How to create a Box-Behnken Design in Python
Learn to create efficient Box-Behnken designs using pyDOE3: a practical guide for optimizing processes with fewer experiments while exploring quadratic effects and curvature.
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Testing Bayesian Optimization for Lab Experiments
Comparing Bayesian Optimization against random sampling and fractional factorial design using real lab data. Learn how BayBE achieves 98% success rate vs 50% for random approaches in optimizing coating hardness.
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Understanding Bayesian Optimization
Discover how Bayesian Optimization revolutionizes experimental design by using machine learning to intelligently guide experiments, saving time and resources compared to traditional DoE methods.
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Central Composite Design vs. Box-Behnken Design
Learn the key differences between Central Composite and Box-Behnken designs, and discover which response surface design is best for your optimization experiments.
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A full factorial design in Python from beginning to end
A comprehensive step-by-step guide to implementing 2-level full factorial designs using Python. Learn to create experimental plans, visualize main effects and interactions, perform ANOVA, conduct model diagnostics, and generate 3D surface plots - all with practical code examples and real filtration rate data.
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Create a Central Composite Design in Python
Practical, reproducible steps to build a CCD with Python.
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A step by step example of a Central Composite Design (CCD)
Learn how to set up and analyze a Central Composite Design through a practical example. Discover how CCD helps find optimal conditions while detecting curvature in your process.
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How to Perform ANOVA with Python
Learn to perform ANOVA analysis using Python and statsmodels. From basic model fitting to advanced techniques like backward elimination and quadratic terms.
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QQ-Plots Explained
Learn how to use Quantile-Quantile plots to validate ANOVA assumptions and ensure your statistical models are reliable. Discover what QQ-plots reveal about data normality and how to interpret different patterns.
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