Clay is a research statistician in the JMP group at SAS. He holds a doctorate in statistics from North Carolina State University.
If you follow pro football (and probably even if you don't), you know that last night the Denver Broncos beat the New England Patriots to earn their spot in Super Bowl 50. But as if a shot at the Super Bowl wasn't big enough, this game meant even more than
The days are getting shorter, and the weather is getting cooler. That means that my favorite time of year is almost here: basketball season. Having spent most of my life on Tobacco Road, I'm not sure that I had any choice but to love playing and watching the game. As
The Golden State Warriors beat the Cleveland Cavaliers to win the NBA championship despite the best efforts of LeBron James. With the Cavaliers depleted by injuries (particularly to Kevin Love and Kyrie Irving), James was faced with carrying his team against a very talented and well-rounded Warriors team. And he
The Generalized Regression platform was introduced in JMP Pro 11 for fitting penalized regression models. Our focus for JMP Pro 12 has been to make model building an easy and natural process using the Generalized Regression platform (we like to call it Genreg for short). This post will focus on the
A penalty can seriously ruin your day. Forget to pay a bill on time, and a late penalty will cost you a few more dollars. When a yellow flag hits the football field, a penalty can cost your favorite team field position, momentum and maybe even some points. But a penalty
JMP is celebrating the International Year of Statistics by honoring an influential statistician each month. This month we take a look at Thomas Bayes, a minister and mathematician whose name is literally attached to statistical inference. Very few details are known about Bayes, but his impact on statistics and science
When building a prediction model, there are a variety of ways that we can model the response as a function of our predictors. The Fit Model platform in JMP allows us to model the response as a linear function of our predictors. The Nonlinear platform allows us to model the
In regression problems, we are often faced with choosing a set of predictor variables from what may be a very large set of candidate predictors. When building our model, we want to find a meaningful set of predictors that yields accurate predictions. Classical methods like forward selection and all-subsets regression