Design of Experiments
Statistical and Analytical Courses
Design of Experiments is specifically designed to meet the analytical needs of those individuals working within a variety of industries. Instruction covers both basic and advanced concepts for the design and analysis of experiments. Presentation of the course material is designed for 16 hours of instruction.

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Biotech, Pharmaceutical & Medical Device Courses

Systematic product development, Quality by Design courses, consulting services and analytical training for biotechnology, pharmaceutical and medical device industries. QbD provides guidance to facilitate design of products and processes that maximize the product’s efficacy and safety profile while enhancing product manufacturability and control.

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Complete curriculum for new product development, manufacturing and business process performance optimization.

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Development tools and templates created by Thomas A. Little Consulting have been used by numerous companies to aid and support various aspects of product development, problem solving, data analysis and risk assessment.

Design of Experiments is required for all scientists, engineers and quality professionals who actively work on all aspects of discovery, product and process development where the goal is to characterize, optimize and improve product and process performance.
Engineering Statistics and Data Analysis is a recommended prerequisite for this course.
Course Objectives
  1. Select factors and responses for experiments
  2. Design experiments appropriate for the information of interest.
  3. Use and apply the structures of orthogonal arrays for product and process development and problem solving.
  4. Ensure the experimental design is efficient.
  5. Use regression techniques in order to analyze the results and make process/product improvements.
  6. Use JMP software to design and analyze experiments.
Detailed Course Outline
Section I: Introduction to DOE
Section II: Experimental Preparation
Section III: Full Factorial Designs
Section IV: Screening Designs
Augment design
Section V: Custom Designs
Generating custom designs
Evaluating custom designs
Analysis of custom designs
Simulation for full distribution modeling
Strategies to minimize experimental size
Adding covariate and uncontrolled factors
Life or repeated measures experiments
Disallowed combinations (nested DOEs)
Split Plot designs
Adding dummy variables
Blocking designs
Mixtures in custom designs
Setting constraints in a DOE
Section VI: Response Surface Designs
Section VII: Special Topics In DOE (optional)
Supersaturated designs
Strip plot designs