Christoph Grieder: Catalogue data in Autumn Semester 2020

Name Dr. Christoph Grieder
Address
Professur für Kulturpflanzenwiss.
ETH Zürich, LFW A 4
Universitätstrasse 2
8092 Zürich
SWITZERLAND
E-mailchristoph.grieder@usys.ethz.ch
DepartmentEnvironmental Systems Science
RelationshipLecturer

NumberTitleECTSHoursLecturers
751-3801-00LExperimental Design and Applied Statistics in Agroecosystem Science3 credits2GA. Hund, W. Eugster, C. Grieder, R. Kölliker
AbstractDifferent 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.
ObjectiveStudents 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.
ContentThe 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 notesHandouts will be available (in English)
LiteratureA selection of suggested additional literature, especially for German speaking students will be presented in the introductory lecture.
Prerequisites / NoticeThis 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)