Christoph Grieder: Catalogue data in Autumn Semester 2021 |
| Name | Dr. Christoph Grieder |
| Address | Professur für Kulturpflanzenwiss. ETH Zürich, LFW A 4 Universitätstrasse 2 8092 Zürich SWITZERLAND |
| christoph.grieder@usys.ethz.ch | |
| Department | Environmental Systems Science |
| Relationship | Lecturer |
| Number | Title | ECTS | Hours | Lecturers | |||||||||||||||||
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| 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"). The topics are introduced as short lectures, but most of the work is done on the computer using different packages of R – a software for statistical computing and graphics. 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 contains the following topics: Introduction to experimental design and applied statistics in R Data handling and data exploration with tidyverse Designs of field and growth chamber experiments theory Design creation with DiGGer Fitting linear mixed-effects models with lme4 Marginal means estimation and post-hoc tests with emmeans Nonlinear regression fits Statistical learning techniques Principle component analysis, canonical correpondence analysis (CCA), cluster analysis Random forest 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) | ||||||||||||||||||||
Competencies |
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