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
- 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.
- 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
- 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
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