I enjoy talking with Phil Rogers , who teaches statistical analysis and managerial decision making, because he enjoys talking about his students. His class at the University of Houston’s C. T. Bauer College of Business is filled with M.B.A. candidates who are already holding down managerial positions in their day jobs.

Phil worked for EXXON for many years, and he took on any number of operations research assignments for which he developed mathematical models. So he likes to invite his students to bring to class the real-world questions facing them in their jobs, and they work together to find the best mathematical approaches to decision evaluation.

Not long ago, Phil told me about some pretty sophisticated analyses of his students have produced of questions that I don’t usually associate with "managerial." Patient deposits for organ transplants. Allocation of turbines at a wind farm. The United States’ bid to become the venue for 2022 World Cup soccer.

He thinks the key to helping people learn modeling techniques is speed: a short learning curve and tools that students can become comfortable with fast. For instance, he likes Monte Carlo software that goes to work quickly for the students, without them having to understand how the software works.

This year he had a great opportunity to test this theory about speed. Sinopec and CNPC, two big Chinese oil companies (big meaning numbers 15 and 25 or some such on the Fortune World 500) each sent a small group of senior managers to Houston to brush up on quantitative analysis. Phil had three days to teach these execs how to build a mathematical model. Three days was all the time they could afford to be away from their home offices.

How did it go? His high-power students, he thought, did quite well. Which is what he expected because, he says, if you can get up to speed quickly in a familiar, universal interface, they way you do in say, Microsoft Excel statistics, "you don’t have to be able to program in Fortran to build a model."