This Journal was used in Martin Demel's martindemel German-language Mastering JMP webcast on Design Diagnostics, given on 09.10.2015. The Journal is in English, so it will be useful to a wide audience.
The journal includes descriptions to help you learn the techniques presented in the webcast plus linked resources for related webcasts, blogs, books, papers and documentation. The zip file also includes these files:
Relative Std Error of Estimate.jmp
The materials do not address Clinical Trial Designs, designs for non-linear or generalized linear models, pure mixture designs, or Covering Arrays. However there are links to a blog entry on DOE for non-linear models and a link to Mastering JMP videos on Mixture Designs.
The journal and webcast include:
Some General remarks on Fisher's design principles, as well on what effect the probability to detect a significant factor - that is when a change in the level of this parameter will cause a practical important change in the response. This probability is the power and you will learn on what it is based and how it could be increased.
Comparing the ability of designs to estimate model effects and highlight that not all designs can estimate all model effects like two-factor interactions or quadratic effects. Then a comparison on power shows that you should make sure to compare apples with apples - that is to compare designs with same number of runs and same model effects.
The focus is on the JMP Evaluate Design Platform, what can be modeled and how well Power Analysis can be estimated.
Comparison of D-Optimal and Alias-Optimal designs and the importance of taking care of aliasing. This part is based on a blog by Ryan Lekivetz. ryan.lekivetz It also describes the Definitive Screening Designs briefly.
A focus on optimality criterion, color map on correlations and estimation efficiency
The last comparison example is based on a "robust and optimal process experiment" example in the book by Bradley Jones BradleyJones and PeterRyan Lekivetz Goos. It highlights i-optimal designs and their advantage against d-optimal designs regarding reduced prediction variance. The Focus is on estimation efficiency, design efficiency and using the JMP Fraction of Design Space plot.