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Controlling Variation: Introduction to the Design of Experiments ‘DOE’

Controlling Variation: Introduction to the Design of Experiments ‘DOE’

Variation is present in every experiment. Learn about DOE techniques to control variation, and to maximise data quality. Commonly used experimental designs are discussed as well as the statistical data analysis tools.

Continue Reading Controlling Variation: Introduction to the Design of Experiments ‘DOE’

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MENUMENU
  • Home
  • Consulting
    • BiostatisticsClassic & Modern Solutions for Life Sciences
    • Machine Learning for EngineersAdvanced Tools for Engineering Applications
    • Predictive Analytics & Market ResearchNovel Data & Machine-Learning Based Solutions
    • Software Solutions & Migration to RTailored Solutions, Training & Coaching
  • Training & Tutoring
    • What Makes Our Training Sessions Different?Our approach based on 25 years teaching applied statistics
    • Corporate Training SessionsOur most popular format, confidential and fully customisable
    • Distance LearningAn efficient way to adapt the pace and keep costs low
    • Individual & Team CoachingThe ideal approach to implement new knowledge into your daily work
  • Training Catalog
    • Research & DevelopmentLearn about DoE to control variation inherent to every experiment, and the analysis of designed experiments. Courses are aimed at scientists, laboratory staff.
    • Multivariate MethodsLearn about multivariate methods and their insightful graphical data summaries. Discover popular and others less known, some classical as well as more recent ones with focus on real-life applications.
    • Market ResearchDiscover statistical tools for making good strategic decisions in the presence of uncertainty. Real-life business & marketing applications, such as segmentation and mapping, are covered.
    • Design of ExperimentsUncover the potential of DoE techniques to control variation inherent to every experiment. Learn about factorial, screening, optimisation designs as well as statistical tools for their analysis.
    • EngineeringDesigned for engineers, learn how to control variation present in every engineering situation. Some sessions deal with the design of experiments (DOE), while others focus on statistical data analysis.
    • ModellingLearn about classic and recent modelling technique to relate different types of variables in an explanatory or predictive perspective and tools for assessing model performance.
    • Life SciencesDesigned for researchers and medical liaisons, this module covers key biostatistical concepts for developing critical reading skills of the medical literature : bias, uncertainty and scope of the results.
    • Data VisualisationDiscover the potential of multivariate & data mining tools for generating insights through data mapping & visualisation.
    • Predictive AnalyticsLearn good practices for successful predictive model-building. Whether you work in R&D, finance or marketing, you will find this module valuable. Real-life applications are used to illustrate the process.
    • R SoftwareHarness the power of R, a free software environment for statistical computing and graphics. Learn how to use R for data visualisation, analysis & modelling. Learn how to master the basics of this open source language.
  • Contact Us