You can look at the code of the problems in the Regression section of the OPL to get some ideas. Here are a few techniques that I am aware of:

- Use the R integration with WeBWorK. This allows you to call any R functions to do statistical calculations. Many of the UBC statistics problems in the OPL take advantage of this.
- There appears to be a function called linear_regression in PGstatisticsmacros.pl. According to the documentation, the usage is:

@xdata = (-1,2,3,4,5,6,7);

@ydata = (6,5,6,7,8,9,11);

($slope,$intercept,$var,$SXX) = linear_regression(~~@xdata,~~@ydata);

Though based on a quick look, this isn't used in any OPL problem, so I don't know how well tested it is. - You can create a for loop within your problem to manually calculate the parameters. This is inelegant but effective.