Galit Shmueli, National Tsing Hua University’s Distinguished Professor of Service Science, will be visiting the SAS campus this month for an interview for an Analytically Speaking webcast. Her research interests span a number of interesting topics, most notably her acclaimed research, To Explain or Predict, as well as noteworthy research
Ronald Snee and Roger Hoerl have written a book called Strategies for Formulations Development. It is intended to help scientists and engineers be successful in creating formulations quickly and efficiently. The following tip is from this new book, which focuses on providing the essential information needed to successfully conduct formulation studies in the
When designing an experiment, a common diagnostic is the statistical power of effects. Bradley Jones has written a number of blog posts on this very topic. In essence, what is the probability that we can detect non-negligible effects given a specified model? Of course, there are a set of assumptions/specifications
The Simulate Responses feature throughout various design of experiments (DOE) platforms has always been a useful tool for generating a set of responses according to a specified model. I use it frequently for the simulated responses in Fit Model (or other appropriate platforms), as a way to check that the
In our previous blog post, we wrote about using designed experiments to develop analytic methods. This post continues the discussion of analytic methods and shows how a new type of experimental design, the Definitive Screening Design[1] (DSD), can be used to assess and improve analytic methods. We begin with a
Development of measurement or analytic methods parallels the development of drug products. Understanding of the process monitoring and control requirements drives the performance criteria for analytical methods, including the process critical quality attributes (CQAs) and specification limits. Uncovering the characteristics of a drug substance that require control to ensure safety
As data analysts, we all try to do the right thing. When there is a choice of statistical distributions to be used for a given application, it’s a natural inclination to try to find the “best” one. But beware... Fishing for the best distribution can lead you into a trap.
The design of experiments (DOE) capabilities of JMP are world-class. You can choose from many designs, such as custom, definitive screening, classical, space-filling, choice and covering arrays. But how do you decide which design to use? It used to be a time-consuming process to compare two designs for an experiment,
Clay Barker has been busy extending the usefulness of the Generalized Regression platform in JMP Pro, adding many new models and enhancing ease of use. Generalized Regression (or GenReg for short) debuted in JMP Pro 11 as the place to do a trio of popular penalized regression techniques: Lasso, Elastic
The purpose of screening in designed experiments is “to separate the vital few factors that have a substantial effect on the response from the trivial many that have negligible effects….The definitive screening design can reliably accomplish the task of screening even if there are a couple of second-order effects,” wrote