Clay Barker

Clay is a research statistician in the JMP group at SAS. He holds a doctorate in statistics from North Carolina State University.

Analyzing Manning and Brady over the years

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

Ranking basketball teams, using Generalized Regression

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

Did LeBron James step up his game in the playoffs?

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

Coming in JMP Pro 12: Interactive model building

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

Why a penalty is good in generalized regression

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

Celebrating Statisticians: Thomas Bayes

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

Supervised binning add-in for predictive modeling

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

Overwhelmed by too many variables in predictive modeling?

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