|Teaching method||Contact hours|
|Course coordinator(s)||ir. SLGE Burgers|
|Lecturer(s)||dr. AJ Boevé|
|dr. JA Hageman|
|dr. PPJ van der Tol|
|ir. SLGE Burgers|
|Examiner(s)||ir. SLGE Burgers|
Language of instruction:
Assumed knowledge on:
MAT-15303 Statistics 1
MAT-15403 Statistics 2
FTE-25806 Research Methods Biosystems Engineering.
The following topics will be addressed in the course:
- linear regression and multiple linear regression: model formulation, meaning of model parameters, checking model assumptions and prediction;
- data transformation;
- experimental design: completely randomized design, block design and factorial design. Calculating the required sample size to obtain a certain precision;
- analysis of variance and pair-wise testing;
- non-parametric tests: Wilcoxon (Mann-Whitney), Spearman rank correlation, Kruskal-Wallis;
- proportion: one population, test for difference between two proportions, binomial distribution;
- contingency tables and the chi-squared tests for goodness of fit, for independence and for homogeneity;
- multiple linear regression: comparing models;
- experimental design: factorial design in blocks;
- selection of variables (quantitative and/or qualitative) to find the optimal linear regression model (checking assumptions);
- repeated measurements;
- calibration, validation and cross-validation.
These methods are relevant for further data analysis in the biosystems engineering domain. The theory of the course will be supported by practicals in which relevant data sets from the biosystems engineering domain will be analysed.
After successful completion of this course students are expected to be able to:
- translate a research question into a statistical hypothesis;
- design the appropriate experiment given the hypothesis;
- choose an appropriate model and check the underlying assumptions;
- analyse the data with an appropriate programm;
- interpret the results of the analysis and draw conclusions with respect to the stated problem.
- lectures: follow classes;
- study the book and make exercises;
- computer practical's (compulsory): (learn) how to use R with R-Studio (and PQRS) and work on case studies related to Biosystems Engineering;
- tutorial (pen and paper practical): work on exercises and case studies where the computer output is given.
The examination consist of two parts:
- written exam with open questions and multiple choice questions,(contribution to final mark: 100%);
- computer practicals (attendance compulsory) have to result in a pass.
Statistical Methods and Data Analysis by R. Lyman Ott and Michael Longnecker. 7th ed., 1174 p. ( ISBN-13:978-1-305-26947-7).
Lecture notes (available at the WUR-shop).
|Compulsory for:||BAT||Biosystems Engineering||BSc||1MO|