Learn in this two-day course how to achieve optimal results with minimal effort using statistical design of experiments (DoE). This method helps you identify statistical relationships and create precise models with as few experiments as possible. What you will learn in this course: ✅ Basics of statistical design of experiments (DoE) – Efficient methods for conducting experiments and analysis ✅ Two-stage experiments – Simple but powerful testing methods for informed decision-making ✅ Block factors & screening designs – Identify the most important influencing factors and analyze interactions ✅ Statistical analysis – Assess the confidence of your results with practical methods ✅ Practical application with Design-Expert® – Create and analyze experimental designs step by step With many practical examples and the software Design-Expert®, you will gain deep insights into efficient experimental methods. 🔹 Sign up now and optimize your experimental design! Course: Introduction to Statistical Design of Experiments (DoE) with Design-Expert® Learn in this two-day course how to conduct efficient and well-founded experiments using statistical design of experiments (DoE). The course offers a hands-on introduction to creating, analyzing, and optimizing experimental designs. Course Content: 📌 Two-stage factorial experimental designs – Create and analyze factorial experiments for well-founded insights 📌 Developing factorial designs – Increase the efficiency and significance of your experiments 📌 Transformations in regression models – Optimize models with adjusted data 📌 Block factors in design and analysis – Structure and refine your experimental designs 📌 Fractional factorial designs – Reduce experimental effort without missing relevant insights 📌 Expanding experimental designs – Systematically add additional experiments 📌 Graphical & statistical analysis – Use charts and statistical metrics to optimally interpret results Prerequisites: 🧠 Basic knowledge of statistics is helpful but not required – statistical fundamentals will be taught in the course. 👨🏫 Benefit from expert knowledge and learn how to take your experimental design to the next level with Design-Expert®!
Course: Experimental Design for Mixture Experiments with Design-Expert® In this two-day course, you will learn how to apply statistical experimental design specifically for mixture experiments. Traditional experimental designs are unsuitable for formulation and mixture optimization — instead, mixture designs are needed. This course will provide you with comprehensive knowledge of powerful mixture experimental designs, from creation to graphical analysis and optimization of formulations. You will work with the software Design-Expert® to efficiently create and analyze mixture designs. Why Mixture Experiments? 🔹 Optimizing formulations with multiple components 🔹 Specialized mixture designs for realistic formulations 🔹 Statistical & graphical analysis methods for precise evaluations 🔹 Practical implementation in Design-Expert® with real-world examples Course Content: ✅ Creating & Analyzing Simplex Designs ✅ Selecting suitable mixture designs & models ✅ Generating contour plots in the triangular experimental region ✅ Creating mixture designs with constraints ✅ Optimizing product compositions & formulations ✅ Evaluating design quality & expanding designs ✅ Creating contour diagrams & trace plots for detailed analysis Requirements: 📌 Basic knowledge of experimental design with factorial designs — ideally at the level of the course “Introduction to Experimental Design with Design-Expert®”. 🔹 Sign up now and take your formulation optimization to the next level!
Two-Day Course: Process and Product Optimization with Response Surface Methodology (RSM) In this intensive two-day course, you will learn how to efficiently optimize processes and products using Response Surface Methodology (RSM). This method goes beyond factorial designs and allows for the best possible adjustment of influencing factors to achieve optimal results. Why Response Surface Methodology (RSM)? 🔹 Ideal for complex optimization tasks where factorial designs are insufficient 🔹 Determine optimal factor settings for product and process optimization 🔹 Create models for response surfaces to perform simulations and define process windows 🔹 Use Design-Expert® software to easily and efficiently create RSM designs Course Content: ✅ Expanding factorial designs with center-point runs ✅ Creating RSM experimental designs such as Central Composite Designs (CCD) & Box-Behnken Designs ✅ Selecting appropriate regression models for precise analysis ✅ Determining robust computational conditions to improve process stability ✅ Simultaneously optimizing multiple objectives for comprehensive results ✅ Evaluating design quality to ensure reliable data Requirements: 📌 Basic knowledge of statistics and experimental design with factorial designs is helpful — ideally at the level of the course “Introduction to Experimental Design with Design-Expert®.” 🔹 Sign up now and elevate your optimization skills to the next level!
Course: Quality by Design (QbD) – Statistical Experimental Design for Pharmaceutical Processes Quality by Design (QbD) is the FDA-recommended method for developing and optimizing pharmaceutical products and processes. The central idea is to define factor spaces within which consistent product quality is ensured. Once a design space is approved by regulatory authorities, the process can be adjusted within these limits without requiring re-approval. This methodology provides new flexibility in the production of pharmaceutical products and medical devices, reducing regulatory hurdles. What You Will Learn in This Course: 📌 Fundamentals of Quality by Design (QbD) & Design of Experiments (DoE) 📌 Statistical Tools: Hypothesis Testing & Regression Analysis 📌 Screening Designs: Efficiently Identifying Key Factors 📌 Response Surface Designs: Defining and Optimizing Design Spaces 📌 Validation of Design Spaces for Regulatory Approval 📌 Practical Implementation with Design-Expert® Software The course will follow a complete QbD cycle using FDA examples and statistically model it with Design-Expert® software. Requirements: 📌 Basic knowledge of Design-Expert® is beneficial. 📌 Knowledge of regression analysis and hypothesis testing is helpful but not required. 🕒 Course Duration: 3 Days 🔹 Sign up now and optimize your pharmaceutical development!
Course: Classical Experimental Design with JMP In this two-day course, you will learn the classical methods of statistical experimental design. The goal of experimental design is to efficiently answer experimental questions with a minimal number of trials. You will learn how to use factorial and fractional designs to identify relevant factors and determine the best experimental conditions. Course Content: 📊 Fundamentals of statistical experimental design – Concepts and methods for efficiently designing experiments 📊 Randomization, replication, and blocking – Proper application of these techniques in experiments 📊 Full-factorial and fractional designs – Creation and analysis of screening plans to identify relevant factors 📊 Screening designs – Determining the most important factors influencing the results 📊 Response surface designs – Optimizing and visualizing the effect surfaces for modeling complex relationships 📊 Custom experimental designs – Adapting experimental designs to specific requirements and questions Prerequisites: 📌 Knowledge of using JMP 📌 Basic understanding of statistical hypothesis testing, ANOVA, and regression 📌 The necessary content can be acquired in the courses "Exploratory Data Analysis with JMP" and "ANOVA and Regression with JMP" 🕒 Course Duration: 2 days 🔹 Sign up now and learn how to maximize the success of your experiments with statistical experimental design!