Thank you Sir, Just wanted to say thank you, for giving me a deeper insight into probability. I studied this from the 1st lecture to this. I will study the last 5 lectures later on.
The same for me, it was an amazing experience in the intuitive understanding of some of probability theory terminology. Also his co-author textbook is so valuable and in the book they gave CLT as the last thing.
@@aniruddhnls This is because at 9:16 he takes n =8 and at 15:02 he takes n = 16 and more. In a nutshell, (as he explains later), if the distribution is skewed you have to take a larger n to approximate normal distribution.
Thank you Sir, Just wanted to say thank you, for giving me a deeper insight into probability. I studied this from the 1st lecture to this. I will study the last 5 lectures later on.
The same for me, it was an amazing experience in the intuitive understanding of some of probability theory terminology. Also his co-author textbook is so valuable and in the book they gave CLT as the last thing.
An error is made at 21:50, but is then corrected at 23:48.
at 15:02 he says the pmf's have approximated to normal. Before at 9:16 he said pmt's do not approximate.????
@@aniruddhnls This is because at 9:16 he takes n =8 and at 15:02 he takes n = 16 and more. In a nutshell, (as he explains later), if the distribution is skewed you have to take a larger n to approximate normal distribution.
this prof is so good at lecturing
+1, i didn't really like statistics, but this lecture series has completely changed it for me.
In love with course outline
Mid point integration is like, we both can’t be right so lets settle on being half wrong each 😂
the puzzle is really good!
godly lecture skills. what a man
One of my students suggested a short cut to calculate probability P(Z>2) without using 1-P(Z2) is equal to P(Z
Thats true because we assume a normal distribution and as it is symmetric , the value will be the same
The potential issue is that most normal tables only show Z >= 0, accounting for that symmetry to save table space.
at 15:02 he says the pmf's have approximated to normal. Before at 9:16 he said pmt's do not approximate.????
@@aniruddhnls watch all the lecture. He literally explain that pmf of Bernoulli is a special case
Excellent Prof!!! ElGrecoProf++
i only understood pdf
how is standard deviation is f(1-f)..at 19:40 ???
It is 9 month later but f(1-f) because we have a binomial distribution with a mean equal to f, so the variance would be f(1-f)