n.BA.AD.AES.23HS (Applied Environmental Statistics) 
Module: Applied Environmental Statistics
This information was generated on: 12 May 2024
No.
n.BA.AD.AES.23HS
Title
Applied Environmental Statistics
Credits
2

Description

Version: 1.0 start 01 August 2023

 

Study Programme Applied Digital Life Sciences
Regulations Applicable RPO, 29 January 2008, School of Life Sciences and Facility Management Academic Regulations, 15 Dec. 2009, Annex for the Bachelor of Applied Digital Life Sciences degree programme
Module Type  
X Compulsory Module    Elective Module    Optional Module
Planned Semester 4th Semester
Module Coordinator Jürgen Dengler
Telephone / E-Mail +41 (0)58 934 50 84 / juergen.dengler@zhaw.ch
Lecturer(s),
Speaker(s),
Associate(s)		 Jürgen Dengler, Stefan Widmer, Daniel Hepenstrick
Entrance Requirements Statistik und Wahrscheinlichkeit, Statistische Modellierung und Simulation, Versuchsplanung und Auswertung
Learning Outcomes and Competencies Theoretical skills:
The students
  • understand how to sample and organise their data to apply statistical methods on these
  • are able to implement a wide range of statistical methods in the programming language R to real-world datasets to answer questions in ecology and environmental sciences
  • are able to present and interpret statistical outputs appropriately.
Transferable skills:
The students
  • use a wide range of sources to find solutions to statistical problems
Module Content
  • Experimental and sampling designs in ecology, including the question of proper replication
  • Repetition of simple statistical methods and their prerequisits (comparison of two samples, ANOVA)
  • Regression techniques (linear, polynomial, multiple, non-linear)
  • Generalized linear models (GLMs) with different distributions (Poisson regression, logistic regression,…) and generalized additive models (GAMs)
  • Information theoretician approach and multi-model inference
  • Linear mixed-effect models (LMMs) and generalized linear mixed-effect models (GLMMs)
  • Unconstrained and constrained ordination techniques
  • Cluster analyses and other classification approaches, including indicator species analysis
  • Modelling of biodiversity patterns and its drivers
Follow-up Modules Computational modelling in environmental sciences, Spatio-temporal data science
Methods of Instruction  Flipped classroom: The students will receive a detailed reader with theoretical background and information on the implementation in R (and sometimes additional materials) which they read themselves. In the class these topics are then jointly discussed and potentially unclear aspects elaborated further.

Supervised R coding: There will be some presentation of master solutions in R in the class, but subsequently students will work on some smaller exercises themselves (with teachers being available for help).

Homework: Students will receive exercises to deal with as homework.
Digital Resources
  • Reader
  • Practical exercises in R with commented solutions
  • Scientific papers on specific topics
Lesson Structure / Workload  
 Contact Hours 28
 Guided Self-Study 14
 Independent Self-Study 18
 Total Workload 60
Classroom Attendance Attendance is encouraged, but not enforced with attendance list
Assessment
  • Written exam at the end of the semester 40%
  • Coursework 60%: practical exam (students have to analyse a real dataset, prepare the results, interpret them and present everything in science-adequate style)
 
If there is a low number of participants, the lecturer may change the form of a repeat examination after consultation with the head of the study programme: e.g. an oral examination can be used to replace a written one. Please report any changes to the form of examinations by e-mail to pruefungsadmin.lsfm@zhaw.ch and Cc. Head of study programme
Language of Instruction  English
Comments -

 

Course: Applied Environmental Statistics
No.
n.BA.AD.AES.23HS.V
Title
Applied Environmental Statistics

Note

  • No module description is available in the system for the cut-off date of 12 May 2024.