Stephen Mistler is an analytical training consultant at SAS. He teaches multiple SAS courses, including: Determining Power and Sample Size using SAS/STAT Software, Introduction to Programming with SAS/IML Software, Multilevel Modeling of Hierarchical and Longitudinal Data, and Structural Equation Modeling using SAS. His educational background is in quantitative psychology and he is currently working on his dissertation, which focuses on the use of multiple imputation for multilevel data. Stephen was named a SAS Student Ambassador Honorable Mention for papers that he presented at SAS Global Forum. He also received a SAS Summer Fellowship in Statistical Software Development.
Part 1 of this topic presented a simple Sudoku solver. By treating Sudoku as an exact cover problem, the algorithm efficiently found solutions to simple Sudoku problems using basic logic. Unfortunately, the simple solver fails when presented with more difficult Sudoku problems. The puzzle on the right was obtained from
Sudoku solvers have been written in SAS using a variety of methods (e.g., the DATA step, PROC SQL, and PROC CLP). Surprisingly, SAS/IML appears to have been overlooked for this purpose. On a challenge from a coworker, I wrote this blog post to demonstrate the flexibility of SAS/IML in the
When teaching statistics, it is often useful to produce a normal density plot with shading under the curve. For example, consider a one-sided hypothesis test. An alpha value of .05 would correspond to a Z-score cutoff of 1.645. This means that 95% of a standard normal curve falls below a
