M.INC.1002: Data analysis for field biologists

M.INC.1006: Data analysis for field biologists (6 C / 4 SWS)

Learning outcome:
In this module, we provide a basic introduction to data analysis in the R programming environment. We cover data collection and organisation, sampling designs in observational studies and basic statistics. We visualize our data throughout. The course participants will learn how to use classical hypothesis testing, linear regression and generalized linear models. If progress allows, we will introduce more advanced methods such as mixed effect models, models that can be used to correct for varying detection probability during data collection and approaches to extract, analyse and visualize spatial data.
Core skills acquired: Ability to organize, transform and process data in R, ability to critically judge sources of bias resulting from data collection and analysis, ability to choose appropriate tools for the analysis of different types of data (e.g., categorical vs. continuous variables), skills to graphically present key messages, ability to report statistical results.
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Courses and examinations
1. Statistics for Field Biologists (Lecture) (Lecture, 2 SWS)

2.Statistics for Field Biologists (Exercise, 2 SWS)

Exam: Daily assignments submitted as R Markdown files at the end of the course

Prerequisite for examination:
Ability to organize, transform and process data in R, ability to critically judge sources of bias resulting from data collection and analysis, ability to choose appropriate tools for the analysis of different types of data (e.g., categorical vs. continuous variables), skills to graphically present key messages, ability to report statistical results.


Workload:
180 h (56/124 h, Präsenzzeit/Selbststudium)

Admission requirements:
None

Recommended previous knowledge:
None

Language:
English

Person responsible for module:
Prof. Dr. Johannes Kamp

Course frequency:
Academic Term each winter semester

Duration:
One semester

Number of repeat examinations permitted:
Twice

Recommended Semester:
First Semester

Maximum number of students:
25