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Design of Experiments In this category
This section offers a series of sessions dedicated to the various aspects of design of experiments (DOE) and experimental data analysis. Introductory classes deal with the general principles for designing experiments and the use of analysis of variance to analyze data. More advanced sessions present the use of fractional designs and response surface methodology for product/process optimization.
    Session Name+   Summary   Length (days) 
 Introduction to the Design of Experiments (DOE)  Introduction to the Design of Experiments (DOE)  This 2-day hands-on workshop presents classical techniques to design efficient experiments as well as the tools to analyze their results.
The principles of sample size calculations, strategies to remove undesirable sources of variability like the use of blocks and controls, as well as the most commonly used experimental designs are discussed. The statistical analysis of designed experiments is progressively introduced, starting with the t-test method used to compare two groups. Then, the analysis of variance technique (ANOVA) is extensively covered from simple one-factor experiments to more advanced multi-factor situations where the interaction between factors needs to be considered. Multiple comparisons techniques used to locate differences are also presented. 		  2.0 
 Advanced Experimental Designs  Advanced Experimental Designs  This training course covers advanced experimental designs to account for different constraints that experimenters have to deal with, such as: time, resources, material heterogeneity, repeated measures on subjects and randomization constraints. It also deals with how to analyze data and interpret results based on the design selected.
 		  2.0 
 Screening Designs  Screening Designs  This 1-day training reviews how to construct advanced experimental designs to screen for the most influential factors to study in future experiments or studies. These designs require very few runs, thus minimizing the time and cost needed at this preliminary phase.   1.0 
 Optimization Designs and Response Surface Models  Optimization Designs and Response Surface Models  This 2-day course covers the construction of advanced experimental designs specially designed to optimize a process or formulation according to the study objectives and using a minimum number of runs.   2.0 
 Displaying 1 to 4 (of 4 sessions)   Result Pages:  1  
 
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