The Design of Experiments (DOE) Techniques Training Program aims to equip participants with the skills to design and analyze effective experiments. This training enhances data collection, analysis, and decision-making processes through statistical methods and optimized experimental procedures.
The training is for professional development purposes, and the certificate obtained does not replace the MYK authorization certificate in trainings where the MYK authorization certificate is mandatory.
- Introduction to Experimental Design
Definition and importance of experimental design. The role of DOE techniques in various industries. Basic principles and methods of experimental design. Selection of experimental variables and factors.
- Basic Statistical Concepts and Understanding DOE
Statistical concepts: Mean, variance, confidence intervals. Basic statistical analyses and tests. Statistical significance and p-value. Factorial experiments and interaction analysis.
- Experimental Design Methods
Full factorial experiments and fractional factorial experiments. Plackett-Burman design and Box-Behnken design. Response Surface Methodology (RSM). Self-conducted and pre-designed experiments.
- Data Collection and Analysis in Experimental Design
Proper data collection and organization. Graphical and numerical analysis of experimental data. Use of regression analysis and ANOVA (Analysis of Variance). Modeling and creating response surfaces.
- Interpreting and Improving Experimental Results
Correct interpretation of experimental results. Decision-making with statistical test outcomes. Analysis of interactions and affected variables. Applications of results in business and engineering.
- Applications of Experimental Design
Use of DOE in manufacturing and production processes. Clinical trials and pharmaceutical research in healthcare. Applications of experimental design in agriculture and environmental science. The importance of DOE techniques in product development and optimization.
- Advanced Experimental Design Techniques
Multivariate experimental design and optimization. Mixture designs and modeling. Monte Carlo simulations and randomized experiments. Advanced regression and modeling techniques.
- Ethics and Advanced Topics in Experimental Design
Ethical responsibilities in experimental design. Data security and confidentiality. Reporting and proper presentation of findings. Transparency of research results.
- Experimental Design Software and Tools
Software used for experimental design: Minitab, Design-Expert, JMP, R. Advantages and disadvantages of the software. Modeling experimental processes using software. Visualization and interpretation of experimental data.
- Decision Making and Innovation with Experimental Design
The importance of data-driven decision making. Using experimental results for product and process innovation. Optimization and methods for increasing efficiency. Applications of experimental design for industrial and commercial success.
The training is open to corporate collaborations, and individual applications are not accepted. The training content can be re-planned based on the corporate participant profile and your specific needs. Following mutual discussions, the scope and method of the training (In-person, Online) will be determined, and the related processes will be completed. If an agreement is reached, the suitable dates and times for your institution's participants and our instructors, as well as the location of the training, will be determined.