What's funny is that on this exact moment of the video I thought exactly the same, went to the comment section to write it and I see that's its already there haha
My first month of Prob. and Stat. lectures and these concepts were still lost on me. I now feel such a wave of understanding that the euphoria can hardly be contained. Thank you!
@@zedstatistics Love how humble you are; read your papers on biostatistics the other day. Not that I understand all (or much) of it, but massive respect and great channel! Much love. Please make many more videos!
I have just watched half the video and can't hold myself back from Thanking you for putting a video with such a clear explanation. I had watched 7-8 videos on You Tube before I found this..Simply Superb
Can't believe 16 minutes back I was struggling with this topic. Thank you for this great lecture! It was so easy to follow. This was my first video on this channel and I didn't even give a second thought and just subscribed.
You precede me by 3 months. You were stuck 3 months back where I was 16 minutes ago. And in spite of this being the only video I have watched, I too subscribed to the channel, with no second thoughts.
I hate this course so much, my lecturer sucks because she will explain the very basic understanding of the theory and then give a bunch of complicated examples as a task the next day😒😒 so thank you for making this video❤
Why don't this channel have more subscribers?! This is the best statistic video I've ever watched on ytb. It cleared my doubts halfway through the video. Thank you so much for this. You're really good at explaining things easily and clearly.
So grateful for your videos. Your teaching style is so natural and suitable for 'stats idiots' like me :) I'm probably the one who's always raising her hand to ask a question but the teacher ignores/misses my raised hand. Most stats videos I've watched assume the viewer understands everything then jumps to the next level. But you actually take the time to make sure your point is understood before moving on. Thank you so much for your superb teaching!
Absolutely adore your videos, especially the series on distributions. I’m sure these are in the works, but could you do videos on the Geometric, Beta, Gamma, Weibull, Pareto, Logistic, and their inverses of those that have them? Really get into the guts of each...
got much planned this year! Hope to get to these but they do take a while :) Have got this comment screenshotted and will give you a shout out when I hit the whole Beta-Gamma functions :)
Had to write a comment to thank you for this video, we covered this in my first year stats unit, and the lecturer has delivered the entire content of this part of the unit almost entirely in maths notation and mathematical definitions that left me puzzled and wondering whether I had registered for the wrong class, in the 16 minutes from your video I understand the content far better then I did after watching hours of lectures on the exact same topic
I have to say; I've had professors throw around the terms PDF, PMF and CDF in class. While I understood that PMF referred to discrete functions and PDF to continuous functions, I never really understood the relationship to CDF until watching this video. Thank you so much, this was excellently done.
I think your ability to explain this concept in such a succinct and clear way really speaks volumes about your grasp on the concepts - it's just as they say; you don't understand a concept until you can explain it really well to someone else. super helpful video!!
well done mate! very few people have art of explaining things perfectly (method, pitch voice, pace, graphics and more) and of course it comes naturally. You are one of them. keep it up!!
1:00 pmf 1:05 - 1:10 which is just a simple way of saying the probability of each discrete outcome 1:15 - 1:24 pdf which we use for continuous variables 1:37 - 1:44 now be careful about pdf because I have called this whole video probability distribution functions 1:44 - 1:53 some people might call all(inclued pmf ,pdf ,cdf) of these together as pdf which does get a bit confusing 1:54 - 2:02 i use pdf to mean the probability density function which relates solely to continuous variables 2:09 - 2:23 now both discrete and continuous variables can construct what is called a cumulative function
Awesome, Superb, liked you explanation and subscribed....By the way, if I use Monte Carlo Normal Distribution to predict the stock price move, do you think how accurate this method will be, if it is not very satisfied because of the flat tail, which method do you think it will be the best bet to predict the price.. thanks again! Alex
Seriously, one of the most I liked video.... Thanks for this wonderful job...🙇♂️ தமிழ்லயும் இந்த மாதிரி சொல்லிகுடுக்க ஆசிரியர்கள் இருந்தால் நல்லாருக்கும்.....😒😒
I’ve been viewing a lot of stats videos on RU-vid and there are a lot of very talented teachers that can explain this tricky subject. But, this channel is the very best of the best!
Thank you! I'm taking mathematical statistics right now and were covering CDF and PDF using differentiation and integration and I finally understand. Thanks again!!!
Holy crap this is perfect. Thank you so much. Once I hit this point in statistics, I have felt lost. The more videos covering this type of stuff, the better!
