Karen Copeland

Karen Copeland, Ph.D is the owner and sole employee of Boulder Statistics, a statistical consulting company providing services to clients in a variety of industry sectors. She earned her M.S. and Ph.D. in mathematical sciences from Clemson University and her B.A. from St. Olaf College. Karen first used JMP while teaching at Macalester College many years ago and has been using JMP ever since.

More on analysis of means and model building

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

Design of experiment: Analysis by model visualization

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

Why model visualization is integral to model building

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”

10 things you didn’t know about JMP: Tips and tricks

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

New in JMP 11: Using Free Text on survey data

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

JMP add-in for medical diagnostic performance

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