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I just sat for PA and I wanted to give a HUGE shoutout. This content is amazing and without saying any details I felt fairly confident with the exam, I will definitely be recommending this site to all my actuary friends
2019-12-15 15:52:39 UTC
I found that the with its focus on hands on training, the course delivers a significant boost to meeting all the learning requirements for the PA exam.
2020-01-07 15:55:53 UTC
Buy 👏 this 👏 course 👏 This course is the reason I passed in December. I wouldn't have been able to prepare at all on my own, and I wouldn't have even been willing to TRY and sit for the December exam if it weren't this product. I passed after only studying for 2.5 months because Sam is really THAT GOOD.
2020-03-02 01:46:50 UTC
I almost exclusively used ExamPA.net for studying for PA, and I basically crammed it all in two days and I got an 8!
2020-03-02 23:59:32 UTC
All in all I felt pretty prepared for the exam. I have a very strong recollection that the exam itself said your final model was to be the glm, Poisson with a log link, with or without the pc1 in it. As in the task stated this would be final model, and then explored the lasso. The lasso then removed a few variables but performed slightly worse on the test set. Leaving a perfect example of interpretability versus complexity of model coefficients. Maybe someone else interpreted the questions differently and I will be kicking myself at a later point. The other point was the lasso however was that it provided code to put the dataset into a model matrix. Which the glm function does not need, this then instantly provided binarized level variables so the lasso would only remove some levels of the variables. So explicit code for binarization wasn’t needed in this case and I explained in my answer this advantage.
2020-07-06 03:48:58 UTC
The videos really helped my understanding of topics. Especially being able to hear the thought process while doing a question. Also, the tips on exam setting and how to save time were really helpful.
2020-07-06 03:47:37 UTC