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Advanced Statistics
| Faculteit | Science and Engineering |
| Jaar | 2019/20 |
| Vakcode | WMLS19002 |
| Vaknaam | Advanced Statistics |
| Niveau(s) | master |
| Voertaal | Engels |
| Periode | semester II b (25-05-2020 till 27-06-2020) |
| 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. |
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| 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. |
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| Uren per week | |||||||||||||||||||||||||
| Onderwijsvorm |
Hoorcollege (LC), Practisch werk (PRC)
(Lectures in the morning, exercises in the afternoon) |
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| 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) |
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| Vaksoort | master | ||||||||||||||||||||||||
| Coördinator | prof. dr. I.R. Pen | ||||||||||||||||||||||||
| Docent(en) | prof. dr. I.R. Pen ,dr. G.S. van Doorn | ||||||||||||||||||||||||
| Verplichte literatuur |
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| 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. |
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| Opmerkingen | |||||||||||||||||||||||||
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