Advanced Statistics

Faculteit Science and Engineering
Jaar 2020/21
Vakcode WMBY018-06
Vaknaam Advanced Statistics
Niveau(s) master
Voertaal Engels
Periode semester II b (24-05-2021 till 26-07-2021)
ECTS 6
Rooster rooster.rug.nl

Uitgebreide vaknaam Advanced Statistics
Leerdoelen At the end of the course:

1. The student can translate specific combinations of experimental design and data into appropriate statistical models.
2. The student can program and analyze statistical models in R.
3. The student can summarize statistical analyses with appropriate tables and graphs.
4. The student can draw justified inferences and conclusions from statistical analyses.
5. The student can describe statistical methods, analyses and conclusions in a format suitable for publication.
Omschrijving Content:
Introduction to R and review of basic statistics. Further topics: general linear models (ANOVA, ANCOVA, multiple regression); generalized least squares; mixed models; generalized linear models; generalized linear mixed models; Bayesian analysis and MCMC; animal models; multivariate analysis. During the last week of the course analysis and presentation of own data set.

Description:
This course teaches advanced statistical analysis almost from the ground up. The only requirement is some familiarity with basic statistical concepts and methods, such as taught in most introductory statistics courses. Some experience with R is useful but not crucial. During
the first three days, basic methods and R will be reviewed to refresh your memory. During the next three weeks, cutting-edge techniques such as GLMMs, power analyses and Bayesian MCMC models will discussed and practiced. Each day will start with a review of the exercises of the previous day, followed by lectures and new computer labs. Mathematics will be kept to
a minimum, and in addition to developing analytical skills, the course also puts much emphasis on producing effective and great-looking graphs (mostly using the ggplot2 package).
The last week of the course will be dedicated to analyzing your own data, unleashing the newly learned techniques. If you have no data yet, alternative suitable data will be found elsewhere or simply created de novo with simulation models. Your methods, results and conclusions will be documented in a report which will be graded.
Uren per week
Onderwijsvorm Hoorcollege (LC), Practisch werk (PRC)
(Lectures in the morning, exercises in the afternoon)
Toetsvorm Verslag (R)
(The final grade is based on the quality of a report containing the students’ individual analysis of their own dataset. Students get written and oral feedback on their reports and a chance to improve before receiving a final grade)
Vaksoort master
Coördinator prof. dr. I.R. Pen
Docent(en) prof. dr. I.R. Pen ,dr. G.S. van Doorn
Verplichte literatuur
Titel Auteur ISBN Prijs
Mixed Effects Models and Extensions in Ecology with R. Springer, 2009 Zuur AF et al.
The R Graphics Cookbook. O’Reilly, 2013 Chang W
The R Book, John Wiley & Sons, 2013 Crawley MJ
Slides, exercises, links to websites and relevant articles are made available on Nestor. No books are strictly required.
Statistical Rethinking. A Bayesian Course with R Examples. Chapman & Hall, 2015 McElreath R
Entreevoorwaarden The course unit assumes some prior statistical knowledge and skills acquired from the firstyear
bachelor course “Inleiding in de biomathematica en biostatistiek” (WLP10B12 ), the
bachelor course “Biostatistiek N2” or similar.
Opmerkingen This course was registered last year with course code WMLS19002
Opgenomen in
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