I had the privilege of participating in JMP’s Analytically Speaking series a couple of weeks ago (June 8, 2016). While I was able to answer many questions submitted during the live broadcast, there were additional questions that are answered in this blog post. In addition, look for future blog posts
Author
Last week, I described model visualization – which is simply applying data visualization to models – and explained why I find it useful. Designed experiments, especially small DOEs, are a perfect place to practice model visualization. Another term for this could be “analysis by Graph Builder.” I am not suggesting
Model visualization? Data visualization has gained traction in the past few years, with numerous interesting books and talks focusing on improving our data visualization skills. JMP’s own Xan Gregg recently spoke about data visualization on Analytically Speaking). Model visualization is simply applying data visualization to models. When I can “see”
A fellow consultant has advised that, when working on submissions for a regulatory agency, you should “model” your submission on previous ones (that is, leverage previous successful work to your benefit). In that spirit, I fully admit to “modeling” this blog post on those by Jeff Perkinson (10 Things You
One challenge in my statistical consulting projects that involve survey data is how to deal efficiently with open-ended questions. One option is to be involved in the planning of the survey such that you minimize their use. A second option is to have an experienced “coder” on your team and
A typical diagnostic test has two outcomes: positive or negative with the goal of classifying subjects correctly based on a “gold standard” or known outcome. These diagnostic tests have specific performance measures that are used to assess the clinical value of a test. Two basic measures for a diagnostic test