Not sure if you read comments on old videos. looking for advice please. I know nothing about maths or excel until a year or so ago (I am 70 now). I have been using Poisson to predict an event. Imagine a coin. Head, Tails and it can land on its edge. But each time the weight of the coin is heavier on the head side, or tail side or even the edge. Those variables are always known. What is the best type of probability distribution I should be using to predict head,tail or edge. I have watched your video series and now just think that Poisson is not the best fit. Any help would be appreciated. Thank you
you are amazzzzzzzzzzzzzzzzzzzzzzzing teacher. God bless you!!!!!!! I pray to god, you live a long long life more than 200 years of life, so that you can make future generation students life easier.
Thank you very much. What are you doing is changing my perspective about the concept of learning. I will definitely recommend your channel. I wish you the best 🤍
At 7:09 onwards, shouldn’t the values of the y-axis of the PDF diagram be 10 times the values shown? That is, the curve’s peak is ~0.4 (not ~0.04) at the mean of the Height (165cm)?
Thank you so much for this. What is the intuition of the gradient of the CDF? With the discrete probability example I understand the Y axis shows the probability. Does the gradient of the CDF show the probability on the PDF? Thanks.
Dear Mr. Justin I need your help and would you make a video on : Stochastic Processes: Definition and property of poison Process and Exponential Distribution, Computing exponentials, Renewal Process Results and Markov Chain Results
Exponential distribution (CDF looks exponential) is a common way to model reliability. If I would now do some Monte Carlo simulation with inverse transform sampling so that I got a group of failure times as an output, do I already see the outcome from that plotted PDF i.e. the higher the Y-axis value peak the more failure times around that time (X-axis) I would expect? The PDF of that exponential function is a decreasing curve but the hazard rate is constant. Why do we call that memoryless (yes, the hazard is constant) even though we would get more failure time values according to the PDF in the beginning (because it looks to be a decreasing curve)?
Very clear explanation thanks. Just curious, how does one determine the height minimum of 140 and maximum of 190? Using known records for short and tall? Also, I guess we are assuming that women's height distribution is about normal? I've always wondered how the population distribution for something like height is determined. Is it determined through sufficient experiments?
Hey man, I would like to take a minute to thank you for your videos. They are so clearly explained, to the point explained in short videos. Really helped in gaining a good grasp of the concepts intuitively and please continue this work of helping students like me.
Thank you for the amazing work you are doing here. I would like to make a request, if I may. Can you inform us a bit more about calculus and how it is relevant for learning statistics, particularly probability distributions? Thank you!
This video was worth it just for the insight that normal distributions graph as S-curves when graphed cumulatively. I've done a lot of thinking this year about S-curves as a problem-solving heuristic, yet was wondering how and why they emerged mathematically...this is our answer.
I'm so glad I found these videos! I am tasked with implementing some of these techniques in code. Deer in front of headlight situation without much of a mathematics background... Not anymore!!
Thank you very much.... I have my test tommorow and now I am clear with the concepts.... This presentation was much more clear than my lecture attend clg ... Keep dng such videos on statistics... It helps us students a lot.. thankyou again ❤❤
thank god you were there! I was almost halfway through my portions suddenly in book they used PDF achronym dk what they were refering. First few min through your vid my doubt got cleared. thankyou
Perfect explanation.... but would be cherry on the cake if u add in some applications and examples n how to use the y-axis values in PDF and CDF in real-time Data related applications Thank you!
You usually don't use the y-axis value of a PDF directly, in an application. You'd be asking the question of what is the probability that the random variable is exactly 1, for instance. It's a meaningless question for a continuous random variable, because the probability of the variable being exactly any given number is infinitesimal. You need to multiply (or rather integrate) the PDF across a finite interval of the random variable, for the PDF to have a meaning of probability being within that interval. It is the integral of a PDF (i.e. the CDF) that has a meaningful concept. One application where you may use the PDF's y-axis value directly, is when finding the overlapping area between two probability distributions. You find the point where the two PDF's intersect, and use that as the upper bound of integration when evaluating one CDF, and the lower bound of integration when evaluating the other CDF.
Would you please do videos on the Weibull Distribution and Triangular Distribution. All of your explanations are fantastic and I haven't found any good videos on those distributions to date.
sir/guys for a random variable how the probabilities are calculated and how the graph is plot can u please explain or provide any link to refer for good explanation iam confused 🤧
