Werner Eugster: Catalogue data in Autumn Semester 2017

Name Prof. Dr. Werner Eugster
DepartmentEnvironmental Systems Science
RelationshipAdjunct Professor

751-0441-00LScientific Analysis and Presentation of Data2 credits2GW. Eugster
AbstractThis lecture gives 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 (via RStudio) will provide hands-on opportunities to get acquainted with data analysis and presentation. Field data gathered with Prof. E. Frossard will be used.
ObjectiveThis 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 etc.). Getting data organized with a spreadsheet program (LibreOffice, Excel) and then transfering them to the open-source R package will be the primary focus. 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?)
ContentTentative Programme:
1. Introduction
2. Data acquisition, data organization, data storage, working with data
3. Graphical presentations I - Spreadsheets
4. Preparation of own data from field course with Prof. E. Frossard / 4. Sem.
5. Correct and problematic graphical data displays
6. Introduction to 'R'
7. Data import and graphical presentation
8. Statistical distribution and confidence intervals
9. Statistical tests - Repetition and hands-on applications
10. Linear regressions
11./12. Analysis of Variance
13. ANOVA - Discussion of results with Prof. E. Frossard

Last week of semester: examination (Leistungskontrolle)
Lecture notesMainly German (with some English passages from text books)
Prerequisites / NoticeTheoretical 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
751-3801-00LExperimental Design and Applied Statistics in Agroecosystem Science3 credits2GA. Hund, W. Eugster, C. Grieder, R. Kölliker
AbstractIn this course, different experimental designs will be discussed and various statistical tools will be applied to research questions in agroecosystem sciences. Both manipulative (field and laboratory) experiments and surveys are addressed and students work with a selection of basic techniques and methods to analyse data using a hands-on approach. Methods range from simple t-tests to multi-factoria
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)