc Computational Cognitive Science  (A - General Mandatory Course Units) 1 semester I b verplicht