why is 'n' also attributed to number of random variables? isn't n only the number of observations? how can observations be variables? The terminology is confusing.
M is the no. of observations of the Binomial distribution , whereas there are n independent identically distributed Randdom variables which follow the Bin(m,p) distribution.
n cannot be unknown else how will have your observed data X and in Binomial n is fixed. And in Negative Binomial n is variable and number of success is fixed.
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you are a good soul.
2:15 why is it all the way up to n? I thought the n was the number of RVs not the number of outcomes of a particular RV
why is 'n' also attributed to number of random variables? isn't n only the number of observations? how can observations be variables? The terminology is confusing.
M is the no. of observations of the Binomial distribution , whereas there are n independent identically distributed Randdom variables which follow the Bin(m,p) distribution.
Those n RV's constitute the random sample.
@@TiyaSen1201I think he means at 2:15 where he says x_i is from 1 to n. Why is the number of outcomes the same as the number of random variables
So great Exploration! Thankyou a lot!
the product start from 1 or 0
Thank you so...so much for this.
It really helped me a lot.
Great explanation, thanks a million!
Does it mean that m and n are not the same ??
thank u so much, this is extremely helpful 👍🏼
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JAI SHREE RAM
Well taught ,thanks so much, understood
Thanks! Helped a lot :)
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You Sir are awesome!!!!!
good explanation..it helped me a lot
Helpful Sir
🙏
Thank you very much.
Why are we differentiating twice?
sign of second derivatives give us whether something is a min or max :)
How do u estimate n when p is known
Is it you mado ?
Good explanation
Thanks so much for this!
good video
Good explanation! But what if both n and p are unknown?
n cannot be unknown else how will have your observed data X and in Binomial n is fixed. And in Negative Binomial n is variable and number of success is fixed.
Thanks sir