Loved the subject, it was fun and quite easy once you understand the lecture content. Lecture slides were self-explanatory and enough, skipped some lectures since I have adhd.

0/5, lecturer is boring, not concise, have to teach myself everything.

hard throughout sem, but final exam was fine (similar to prac exam, and less technical). hated the assignments (very long, tricky & frequent, but only worth 5%). same goes with those weekly quizzes, they used so much time my goodness need to be familiar with r-studio (absolute pain), but barely relevant in final exam only did it cos it was compulsory..but it was ok if you underload and do bludey subjects at the same time. this way i could focus on it and score a high H1

Really not a difficult subject to succeed in if you stay up to date on lectures (Coming from a mathematically challenged comm student). The tutorials might seem overwhelming, but if you get through them, R plays a very minor role in the exam. Lazlo is also a decent lecturer.

Content is quite interesting although using EViews seems quite useless as it is not used in professional environments.

Content is somewhat useful and interesting but the new lecturer is terrible at teaching. EViews is also a terrible choice of software and not relevant so I don’t know why we’re still using it for this subject. Tutorials are a waste of time but mandatory for participation marks.

This is a good subject to teach you about further advanced statistics. However, I have my doubts as to whether or not the approach is the best. E.g EViews is not as widely accepted as excel

May be hard to understand at first (since I forgot some of QM1 lecture topics) but a deep understanding for each concept from lectures and consistent practice is also required. Get yourself familiar with EViews and understand the program. To score well in final exam, do past papers given by the lecturer since the questions are similar to previous papers.

Topics include: review of statistics; tests of the location of populations; simple and multiple regression for use with time series and cross section data, including interpretation of estimates, hypothesis testing and forecasting, an introduction to diagnostics; Logit models; an introduction to time series methods; and seasonality.

This subject is a bit better than QM1!!! Not as tough as QM1