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Hi, I understand that the assumption that the sampling distribution of OLS estimators b0 and b1 is asymptotically normal is a key property. However I'm a bit stuck as to why that is. I assume the magic CLT comes into play here, but I guess there are stil grey areas for me. When we apply the...

Understood. Thank you!

Hi, I was wondering if there was maybe an issue with the formulation of the sample problem #2 on page 101 of the Miller study notes (Bayesian analysis). Question number asks "what is the probability that this manager is a star now?", but it does not define what "now" is - only after looking at...

Thank you @David Harper CFA FRM It was actually a great question to underline the difference between sum of variable and weighted sum of distribution functions. I hadn't even realized I didn't really understand the difference until now.

@David Harper CFA FRM , @Nicole Seaman, all of this makes sense now, I thank you for that.

Hi, I am working on Chapt.4 end of chapter Q&A (which I now understand are not written by David! :)). I am slightly confused by the second portion of Q12 (p96 of study notes) -"what are the mean and standard deviation of a portfolio where the return is a 50/50 mixture distribution of A and B"...

Hello @Nicole Seaman, one last question about the Question Set. I did read the FAQ, promise :), so apologies if I am still a bit confused. For Quant. Analysis, I did find this question "P1.T2.719. One- versus two-tailed hypothesis tests (Miller Ch.7)" that I did not find in the question set pdf...

Thank you very much @Nicole Seaman for the helpful and detailed answer. Florence

Hi @Nicole Seaman, I have been trying to confirm/complete my understanding of the BT website / study materials as far as questions are concerned, as I want to make sure I am using it fully and effectively. I am just trying to recap everything based on the information Let's use the Quantitative...

Hi David, Thank you very much for the detailed feedback, it is much clearer for me now. So when we say "the variance of each random variable is equal to the population's variance divided by n", the random variable is the sample mean itself, i.e. the average of the dices (sample size n) and not...

Hello, I was reading about the Central Limit Theorem today, in the study notes for Miller chapter 4 (p79 specifically), and I realized that I am unclear about the following: (I) we indicate that the variance of each random variable is σ^2/n. As we have shown in the preceding ochapter, this is...