I love that you really make sure basic definitions, assumptions ect. are clear before jumping into the explanations. All of your videos are super sturctured and understandable because of that. Thank you!
it was really usefull to understand the theorie, but I have a question, what happend when the variables have different distributions (beta, log normal, weibull). Which changes are neccesary?. Thanks for the video!
it doesnt affect the sample mean distribution as according to the central limit theorem as long as the variables are independent ,the sum always tends to normal distribution. Even if they are not identical.(given N is large)
I love that you really make sure basic definitions, assumptions ect. are clear before jumping into the explanations. All of your videos are super sturctured and understandable because of that. Thank you!
You made Machine Learning easy to understand . Great series . Thank you!
Sir the video is great but the voice is very low.
great series as usual, thanks again.
Thanks Dear
it was really usefull to understand the theorie, but I have a question, what happend when the variables have different distributions (beta, log normal, weibull). Which changes are neccesary?. Thanks for the video!
it doesnt affect the sample mean distribution as according to the central limit theorem as long as the variables are independent ,the sum always tends to normal distribution. Even if they are not identical.(given N is large)
thanks and please post more
thanks buda