Skewness and Kurtosis : the two summary stats they never taught you
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- Опубликовано: 15 июл 2024
- All about Skewness and Kurtosis, the two missing summary statistics they never taught you!
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0:00 Average
2:13 Standard Deviation
4:00 Skewness
6:53 Kurtosis
Kurtosis is critically important and it's not typically discussed in entry- or even intermediate-level statistics courses. So happy to see you covering it here, your content deserves an Olympic gold medal.
Thank you so much
thanks for the words! Kurtosis is one of my favorite topics haha
@ritvikmath Yes, I ❤️ that word! It sounds like a disease... I am going to stay home today because I have the kurtosis! 😉
Skewness measures the degree of asymmetry of the distribution, while Kurtosis measures the degree of peakedness and flatness of a distribution
As someone who is currently transitioning into Data Science from an Econ background, I appreciate the intuitive examples you use in your videos! Thanks and keep up the great work!
+1
Glad it was helpful!
Very good. A slight clarification though: heavy tails does not necessarily mean "a lot of outliers." A single observation that is 7 standard deviations from the mean is enough to indicate a heavy tail. After all, such an observation would not happen, for all intents and purposes, under normality. The tails of heavy tailed distributions, while much higher than normal tails, are still very close to zero.
Your videos are really amazing. Thank you so much for the intuitive and motivating examples. They are true gold.
Thank you for explaining the topic in such a simple manner, this topic initially was quite difficult. Thanks!
Thank you. Well done job of fleshing out the topics.
completely agree - thank you for helping everyone have a richer understanding of variation!
Lucky to have bumped on this video. Learnt a lot from it. Thanks for your approach. It's true most lectures avoid these.
I first learned about kurtosis in my high school research class - it was a stat we looked at for our project, but I really didn't know what it was aside from being a weird word...
Thank you for the explanation. This is a great, well-needed video!
I learned more than any of my uni courses in this video
Thank you so so much
Hi, thank you sooo much for the explanation! You really brought out the most important characteristics and meaning of each metrics. :)
Glad it was helpful!
Very much needed. Thank you
This is really great!
Just started learning about kurtosis for a finance course and am very happy to have found your video! Very helpful. I love how you break things down so that they're easy and intuitive to understand.
Glad it was helpful!
which course
Great video! Thank you.
Loved this video!
very well explained , clear and Crisp
Excellent summary, thank you
I have a bachelor's and Masters degree in statistics and I can say this is the best explanation so far on these concepts
My man- u r my savior and a legend.❤
Very well done
Best explanation on this topic :)
12:54 Wow, I didn't know about moments - except for "moment-generating functions".
Very useful especially the example the application of kurtosis and skewness on growth and inequality
Thanks!
Awesome explanation. Bravo!
Many thanks!
I never understood moments of a distribution.
12:21 hit me like a brick when I realised.
It’s quite a strange concept unless properly motivated
same here - there’s a clarity to the way this is outlined here that is just crystalline. subbed
Great example sir , I Understood
Thankss!
Cheers pal!
Thank you for you video.
You are welcome
Thank you for this viseo. For some reason never thought about including kurtosis in my descriptive statistics analysis.
Do you think you can do (or maybe you already did) vidoe on advanced statistics analysis: cdf, etc. More from the perspective what else can be added to it. Thank you!
9:52 correction: kurtosis of a standard normal distribution is 3. the excess kurtosis is then whatever the kurtosis is minus 3.
I really want to know more about Kurtosis! Please make a video about it!😊
Thanks for the suggestion!
You are the best!
Thanks!
👏 👏 👏 Very well done!
Thank you! Cheers!
I have a BA in Stats and can confirm the accuracy of the video's title.
Can you do a similar video with examples on other moments and things like statistical entropy? 😁
Great suggestion!
9:51
A Normal distribution has a kurtosis of 3 not zero.
Excess kurtosis for normal distribution is zero.
Pls correct me if I'm wrong.
yes you are right. I missed the word "excess" there.
I think the full distribution in visual form is the best for non-technical audiences. Then you don't have to dumb down and you don't have to explain it much either. If graphs are forbidden for some odd reason, maybe "buckets" can be a good way. Like 20% of the students had less than 30p, 30% in range 30-50p etc. Honestly even standard deviation is tricky to understand even for technical people. Ask someone who "should know" to explain - on the spot - the difference between standard deviation and the mean deviation.
11:29 What about the median?
Which is better - mean or median? Could you do a video on this?
Thanks!
Thank you for the explanation! Can you further clarify the difference between standard deviation and kurtosis? They feel similar as they relate to outliers to some degree, at least when you explained it in your example. Thanks!
Kurtosis puts more weight to observations that are far away from the mean.
Wow!
"Look at full distr." + "plot that full distribution and think about..."
The best descriptor of a set is the set :-)
Pretty much!
Problem with these higher moments is with their sensitivities to outliers. How much more data we need to "reliably" estimate these (ever-increasing) higher moments?
That is an interesting angle to look at it!
Can you Please explain how to measure skewness and kurtosis numerically? 👏❤️
What are the meaning of those symbols in the formula
Can you explain how to.calculate skewness and kurtosis?
9:16 But you said that the standard deviations of both distributions is the same, correct?
How is that possible that the number of outliers in both distribution differs, YET they still both have the same standard deviation?
Thanks!
kurtosis of normal distribution is 3. Excess kurtosis is amount exceeding 3.
CFA level 1 brought me here
THANK YOU, in my case for describing Kurtosis.
Of course!
One thing that kills me is when, like in the video example, the blue distribution is skewed to the left, my brain wants to understand as the other way around, e.g, 'to the right', because that is what the figure looks like, as if it was 'pending to the right'. D:
Good old pen and paper.
Congratulations ♥, You got a new Subscriber!.
@ritvikmath, You explained it in very intuitive way.