1 Day
A one day course in advanced statistics, relevant to all professionals in the analytical sciences, life sciences and related fields.
This course is designed for scientists involved in experimental design, decision making and process optimisation. The course shows how to use analysis of variance, randomisation and manipulation of controllable variables to reduce variability, time and cost of design and development.
● Setting method development objectives
● Full Factorial Designs and Screening Designs
● Optimisation of Factors
● Application of Multivariate techniques for complex datasets
Experimental Design and Optimisation
- Introduction
- Scale of method development
- Randomization and blocking
- Two-way ANOVA
- Latin squares and other designs
- Interactions
- Factorial versus one-at-a-time design
- Factorial design and optimization
- Optimization: basic principles and univariate methods
- Optimization using the alternating variable search method
- The method of steepest ascent
- Simplex optimization
- Simulated annealing
Multivariate Analysis
- Introduction
- Initial analysis
- Principal component analysis
- Cluster analysis
- Discriminant analysis
- K-nearest neighbour method
- Regression methods
- Multiple linear regression (MLR)
- Principal components regression (PCR)
- Partial least squares (PLS) regression
This course is suitable for method developers who wish to use statistical software to improve the development and optimization of methods and to have a greater understanding of the results generated by the techniques.