DISCLAIMER: firstly, i am not a professional writer, so if there are mistakes in my language, move on. secondly, the review is mostly based on opinions, so i would advise you to google for more reviews to get a diverse take on the modules you are interested in. thirdly, module details change every semester, so do not expect every assessment will stick. last but not the least, i will not talk about grades. hope this is useful for you, cheers.
what do you learn?
well, of course, from the module title, you know you will be dealing with all the non-parametric statistical methods. these include things such as sign test, Wilcoxon signed rank test, Wilcoxon rank sum test, Kruskal-Wallis test statistic, Spearman's rank correlation, order statistics, goodness of fit test and Kernel density estimator. this module relies heavily on statistical methods and there are quite a number of proofs to be familiar with.
assessments?
students are graded on an assignment (that has similar weightage as typical midterms) and finals.
my personal opinion?
the content is very easy to understand and pick up, you just have to be mindful of careless mistakes for careless mistakes can be very costly in this module. the many proofs present in this module can be annoying but not all are very difficult. the workload and content is very manageable. aim for full marks for the assignment for the mean/median for it is very close to 100%. finals can be difficult at the end but the first few questions are the standard tutorial and assignment questions. practise plays a very very very big role to scoring in this module. the bell curve isn't as smooth as a typical statistical module but not that steep also.
click here to see the modules i have read and their respective reviews. do feel free to contact me via Telegram @alvinngjh for any questions or what not.
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