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
We want to help scientists and engineers be successful at developing formulations quickly and efficiently. Success requires good strategies to get the right data in the right amount at the right time. That's why we published the book Strategies for Formulation Development: A Step-by-Step Approach Using JMP. We have worked
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,
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
I once overheard someone call Arizona State University Professor Doug Montgomery their “DOE idol.” I’m sure others echo the sentiment because I’ve seen the warm welcome he receives at our JMP events when he discusses design of experiments (DOE). Most recently, I’ve seen him give keynote talks at Discovery Summit
Dr. Karen Copeland will be our featured guest on Analytically Speaking on June 8. She is the owner of Boulder Statistics, a successful consultancy to a wide array of industry sectors around the world — medical device, diagnostics, chemicals, marketing, environmental, consumer and food products, pharmaceuticals, and web analytics, among