First of all i’d like to apologize if this has become a redundant topic but honestly, i didnt browse through the list of threads properly in the interest of time.
I just wanted to know how are you guys scoring on the practice problems available on cfai website? i have solved a handful of them and they looking pretty difficult … i’ve surprising scored 75%-80% on those question (but the sample size is too small) but i really had no clue to many of the questions.
If you have gone through all of the practice questions and have maintained correct ratio of above 75%, then i am pretty sure that you are nailed on passing and i’m kinda jealous of you.
I’d like to get more practice under my belt, since there are many topics they could give me vignettes on that would give me difficulty. Honestly, the conceptual questions give me a big boost, and quant is a huge asset for me. I couldn’t understand the presentation of some mock exam questions in comparison to the EOCs or BBs. Trying to close that gap!
Tickersu, I’m going to hit you up for some quant tutoring next year. Statistics just makes my head hurt.
I’m trying to decide if my Ho = I pass the exam (score > 67%) whether I want to fail to reject a false hypothesis or would prefer a Type II error.
I haven’t even gotten to the last Quant chapter because the more time I spend trying to figure it out, the more likely I am to fail (by not spending enough time reviewing things I might actually get right.)
Next year? Keep your eye on the prize, and next year is LIII!
To be frank, I see the time series chapter as low yield if you haven’t gotten to it. Try to understand the time series material that pops up in the mock exams and online assessments, though. For quant, I would say regression is the more likely topic, but at best it makes up 15% of the exam. In comparison, Equity and FRA make up a minimum of 15% of the exam. I think picking up “easy” points (from your own perspective) is the best way to improve right now. I’m currently looking at pension accounting and then I’ll look at some derivatives (swaps, mainly).
I just mean the general principles of regression analysis. If I had to pick one chapter from the quant material, it would be the multiple regression chapter (10, I believe). Multiple regression just added a few things from simple linear regression, so it would help you get points in simple linear regression vignettes (mainly, more than one independent variable). Some of these ideas can be carried into time-series analysis. In addition, this chapter covers some regression pitfalls. As you said, MC, heteroscedasticity, serial correlation, working with the ANOVA table, t-tests on coefficients would be good topics to cover. and ANOVA table should be free points, since it’s most likely plug and chug (calculate SER, MSE, F-test, R-squared). If you have time, I think knowing assumptions and null hypotheses for different tests are another place to pick up points.