PROEFOPZET EN DATAVERWERKING
 
Taught in Schakelprogramma tot Master in de biowetenschappen, landbouwkunde
Schakelprogramma tot Master in de biowetenschappen, tuinbouwkunde
Linking course : Master in Biosciences,Agricultural science - Food Industry for Prof.Bach. in agro- en biotechnologie (alle afst.)
Linking course : Master in Biosciences,Agricultural science - Food Industry for Prof.Bach. in biomed. Labtechnologie
Linking course : Master in Biosciences,Agricultural science - Food Industry for Prof.Bach. in chemistry
Linking course : Master in Biosciences,Agricultural science - Food Industry for Prof.Bach. in voeding- en dieetkunde
Theory [A] 24.0
Exercises [B] 0.0
Training and projects [C] 24.0
Studytime [D] 120.0
Studypoints [E] 4
Level in-depth
Credit contract? Unrestricted access
Examination contract? Access denied
Language of instruction Dutch
Lecturer Tanja Van Hecke
Reference BZBLBK01A00004
 
Key words
Experimental design, statistics, data analysis, general linear model, t-test, ANOVA, regression, non-parametric test

Objectives
  • The students should learn how to set up an experimental design within the boundaries of the available resources such that the generated information allows to obtain statistically well-founded conclusions.
  • The student should be able to analyse data from scientific experiments or literature by means of the most appropriate method and to interpret the subsequent results.
  • The students should be able to analyse data by means of a standard statistical software package like SAS Learning Edition.


Topics
  • Experimental design:
                - sampling
                - observational techniques
                - randomisation
                - sample size determination
                - factorial designs
                - standard experimental designs for field trials like Randomised Complete Block Designs,
                 Latin- and Greek Latin squares, lattices.

  • Data analysis:
                - outlier detection
                - visualisation of data and results
                - chi-square tests
                - t-tests
                - analysis of variance, 1 and 2 factor ANOVA
                - linear regression: simple, multiple and model building, R2, AIC, multicollinearity
                - multivariate techniques like factor analysis (e.g. PCA)
                - time series
                - practical analysis by means of SAS Learning Edition

Prerequisites
The course starts from the content of an introductory course on probability theory and statistics

Final Objectives
  • Students have a sufficient understanding of experimental design and related statistical methods to be able to design and implement a specific experiment to resolve the scientific problem at hand.
  • Students can analyse the generated data with or without aid of a statistical software package.
  • Students are able to extract well-founded conclusions from the results of a scientific experiment and subsequent data analysis.
  • Students are able to interpret statistical results published in scientific literature.


Materials used
::Click here for additional information::
Statistical software package: SAS Learning Edition
Course notes
SAS Learning Edition manual

Study costs
Cost: 5.0 EUR


Study guidance
  • During the exercise sessions the students are coached by an assistant.
  • Students can use a specifically designed electronic learning environment which presents the theoretical background and applications to realistic example cases of most common statistical techniques.
  • After each exercise session students are asked to solve some additional problems by means of this electronic learning environment. The student specific scores are saved in a database such that problems can be detected in an early stage.


Teaching Methods
  • Theory: oral lectures
  • Exercises: hands-on practical PC sessions and additional exercises through electronic learning environment


Assessment
  • Theory: period aligned evaluation
  • Exercises: period aligned and permanent evaluation through electronic learning environment


Lecturer(s)
Theory: T. Van Hecke
Exercises: S. Maenhout