Symmetry and Skewness (1.8)
ฝัง
- เผยแพร่เมื่อ 13 พ.ย. 2015
- Learn about symmetry and skewness with respect to histograms, boxplots, and stemplots.
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Teacher: Define skewed
Me: I'm skrewed
Haha
hahah!!! def me
On a serioud note tho, this helped me a ton. Thanks a lot!
A brief message to let you know that your Statistics 1 videos are awesome! So easy to learn with all of them. Thanks so much!
Thank you! I see by the comments, that I am not the only one that struggles with this. I am in a Statistics course, it will great when things adhere to my brain. 😄
Amazing video. It not just taught how to read skewness in boxplot, but also cleared the miss conception that median and mean are not always same.
Skeweness : refers to asymmetry in a graph. Direction of skewness can be determine through the 'long tail' in a distribution.
Central tendency can also help to determine the skewness of a graph.
▪️Symmetrical : Mean = median. Mean determines the balance point while the median detrrmine the symmetry sinceits the middle point. 2:49
▪️ Skewed to the left : mean < median. Mean is closer to left side of distribution 3:41
▪️ Skewed to right: mean> median. Mean is closer to right side of distribution 3:49
Thank you for making this so easy. I’ll have to watch a few times since it seems inherently tricky but itll stick eventually
It could not be explained more beautifully or simply! Thank you once again.
This is conceptually very good already it would be nice if the author also discuss transformations in one of the videos say like common transformations include square , cube root and logarithmic and why we need to improve this kind of skew in real world data processing
Amazing content, beautiful and easy to understand, clear and simple and highly recommended for all statistics enthusiasts! Not to mention the extremely cute animation and characters! Gob Bless You!
Straight forward and precise
Loved the video
What a simple and easy way to explain this tricky concept. Thank you.
So simplistic and easy thanks for the effort !
greetings from Australia ! Thankyou so much.
Wonderfully explained. Thank you!
This is a fantastic explanation. Thanks man
Great, big help! Thanks a lot!
Thank You very much! Helped a lot
gotta luv da way of ur explanation
You are a tremendous help and valuable resource for all statistics students.
So helpful thanks!
The graphics are so clean to look at. It lessen the stress, Statistics gave.
thanks a lot!!!
I'm full of hope now,😭thank you so much.
very helpful videos. thank you.
greetings from Turkey! I love your simple explanations!
Thank you so much :) Greetings from Canada!
Great Explanation!
Very nice explanation wanted more and more content like this 😊
OMG how beautiful!!
THANKS!!
Thank you for your comment!
Such a beautiful and clear explanation. Thanks. :)
Greetings from Costa Rica.
bundles of thanks
what a great explainer. way better than these so called "stats" channels
Your explanations are so lucid.. ❤️
Thank you so much for your comment!
good explanation!
Understood your explanation..
I'm a senond year student studying Economics .
❤❤🇵🇬🇵🇬
Amazing tutorial ❤please explain poisson distribution also
Awsome explaination😀😀
thank u for this
thanks a lot
Where is the kurtosis part?
Thank you for making this whole series it helped a lot.🙏
greetings from India
BEST VIDEO ABT THIS TOPICI EVERRR
Thanks sir ❤
thanks for making this so easy.
I do what I can :) thank you for watching!
Very helpful
Amazing
thank you
sir can we consider outlier in oder to count the range = max - min suppose my outlier is minimum shall i consider ?
Great teaching, please what software did you use for this animation
Learnt lot from this vedio
excellent
1:08 If I have a double or back to back stem & leaf plot do I pivot each set of leaves as if they were two individual stem and leaf plots? To put it another way do I mirror the left leaf plot over to the right side so that it's above the stem when I rotate it?
I'm asking this here because everyone talking about back to back stem & leaf plots either don't talk about skewness or the ones that do have their comments turned off.
Wow that helped a lot thanks sm
No problem! Thanks for watching
Hi, great videos so far. I have a doubt however, on the Wikipedia page for skewness, there is a para that states:
" Many textbooks teach a rule of thumb stating that the mean is right of the median under right skew, and left of the median under left skew. This rule fails with surprising frequency. It can fail in multimodal distributions, or in distributions where one tail is long but the other is heavy. Most commonly, though, the rule fails in discrete distributions where the areas to the left and right of the median are not equal. Such distributions not only contradict the textbook relationship between mean, median, and skew, they also contradict the textbook interpretation of the median."
This seems to contradict the video statement at 3.06. Could you please help on this.
I am so astonished about this video ,i am better able to do this problem.
Please make a video on kurtosis
What about the mode,does it effect the skew stuff?
Great
Damn man, this is it!! Thank you very much for sharing!! Very well and great explained! Greets from Switzerland, Zurich
Cheers!
Thank you.
Thank you as well for watching!
👍Nice
literally the perfect video
Thank you!!
I've seen this ad ( when the guy says, he's applying to residency school) a million times on many videos. Did this guy ever get into residency school?
Thank you so much!!! I really appreciate it :)
Why do we calculate standard deviation by using mean? Namely, why dont we use mode instead of mean?
You have said that to find the median: If items are N = 10, the median will be the average of the values at positions 5 and 6 (which is the middle). But for the median in the skewed histogram, you said the median is between the interval 16 and 18 which is the 8 interval, and it's not in the middle. It's a bit confusing. Can you clarify.
gold.class.teaching
I have a doubt. In case of a left skewed distribution, you said that mean < median. When we move from a symmetrical to an non symmetrical distribution, won't the mean also shift towards right side just like median? So can't there be a scenario when mean becomes more than the median?
Hi,
No the central value( mean) remains always approximately at the center of series irrespective of the distribution you have. There would be shift in the mode and median in negatively or positively skewed distribution
Mean is mathematical average ,so it remains the same somehow. While mode n median are positional average which changes its position according to skewned n non skewed distribution
sir are we allowed to draw an outlier in a histogram ?
You certainly can!
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😊
a+
3:45 sshouldnt it be the opposite?
If the frequency is higher on the right, the mean or average should be higher while the median, which is the central value should still be towards the center ?
I dont get it
So at 3:45 we see that the graph is skewed to the left. You are correct that the frequency is higher on the right. But because it is, there are more data values in this region. Since the median is always located at the middle of an ordered data set, we know that the median is going to be closer to this chunk of data. It is for this reason that the median is not located in the middle of the histogram, but rather towards the right of the histogram to accommodate for the chunk of data. If you still don't understand this, pause at 3:24 and if you do the calculation you'll see that the number of data points to the left and right of the median is going to be the same, and it should be that way because the median is always located in the physical middle of an ordered data set. As for the mean, we know that it is the balance point of a data set, so we know that it cannot be located in this high-frequency part of the histogram. I hope that helps!
But please once tell the logic without numbers that why should mean be greater than median in a rightly skewed distribution
Most variables that are generated by multiplicative processes (such as household income) are skewed to the left. If, when we take logarithms of the horizontal axis, we transform the variable so that the distribution is normal, we call the variable lognormal.
sir recive greeting from pakistan thanks
why were you saying distribution like that ahahaha 3:41
math in no yes
This makes not since to me
This is one of my older videos that I will be re-doing in the future. What part didn't make sense to you? I can try to explain through here