Got a statistics exam tomorrow and came across your movies.. Even though I'm quite confident in my knowledge for tomorrow your videos helped me a lot (especially the one on Central Limit Theorem) Thank you!
Thank you so much for this treasure of knowledge that is your channel. Please consider adding this video to the playlist on continuous distributions so it is easy to retrieve :-)
I love coming back to these videos after a lecture. They help fill in all the gaps in terms of explanation. Spot on. They are a massive help. Thank you so much. Please keep em coming. :)
These will be the "basic" stuff in the future. The job market will expect you to integrate knowledge from various disciplines and make use of them in sophisticated ways.
This arises in a number of situations. I (briefly) discuss one common one at about the 1:30 mark. In applied situations, we often use the results without explicitly stating the mathematical underpinnings.
hello, just a confusion, the Z formula i have studied is ((Xbar- mu)/ sigma) but in this video, root of n is also included. it will be really very helpful if you could answer this query to me. by the way the lecture was really great. Thanks in advance
For a single observation: Z = (X - mu)/sigma. For the mean of n observations: Z = (X bar - mu)/(sigma/sqrt(n)). It's not really two separate formulas, as the former is just the latter with n = 1.
Deriving the t distribution is far beyond the scope of this video. While not outrageously difficult to do, it's far from trivial, and requires some background knowledge in mathematical statistics (transformations, integration, etc.). In anything but a mathematical statistics course, introducing the t distribution with the derivation would be ridiculous. I may very well derive the t distribution in a future video, but I'll let it be known I'm doing that in the title.
![I.N "HALLUCINATION" | [Stray Kids : SKZ-PLAYER]](http://i.ytimg.com/vi/n5B5q1Hwt_U/mqdefault.jpg)
i think you are most professional instructor i have been meet, Big Like From Egypt :)
Thanks for the very kind words!
Got a statistics exam tomorrow and came across your movies.. Even though I'm quite confident in my knowledge for tomorrow your videos helped me a lot (especially the one on Central Limit Theorem) Thank you!
I'm glad to be of help!
Thank you so much for this treasure of knowledge that is your channel. Please consider adding this video to the playlist on continuous distributions so it is easy to retrieve :-)
I don't see the point of seating in a classroom, it is a time consuming, this way easy to understand. Thank you JB.
You're very welcome!
there are ways to do that through the internet too ;)
16? My dude I am a junior in college learning this stuff for the first time
You're creating some serious interest in my course!!!
Thank you!!
I'm glad to be of help!
Very intuitive, way better than my professor
You are the best channel on RUclips ever
I love coming back to these videos after a lecture. They help fill in all the gaps in terms of explanation. Spot on. They are a massive help. Thank you so much. Please keep em coming. :)
You give great explanations! This was very clear; I really understand the relationship between T and the Standard Normal now. Thanks!
You are very welcome Timothy. And thanks for the compliment!
you are my best teacher!
Where can I find the proof which is mentioned at 2:03 in the video?
really nice.. u r n excellent teacher.. very informative
+gaurav gregrath Thanks for the compliment!
Why go to university when i can learn from my house hahah thanks
You're very welcome!
These will be the "basic" stuff in the future. The job market will expect you to integrate knowledge from various disciplines and make use of them in sophisticated ways.
Your way forces any one to subscribe, it's rely amazing
Thanks!
Simple but brilliant explanation...thanks :)
You are very welcome!
dude long live and prosper
I love you man :P
What program are you using to create these videos?
The base is a Latex/Beamer presentation. I annotate the pdf with Skim, and use Screenflow to record and edit.
Thanks for the great lesson!
You are very welcome! Thanks for the compliment!
Your vids are super cool! Thank you!
You are very welcome!
u r legend.....
thank you so much, this is really helpful
So what are the cases when we have to divide Z (normal R.V.) by U (Chi² R.V.)?
This arises in a number of situations. I (briefly) discuss one common one at about the 1:30 mark. In applied situations, we often use the results without explicitly stating the mathematical underpinnings.
love ur videos , keep them coming
Thanks! There are definitely more to come. I'm hoping for a productive holiday season.
hello, just a confusion, the Z formula i have studied is ((Xbar- mu)/ sigma) but in this video, root of n is also included. it will be really very helpful if you could answer this query to me.
by the way the lecture was really great. Thanks in advance
For a single observation: Z = (X - mu)/sigma. For the mean of n observations: Z = (X bar - mu)/(sigma/sqrt(n)). It's not really two separate formulas, as the former is just the latter with n = 1.
Thank you, Sir
He is not explaining how the formula are derived instead always only shows the formula upfront and how to handle them. :(
Deriving the t distribution is far beyond the scope of this video. While not outrageously difficult to do, it's far from trivial, and requires some background knowledge in mathematical statistics (transformations, integration, etc.). In anything but a mathematical statistics course, introducing the t distribution with the derivation would be ridiculous. I may very well derive the t distribution in a future video, but I'll let it be known I'm doing that in the title.
2:00 I guess it should be wiDth, not with. There is a typo. The tutorial is great tho!
Derive f(t) idhukku and pls
If only I knew/understood what "degrees of freedom" mean :/
cool
Bomboclaat
Finally got to see the pdf of a t-distribution and boy… it sure is ugly 😂