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
CollegeShould pay more attention to practical stuff rather than theories.
She should NOT be teaching this class. While the hw was fair and midterm was ok, the final project had nothing to do with the class (2 little time to work on it too). Let alone the lectures never made sense. She needs to be better prepared and use a book atleast. A very frustrating class when it should not be.
A great lecturer and very passionate about the subject, go to lecture!! She focuses only on the most important things in lecture and almost everything emphasized in lecture is tested on. Very fair tests just like the practice tests, lots of time for homework and quizzes, very accessible in OH.
The professor is a very very nice person. However, the course is taught superficially and moves very slowly. (6 weeks on glm, 2.5 weeks on ridge/lasso, 1.5 weeks on nonparametric methods) I also need to do a lot of repeated and really boring practice problems to do well on exams.
Clearly passionate about the subject but is an extremely harsh grader and stickler for notation. Expect to spend lots of time in office hours and study groups if you want to pass. Grading 15% quizzes (unlimited attempts), 20% homework, 20% midterm, 45% final
Professor Meiring is a very nice person, but it felt like she was juggling many things at once. It felt difficult to get more than a surface-level understanding of the material and the final was worth 45% which made the class stressful. Luckily, the midterm and final were similar to the practices (which are previous exams) which was helpful.