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Introduction to Photostability
A Practical Introduction to
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Experimental Design for Productivity and Quality in Research & Development
| Type | Course |
|---|---|
| Language | English |
| Date |
August 18, 2009
to August 20, 2009 |
| Venue |
Four Points By Sheraton 1201 K Street NW Washington, DC 20005 US |
| Chemistry Specialties |
|
| Chemistry Techniques |
|
| Contact |
American Chemical Society 1155 Sixteenth Street, NW Washington, DC 20036 US (800) 227-5558 shortcourses@acs.org |
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Learn how to significantly improve your R&D quality and efficiency and how to match appropriate experimental designs to real-world problems with two expert instructors. Basic concepts of experimental design will be covered, as well as strengths and limitations of popular experimental design techniques; the applicability of common designs; and determining which experimental designs are appropriate or inappropriate for particular situations. The course assumes no previous knowledge of statistics and is aimed at both beginning and experienced R&D scientists.
Key Topics
* Basic concepts of experimental design
* Strengths and limitations of popular experimental design techniques
* Applicability of common designs
* Determining which experimental designs are appropriate or inappropriate for particular situations
Session titles
* Linear Models
* The importance of n, p, and f
* Regression Analysis
* Residuals
* Degrees of Freedom
* Basic Design Concepts
* Looking for Pure Error
* Calibration
* Coding
* Factorial-Type Designs
* Yates Algorithm
* Screening Designs
* Plackett-Burman Designs
* Hadamard Designs
* Taguchi Designs
* Response Surface Designs
* Central Composite Designs
* Box-Behnken Designs
* Mixture Designs
* Comparing Different Designs
* Choice of Model
* Matrix Least Squares Solution
* Replication and Pure Error.
* Sums of Squares
* The Rosetta Stone of Statistics
* Looking for Lack of Fit
* Orthogonal Designs
* Classical Data Analysis
* Fractional Factorial Designs
* Blocking
* Multiple Response
* Scheffe Mixture Model
* Inercept Mixture Model
* Analysis of Variance (ANOVA)
* Correlation Coefficient
* F-Test for Regression and Lack of Fit
* Confidence Intervals and Bands