Design of Experiments for Optimisation

Design of Experiments for Optimisation: Maximize Results with Data-Driven Strategies

Unlock the power of data-driven decision-making with our course on Design of Experiments (DoE) for optimisation. This course delves into the crucial role of experimentation in science, technology, product development, and process improvement, emphasizing how experimental design directly impacts the validity of your results.

Course Highlights:

• Response Surface Methodology (RSM): Master the fundamental concepts of RSM to effectively maximise or minimise desired response variables.
• Prerequisites: This course builds upon foundational DoE knowledge, including Analysis of Variance (ANOVA) and factorial designs. Review these concepts in courses like "Design and Analysis of Experiments" or other available resources.
• Linear Regression Models: Learn to build and validate linear regression models to fit experimental data and assess model adequacy. We'll cover techniques for handling experiments with inaccurate factor levels and missing observations.
• Experimental Designs for Linear Models: Explore designs utilizing central points to evaluate model linearity (lack-of-fit).
• Factorial Designs & Steepest Ascent: Apply factorial designs to establish a linear model and identify the optimal direction for steepest ascent experiments.
• Central Composite Designs (CCD): Utilize central composite designs to develop quadratic models and pinpoint experimental conditions that maximise your response.
• Multivariate Analysis: Gain proficiency in analysing multiple responses simultaneously through practical, illustrative examples.
• Three-Level Designs: Discover the application of Box-Behnken and face-centred composite designs for advanced optimisation.
• Real-World Applications: Benefit from practical insights derived from research across industry and academia.
• R-Studio for Data Analysis: Learn to analyse data using R-Studio. While not an R tutorial, the course provides downloadable R code and data files, with brief function explanations, allowing for easy adaptation to your own datasets.
• Software Flexibility: If you're already proficient with other DoE software, feel free to use your preferred platform – the results will be consistent.

Who Should Take This Course?

This course is ideal for:

• Researchers in both academic and industrial settings.
• Master's and PhD students seeking to enhance their experimental design skills.
• Engineers looking to optimise processes and product formulations.
• Anyone who conducts experiments and wants to improve the efficiency and effectiveness of their research.

Elevate your experimental design skills and achieve optimal results. Enroll now!