If we remodel and restructure our applied Statistics courses for our MS program (and perhaps first year PhDs), what kind of changes would you like to see.
Let's focus on Stat 8051 and 8052 now. Here's my criticsm of these courses, as they are now:
- We have too much classical linear regression. Nothing fundamentally wrong with that, except that many (most? all?) students now have a decent idea what regression is, before coming into our program. Most have had some exposure to regression methodology like ordinary least squares, some software, some theory, some experience with data analysis. So, do we need to begin with where we begin the course, and go as slowly as we do?
- Who'se afraid of experimental design? We have, essentially, a full semester-long 4-credit course on very classical experimental design. Do we need all of that? How much of modern statistical research is on design of experiments, and why should it take precedence over every other applied statistical topic? Also, how much of the course is actually applicable in a modern world?
- Too little of new and exciting stuff: A semester of regression and a semester of experimental design leaves little or no room for any other topic. Is this a problem?