this is kind of late but I was wondering how you got the standard deviation of baby height measurement is +/- 1.2 vs the standard deviation of adult heights of 8? How did we get the value of 1.2 and 8? I am not sure if I used the correct wording or if what I am asking make's sense but hope I can get an answer!
@@santiagoramos1313 I just googled "average adult height" and found a table that listed the mean and standard deviation for the world and individual countries. I then did the same thing for babies. In other words, I didn't calculate those values, I just looked them up in a table.
These unexpected silly songs really relieve the pressure before getting into the subject. They are an integral part of the Statquest pedagogy, they act as a mind-opener :)))
Luckily this video popped out in the beginning of my search list ! Such a clear explanation about Normal distribution that I was struggling to understand since years!
I subscribed halfway through the intro. when you know, you know. Edit> I just watched the entire video, it's brilliant. A wonderfully concise overview of the subject. I'm always looking for videos like these to give me the core explanation that I can hang the details on to as I go. 5 minutes to explain what it takes a textbook 50 pages. I can't wait to watch the next video on the central limit theory. Thank you Josh great video.
@@statquest BAM! I just finished my exam today. Your videos are super helpful and also fun. Now I don't watch them for the sake of exams just simply for fun.
Strange that we have to look for online resources like this one to try to make sense of normal distribution after we have signed up for a paid course thats supposed to teach us the concept. Lecturers and course material just assume most people know distribution at a certain level.
wow this is so much easier to understand then my dense obtuse intro to stats textbook!! thank you so much, i had no idea what they were talking about in that dang book
These videos are literally amazing, every single one is so clear and well presented and the use of visuals is top notch. Can't wait for the central limit theorem one!
dude, you make my day. every time I watch one of your vids I am usually super stressed bc our prof doesn't take the time to explain a darn thing. thanks a lot for making this free and enjoyable
Hi, In recent times i started following you, you have done a amazing job. you made the concepts are pretty clear. It would be great if you make a video about Roadmap for machine learning (from your point of view). Thanks in advance :-)
Hello,I got confused at the last part. 😭😭 I don't understand why it is not ".....low probability of measuring a mean within +/- 8 inches of the mean"? (4.08 min) Please help😢😢
At 3:20 I show that 95% of the measurements of adult males fall between 70 +/- 8 inches. In other words, the probability of measuring an adult that has a height somewhere between 70 +/- 8 inches is 95%. This is a pretty high probability. In contrast, at 4:06, we are looking at the probability of measuring an adult that has a height somewhere between 70 +/- 1.2 inches of the mean. In this case, there is a relatively low probability, especially when compared to measuring a baby with a height somewhere between 20 +/- 1.2 inches. Does this make sense?
this is an extremely precious channel whoever wants to start with stats... Thank you so much for making people's life easy by this channel. N, of course, the starting songs are really congenial.
3:27 How did you get +-1.2 inches & +-8 inches? You only said that 95% of data are within 2 sdv but you didn't explain how to calculate the range approximation.
For any normal curve, 95% of the data are within 2 standard deviations of the mean. This is just the way those curves are defined. So, since we know the standard deviations of both curves, we can just multiply them by 2 to find out what 2 standard deviations are.
Hi Josh, I have a fundamental doubt. In rolling z-score method we use a window size of lets say 15. In central theorem it states that a distribution is normal only when no. of samples >=30, which in case of rolling z-score isn't. But how are the results so good when using that particular rolling z-score method and what is statistical significance behind it?
The Central Limit theorem only says that "sums (and thus averages) are normally distributed". It doesn't give us a minimum sample size. The most commonly used "minimum" sample size is 30, but this is just a "rule of thumb" and not a fixed number. In reality the sums (or averages) are normally distributed with far fewer measurements. In my video on the central limit theorem ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-YAlJCEDH2uY.html , I used n=20 and that worked even when the original distribution was very different from the normal distribution.
Do we observe asymmetry in the bell curve in real data? Like a skew towards one side in general when we actually measure stuff. A mystery skew of the universe, I guess I am fishing for.
It depends on what you're measuring. Somethings are shockingly similar to a normal curve - it's kind of crazy how good the shape is at modeling things. But other other datasets are skewed, and for those, we have different distributions that we can use.
I am grateful to you 😍😍and I also want to explain to me logit model ,types of this model like (log-log and c log -log )and Perturbation technique🙊🙊🙊🙏🏻🙏🏻🙏🏻🙏🏻 .All my sincere thanks and respect 😍🙏🏻Joshua Starmer .
First, I thank you for your cooperation and your interaction. I really need your assistant to understand these subjects. I have benefited very much from my follow-up of the topics that you have already presented in this channel. I have a graduation project that I want to complete soon so I need to understand these topics. Thank you again .God bless you Mr Joshua Starmer 🙏🏻🙏🏻🙏🏻🙏🏻🙏🏻🙏🏻
I think that if you really want to be good at ML, you need to understand data, and the normal distribution is one of the most fundamental concepts for understanding data.
Clear and simple explanation. As most things are in the world. There is not complex or heavy themes or things. There is just people that do not know how to explain it because they do not understand them. I used this explanation for understanding limit of detection in analytical measurements. Thank you
Thanks carolina! Please hold no anger toward me for I have done you no wrong!and please succeed at all you may do.its the point you are given.not the points you take.
I didn't understand why there is a "low probability of measuring a man within +/- 1.2 inches of the means" since its the range with more measured samples. Shouldn't it be the more likely?
The "low probability" is relative to measuring a baby within 1.2 inches of the mean. Because there is more variation in adult height, there is a low probability.
I like the content and the way you explain it. Thank you. However, please buy a video editor friend a dinner and have them make a small interesting intro 😄
Hi, great videos for basic conceptual understanding. Can you make one about z-scores? I want to understand how the values are scaled, I think it does it across variables to account for differences in units.. but that stumps me up! Can you explain when to use it and when not to? Or any links where bloggers/ youtubers have already explained this properly 😅
University tells lecturers to mark students conform to NORMAL DISTRIBUTION, but sometimes it's unreasonable, I should get a HD in parrallel commputing but professor gave me 84 in order to conform to that mathamatical model.
Glad you like the videos! I have one on the binomial: ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-J8jNoF-K8E8.html and the good news is that the normal distribution is the same thing the Gaussian distribution, so you already know about that one. And I have a video on the exponential distribution (which is related to the poisson): ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-p3T-_LMrvBc.html
To learn about that, see: ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-vikkiwjQqfU.html and ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-SzZ6GpcfoQY.html
Wow, I like you. I just like you. 2 days of trying to understand normal Distribution!! You killed it in less than 5mins. Give me more with practical examples. 🤓
You use the formula for a normal distribution. www.itl.nist.gov/div898/handbook/eda/section3/eda3661.htm You plug in the mean, standard deviation and the x-axis coordinate and the output is the y-axis coordinate.
I'm not sure I understand your question. If you are asking about how to calculate the mean and standard deviation, see: ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-SzZ6GpcfoQY.html
