Well, I've watched other videos to this topic before. So tbt, I didn't got in touch with the derivation for the first time, but it made klick. Very intuitive, this helped alot to get a sense of the formula, thx.
Great explanation but why did you take (1- 1/1000000)^1000000 to the power of -1? i get that we then get e^-lambda but why did you do that in first place?
+vrj97 This distribution is for when you have MANY experiments and a small probability of successful outcome of each experiment. If we can use an arbitrary large amount of experiments, then this distribution is best shown if we take our arbitrary amount to mean infinitely many experiments.
Thank you! It is the only derivation I found exact through whole search. Thank you!! My happiness!!
This was an absolutely perfect explanation. I have been looking for something like this for awhile. Thanks!
+Bret OBrien Thank you!
Well, I've watched other videos to this topic before. So tbt, I didn't got in touch with the derivation for the first time, but it made klick. Very intuitive, this helped alot to get a sense of the formula, thx.
nice! Poisson distribution demystified. Thank you. Text book just throws an equation at you
VERY NICE VIDEO, THANK YOU SO MUCH❤
I love you right now! needed that for an assignment..
That was crystal clear. Thanks!
Great Video with very clear explanation.
thank you very much.that is a clear explanation.
Great explanation but why did you take (1- 1/1000000)^1000000 to the power of -1? i get that we then get e^-lambda but why did you do that in first place?
You can prove that the limit equals e using natural logarithm properties and l'hospitals rule from calculus.
That was a good explanation. Everything makes sense to me except for the fact that you said that ∞ /∞ = 1 but isn't it an indeterminate form?
excellent work
nice and clear, thanks.
at 8:31 , why did he take negative power?
Good Work!!
salute to u man
Can someone explain to me why we take the limit as n goes to infinity?
+vrj97 This distribution is for when you have MANY experiments and a small probability of successful outcome of each experiment. If we can use an arbitrary large amount of experiments, then this distribution is best shown if we take our arbitrary amount to mean infinitely many experiments.
Thanks!
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Hahaha :)