thanks, there was alot of confused since they look similar.

Is negative binomial a sum of a fixed number of iid geometric RVs?

you like Sheldon from BBT. Great video tho.

🤓

you literally saved my life

Well done. The document is well organized and is a very useful tool/resource.

Great, thanks so much for sharing the document!

Great, very helpful.

The way you say poission wants me kill myself

this was fantastic!!! thank you

Thanks!

Thanks for showing the cup fall off. 😂😂Made my day.

thank you this is really helpful !

great video! nice explanation

You explained this concept perfectly. I hope this comment helps you in the algorithm so more people can see your content!

Hi, the connection to the density function (of the uniform distribution) is not clear. Besides, if you look at the graph (6:20) and take the base (6-1=5) times the height (1/6), you will not get 1, which is required in a density function.

thank you sir, great explanation. 👍

Many thanks, Allah bless you

You are awesome. Could you please create more videos on statistics?

I'm learning Statistics for Data Science and this video helped me learn the difference between these two concepts! Keep up the good work!

Nice vid with the bite size info. Thanks for your effort.

Perfect, thanks! That would be nice if you let us take a screen shot from the whole board at the end.

This is well explained.

What would be the answer if i ask what is the relation between binomial and negative binomial.....?

super helpful! Thanks Michael

Thanks heaps! You explained in such an intuitive and simple way, its much easier than I originally thought!

thank you sir

This is so concise and succinct. TY for this great video - will check out others of yours for sure!

Thank you

Truly no nonsense explanation!

This is Gold!

This is Gold!

Great vid! Just an FYI, Poisson is pronounced "PWA-son" Like how croissant is "C(r)WA-son(t)" Or Eau du Toilet is "Eau du TWA-let" Merci ;)

This is totally incorrect. He has calculated the average of the sample-space (i.e., set of unique observations) instead of the population. To calculate arithmetic average of the population, he would need counts of the observations. Other way of achieving this is by using probabilities (which are nothing but relative frequencies). For a population, population average always equals the population expected value. µ = 1/N ∑ nx = ∑ (n/N)x = ∑ px = E(x) However, a sample average (which is not average of a sample space but the sample itself) can differ from the population expected value. There is no such thing as expected value of a sample. For example - if I use this guy's second case, we can clearly see the expected value of the population (which the second table summarizes) is 3.7. However, if I extracted a 12 observation sample from the population, it can turn out to be something like: 3, 2, 1, 1, 2, 2, 5, 4, 4, 1, 3, 6. The average of this sample is: 2.8. Because, in this case, there is a big difference between the sample mean (2.8) and the value we expected to get (3.7), we can conclude this sample is not representative (that is, not a good mimic) of the population.

Hey mate, just wanted to let you know this is a clear and concise explanation and really helped me fully understand the concept. Thank you

What is the resolution of this exercise: Consider a queuing model with two attendants and a waiting position operating under steady-state conditions. Suppose that if a customer arrives and finds both agents busy and the waiting position unoccupied, then the customer will wait as long as necessary for service. If the customer finds both attendants busy and the waiting position also occupied, he leaves immediately. Customers access the system according to a Poisson process with a rate of 2 customers per hour and that service follows an exponential distribution with a mean of 1 hour. The proportion of customers who arrive at the system and will not be served is: a)2/5 b)1/8 c)2/3 d)2/7 e)1/6

finally, I can understand this intuitively. I'm non-native speaker but I can understand him completely. Thank you sir!

Thank you! This is exactly what was so confusing about exponential for me, that it was so similar but I couldn't properly separate it from the others!

Me watching this 10 min before exam 🥲

My grade its saved 🤧🤧🤧

Do you have any videos on Density Variances?

very helpful, Thanks mate!

Your explanation was so good, I didn't even need to watch the whole video, it finally clicked! Thank you!

Thank you!

great explanation

Excellent explanation Michael. Thanks a lot. Do you plan to do any video on the inverse transform theorem?

Bro the eye contact in this video is 10/10!

your explanation is surprisingly clear! awesome!

Very good thanks.

Love your work Michael!! Well done. I thought perhaps you might've explained why the expectation of the DGP given the input is the mean as I'm left wondering why this is. Good luck!