Generalized linear models; log-linear models with application to categorical data; and nonlinear regression models. Discussion of each technique includes graphical methods; estimation and inference; diagnostics; and model selection. Emphasis on application rather than theory. R/SAS computation.
4
UnitsOptional
Grading1, 2, 3
PasstimeNone
Level LimitLetters and science
CollegeHomework is very important for exams. Be prepared you will be fine.
when you see his name, run! a parrot can teach better than him. My college regression was much deeper than the one offerer here for grads.
Prof Wang is such a nice guy! I've never been an office hours student but both times I went to his he was very kind and totally cleared things up. He does have an accent but his English itself is very good, it doesn't get in the way of understanding lectures and I actually think hes very clear in class. Would definitely take him again
Wang is not a bad professor, but is extremely difficult to understand. Tests are not particularly hard and his midterm is A LOT easier than the final. Go to CLAS and you will be fine. Material starts to get tricky at the end of the quarter but is doable. Does not speak english and notes are not that good, but if you go to CLAS then you can get by
No partial credit on tests. Really hard to understand his accent. Some examples are decent others are just confusing and pointless.
super easy midterm (30% of grade). unreasonably hard final (50% of grade). he does not like to give partial credits in tests. he likes to do a lot of example problems on the board, which is very helpful. overall, a pretty hard class.