751-3801-00L  Experimental Design and Applied Statistics in Agroecosystem Science

SemesterHerbstsemester 2020
DozierendeA. Hund, W. Eugster, C. Grieder, R. Kölliker
Periodizitätjährlich wiederkehrende Veranstaltung
LehrspracheEnglisch



Lehrveranstaltungen

NummerTitelUmfangDozierende
751-3801-00 GExperimental Design and Applied Statistics in Agroecosystem Science
Course will be held in German unless there are students present who ask for English lecturing. Handouts are in English and will be made available on Moodle together with the video recorded during the lecture.
Students should be aware that in addition to 2 hours of presence during the online lecture there are 3-5 hours per week of individual study necessary to fulfill the targets of this course.
The lecturers will communicate the exact lesson times of ONLINE courses.
2 Std.
Do10:00-12:00ON LI NE »
A. Hund, W. Eugster, C. Grieder, R. Kölliker

Katalogdaten

KurzbeschreibungDifferent 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.
LernzielStudents 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.
InhaltThe 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.
SkriptHandouts will be available (in English)
LiteraturA selection of suggested additional literature, especially for German speaking students will be presented in the introductory lecture.
Voraussetzungen / BesonderesThis 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)

Leistungskontrolle

Information zur Leistungskontrolle (gültig bis die Lerneinheit neu gelesen wird)
Leistungskontrolle als Semesterkurs
ECTS Kreditpunkte3 KP
PrüfendeA. Hund, W. Eugster, C. Grieder, R. Kölliker
Formbenotete Semesterleistung
PrüfungsspracheEnglisch
RepetitionRepetition nur nach erneuter Belegung der Lerneinheit möglich.
Zusatzinformation zum PrüfungsmodusStudents must solve six mandatory exercises using the statistical computer language “R” and report the results in English. They must upload the exercises as PDF file to a Moodle repository for grading. Failing to provide an exercise until the given due date yields a mark 1 for the given exercise. The final mark will be calculated as arithmetic average of all marks of the six exercises.

Lernmaterialien

Keine öffentlichen Lernmaterialien verfügbar.
Es werden nur die öffentlichen Lernmaterialien aufgeführt.

Gruppen

Keine Informationen zu Gruppen vorhanden.

Einschränkungen

Keine zusätzlichen Belegungseinschränkungen vorhanden.

Angeboten in

StudiengangBereichTyp
Agrarwissenschaften MasterMethods for Scientific ResearchWInformation
Agrarwissenschaften MasterDesign, Analysis and Communication of ScienceOInformation