PNS0233 Multivariate statistics and experimental design, 2.0 Credits
Subjects Mathematic Statistics
No Level Indicated
Pass / Failed
The requirements for attaining different grades are described in the course assessment criteria which are contained in a supplement to the course syllabus. Current information on assessment criteria shall be made available at the start of the course.
Basic knowledge in statistics
After completing the course the student shall be able to
-recognize the importance of the selection of the proper experimental design
-choose the most appropriate multivariate procedures for a given design
-explain how different type of data can be analyzed by statistical methods
-use Principal Component Analysis,
-compute the results of multivariate analysis including Principal Component Regression (PCR) and Partial Least Squares (PLS)
-interpret statistical results in the light of research questions
The course will give an overview of common multivariate statistical methods in food science and biology. Major aspects of study experimental design will also be discussed.
Specifically, the course will cover following topics:
-Multilevel Categoric Design (General Factorial)
-Data import and handling
-Principal Component Analysis
Formats and requirements for examination
Participation in 80% of the lectures and assignment prior to the lectures, which consists of reading part of the course material, data analysis using software provided by the course, and preparation of a short rapport based on the data analysis.
The course is intended for PhD-students. Post-docs can attend the course if positions are available.
Course organiser: Ali A. Moazzami, in collaboration with Sartorius Data Analytics AB
Department of Molecular Sciences