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JMP - Design of Experiments

Ort : Inhouse
The complete learningcycle starting with tools to create the list of influencing factors, followed by the typical screening desings and finally optimization RSM designs like Box Behnken or Central composite is introduced alongside practical examples.
Description

Product information "JMP - Design of Experiments"

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:
๐Ÿ“Š Fundamentals of statistical experimental design โ€“ Concepts and methods for efficiently designing experiments
๐Ÿ“Š Randomization, replication, and blocking โ€“ Proper application of these techniques in experiments
๐Ÿ“Š Full-factorial and fractional designs โ€“ Creation and analysis of screening plans to identify relevant factors
๐Ÿ“Š Screening designs โ€“ Determining the most important factors influencing the results
๐Ÿ“Š Response surface designs โ€“ Optimizing and visualizing the effect surfaces for modeling complex relationships
๐Ÿ“Š Custom experimental designs โ€“ Adapting experimental designs to specific requirements and questions

Prerequisites:
๐Ÿ“Œ Knowledge of using JMP
๐Ÿ“Œ Basic understanding of statistical hypothesis testing, ANOVA, and regression
๐Ÿ“Œ The necessary content can be acquired in the courses "Exploratory Data Analysis with JMP" and "ANOVA and Regression with JMP"

๐Ÿ•’ Course Duration: 2 days

๐Ÿ”น Sign up now and learn how to maximize the success of your experiments with statistical experimental design!


Details

Die Grundidee der statistischen Versuchsplanung umsetzen kรถnnen Randomisierung, Wiederholung und Blockbildung richtig einsetzen Multiple faktorielle Versuchsplรคne und teilfaktorielle Screening Plรคne erstellen und analysieren Screening-Designs zur Bestimmung der wenigen wichtigen Faktoren aufstellen Response Surface - Plรคne zur Optimierung und Darstellung der Wirkungsflรคche verwenden Benutzerdefinierte Versuchsplรคne richtig einsetzen

Teilnehmer sollten vertraut mit der Bedienung von JMP sein, sowie ein grundlegenden Verstรคndnis von statistischen Hypothesentests, ANOVA und Regression haben. Die notwendigen Inhalte kรถnnen Sie in den Kursenย Explorative Datenanalyse mit JMPย sowieย ANOVA und Regression mit JMPย erlernen.

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DoE

Design Expert - Introduction
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:ย โœ… Basics of statistical design of experiments (DoE) โ€“ Efficient methods for conducting experiments and analysis โœ… Two-stage experiments โ€“ Simple but powerful testing methods for informed decision-making โœ… Block factors & screening designs โ€“ Identify the most important influencing factors and analyze interactions โœ… Statistical analysis โ€“ Assess the confidence of your results with practical methods โœ… Practical application with Design-Expertยฎ โ€“ Create and analyze experimental designs step by step With many practical examples and the software Design-Expertยฎ, you will gain deep insights into efficient experimental methods. ๐Ÿ”น Sign up now and optimize your experimental design! Course: Introduction to Statistical Design of Experiments (DoE) with Design-Expertยฎ 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:ย ๐Ÿ“Œ Two-stage factorial experimental designs โ€“ Create and analyze factorial experiments for well-founded insights ๐Ÿ“Œ Developing factorial designs โ€“ Increase the efficiency and significance of your experiments ๐Ÿ“Œ Transformations in regression models โ€“ Optimize models with adjusted data ๐Ÿ“Œ Block factors in design and analysis โ€“ Structure and refine your experimental designs ๐Ÿ“Œ Fractional factorial designs โ€“ Reduce experimental effort without missing relevant insights ๐Ÿ“Œ Expanding experimental designs โ€“ Systematically add additional experiments ๐Ÿ“Œ Graphical & statistical analysis โ€“ Use charts and statistical metrics to optimally interpret results Prerequisites:ย ๐Ÿง  Basic knowledge of statistics is helpful but not required โ€“ statistical fundamentals will be taught in the course. ๐Ÿ‘จโ€๐Ÿซ Benefit from expert knowledge and learn how to take your experimental design to the next level with Design-Expertยฎ!

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Design-Expert
Design-Expertยฎ โ€“ The User-Friendly Software for Design of Experiments (DOE) Design-Expert is a powerful design of experiments (DOE) software that combines an easy-to-use, intuitive interface with the latest techniques in multifactorial data analysis. The software guides you through all classic DOE phases: screening, optimization (RSM), and validation. With Design-Expert, you save time and costs in product development while improving process conditions. Why Choose Design-Expert? โœ… Interactive 3D visualizations & contour plots โ€“ Quickly and easily identify optimization potential โœ… Versatile experimental designs โ€“ From classic designs to split-plot and mixture designs โœ… Multivariate optimization โ€“ Optimize multiple response variables simultaneously โœ… Excel export โ€“ Objective functions can be exported directly as formulas โœ… Propagation of error analysis โ€“ Find robust settings for your processes Features of Design-Expert ๐Ÿ“Š Rotatable 3D surface plots โ€“ Perfect for visualizing response surfaces ๐Ÿ“Š Interactive contour & ternary plots โ€“ For precise process and formulation optimization ๐Ÿ“Š All classic design types โ€“ Including D-optimal screening designs and I-optimal designs for RSM ๐Ÿ“Š Definitive screening & split-plot designs โ€“ Ideal for hard-to-change factors ๐Ÿ“Š Mixture & combined designs โ€“ Perfect for chemistry, pharmaceuticals, and formulation development ๐Ÿ“Š Optimization platform with numerical optimization โ€“ Automatically calculates the best factor settings Perfect for: โœ”๏ธ Research & Development โœ”๏ธ Chemical and pharmaceutical applications โœ”๏ธ Process optimization & quality control โœ”๏ธ Formulation & recipe development Download Design-Expertยฎ โ€“ Free Demo! Try Design-Expert, the powerful DOE software, for free! The trial version is available for download on the Stat-Ease website. ๐Ÿ”น Test all features โ€“ Experience the full power of screening, optimization (RSM), and validation ๐Ÿ”น Intuitive user interface โ€“ Simple operation for fast results ๐Ÿ”น Optimize formulations & processes โ€“ Ideal for research, development, and quality assurance ๐Ÿ“ฅ Download your free demo now: โžกย Design-Expert Trial Version

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Design Expert - Quality by Design
Course: Quality by Design (QbD) โ€“ 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: ๐Ÿ“Œ Fundamentals of Quality by Design (QbD) & Design of Experiments (DoE) ๐Ÿ“Œ Statistical Tools: Hypothesis Testing & Regression Analysis ๐Ÿ“Œ Screening Designs: Efficiently Identifying Key Factors ๐Ÿ“Œ Response Surface Designs: Defining and Optimizing Design Spaces ๐Ÿ“Œ Validation of Design Spaces for Regulatory Approval ๐Ÿ“Œ Practical Implementation with Design-Expertยฎ Software The course will follow a complete QbD cycle using FDA examples and statistically model it with Design-Expertยฎ software. Requirements: ๐Ÿ“Œ Basic knowledge of Design-Expertยฎ is beneficial. ๐Ÿ“Œ Knowledge of regression analysis and hypothesis testing is helpful but not required. ๐Ÿ•’ Course Duration: 3 Days ๐Ÿ”น Sign up now and optimize your pharmaceutical development!

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