Werner Eugster: Catalogue data in Autumn Semester 2018 |
| Name | Prof. Dr. Werner Eugster |
| Department | Environmental Systems Science |
| Relationship | Adjunct Professor |
| Number | Title | ECTS | Hours | Lecturers | |
|---|---|---|---|---|---|
| 751-0441-00L | Scientific Analysis and Presentation of Data | 2 credits | 2G | W. Eugster | |
| Abstract | Students will get an introduction to the scientific work with data covering all steps from data entry via statistical analyses to producing correct scientific graphical output. Exercises with the data analysis software R/RStudio will provide hands-on opportunities to get acquainted with data analysis and presentation in adequate graphs. Field data gathered with Prof. E. Frossard will be used. | ||||
| Learning objective | This lecture with exercises gives an introduction to the scientific work with data, starting with data acquisition and ending with statistical analyses as they are often required for a bachelor thesis (descriptive statistics, linear regression, simple analyses of variance etc.). Using open-source R/RStudio software will be the primary focus via a hands-on approach. An imporant aspect will be to learn which graphical representation of data are best suited for the task (how can data be presented clearly and still scientifically correct?) | ||||
| Content | Tentative Programme: - Introduction - Introduction to 'R' - Data acquisition, data organization, data storage, working with data - Data import and graphical presentation - Preparation of own data from field course with Prof. E. Frossard / from 4th semester - Correct and problematic graphical data displays - Statistical distribution and confidence intervals - Statistical tests - Repetition and hands-on applications - Linear regressions - Analysis of Variance - Discussion of ANOVA results with Prof. E. Frossard Last week of semester: examination (Leistungskontrolle) | ||||
| Lecture notes | Mainly German (with some English passages from text books) | ||||
| Prerequisites / Notice | Theoretical background in ensemble statistics from the mandatory course in the 4th semester; students should have cleared the examination of that fundamental course to be able to follow | ||||
| 751-3801-00L | Experimental Design and Applied Statistics in Agroecosystem Science | 3 credits | 2G | A. Hund, W. Eugster, C. Grieder, R. Kölliker | |
| Abstract | Different experimental designs will be discussed and various statistical tools will be applied to research questions in agroecosystem sciences. Statistical methods range from simple analysis of variance to mixed-models and multivariate statistics. Surveys and manipulative field and laboratory experiments are addressed and students learn to analyse data using a hands-on approach. | ||||
| Learning objective | Students will know various statistical analyses and their application to science problems in their study area as well as a wide range of experimental design options used in environmental and agricultural sciences. They will practice to use statistical software packages (R), understand pros and cons of various designs and statistics, and be able to statistically evaluate their own results as well as those of published studies. | ||||
| Content | The course program uses a learning-by-doing approach ("hands-on minds-on"). New topics are introduced in the lecture hall, but most of the work is done in the computer lab to allow for the different speeds of progress of the student while working with data and analyzing results. In addition to contact hours exercises must be finalized and handed in for grading. The credit points will be given based on successful assessments of selected exercises. The tentative schedule containst the following topics: Introduction To Experimental Design and Applied Statistics Introduction to 'R' / Revival of 'R' Skills Designs of Field and Growth Chamber Experiments Nonlinear Regression Fits Multivariate Techniques: Principle Component Analysis, Canonical Correpondence Analysis (CCA), Cluster Analysis ANOVA using linear and mixed effect models Error Analysis, Error Propagation and Error Estimation Introduction to autoregression and autocorrelations in temporal and spatial data and how to consider them in ANOVA-type analysis This course does not provide the mathematical background that students are expected to bring along when signing up to this course. Alternatively, students can consider some aspects of this course as a first exposure to solutions in experimental design and applied statistics and then deepen their understanding in follow-up statistical courses. | ||||
| Lecture notes | Handouts will be available (in English) | ||||
| Literature | A selection of suggested additional literature, especially for German speaking students will be presented in the introductory lecture. | ||||
| Prerequisites / Notice | This course is based on the course Mathematik IV: Statistik, passed in the 2nd year and the Bachelor's course "Wissenschaftliche Datenauswertung und Datenpräsentation" (751-0441-00L) | ||||