I enjoy this video very much. You sure prepared your lecture so well and presented it step by step making it so simple to understand. Thank you much !!
Thanks this helped a lot. Had no idea what an outlier was because my professor forgot to go over it with us, and then gave us an assignment on it. So i resorted to youtube to teach me lol.
The video is very helpful. thank you. I have a question about the max value, in general per the video the max value is 50, but when the max value becomes the outlier then what will be the max value where the whisker will be plotted. Because 50 is outlier, does it mean any value near but less than or equal to 36.5 becomes the max value.
+Jagadish Katam Thank you for your question. In the example you referenced, the maximum value, 50, turned out to be an outlier. However, it is still the maximum value of the data set and will be the end point of the right whisker in the boxplot. We do not dismiss it as the max value because it is an outlier.
From my understanding, you can’t dismiss 50 as the max value. That is why we do the spread analysis in the first place in order to determine if our data or sample is skewed by an outlier if there is one present in the data set. And so, that is why we first try to find things such as range, variance, standard deviation and quartiles in order to see if the MEAN is a good representative of the population sample. That’s my inference from my stats class
It was explained well up until the equation to determine outliers. In the video, we are expected to learn the equation by rote, rather than understanding it. I think the video would be better if you explained what you were doing by using a diagram and also giving an explanation as to why the mathematical community accept this method rather than any other alternative. Otherwise, it's mindless.
Step 3 and step 4 are not correct equation should be [Q1 -1.5 (IQR) , Q3 + 1.5 (IQR)]. But they just did [Q1(IQR) , Q3(IQR)] then got the value set from that. ]
+gole rural We use 1.5 to create an interval. The interquartile range (IQR) represents the middle 50% of the data values. When we multiply the IQR by 1.5, and then add/subtract this value to/from the IQR, we are creating a larger interval around the IQR, that is twice the size. This larger interval is centered (geometrically speaking) around the median. Any value outside of this interval is considered too small or too large and not descriptive of the data set. One can visualize this by imaging the box in the boxplot as a strip of paper that can be cut in half and then opened up like window shutters.
That random dog howl was an interesting addition to the tutorial.
who said it was a dog?
@@ericphan8806 the lady in the video
that dog howl played a perfect role for me to grasp the concept of box plots hence it was very essential
your dog howling freaked me out....................nice tutorial helped me a lot....thanku
Holy crap I was falling asleep watching this and your dog howled! I thought the world was ending.
LMFAO!! That was hilarious....
who said it was a dog
this was the easiest video I've ever experienced. Keep up the awesome work!!!
You're one of the best things america has ever produced
lol I was watching this in a quite library & started laughing out loud at that dog segment, awkward
I enjoy this video very much. You sure prepared your lecture so well and presented it step by step making it so simple to understand.
Thank you much !!
Very clearly explained. Concepts are crystal clear. Thanks a lot.
Lovely video. Thanks. Got what i wanted after a long search!.
Thank you so much!
excellent video
Excellent video!
You pooted, then the dog howled. You didnt think that anyone heard it? Well I did. Great video too.
Lol the dog was the best part
Thanks a lot for such a concise and clear info on quartiles , really helped me a lot :)
thank you very much may god bless you
4:55 that was amazing
THANK YOU! That helped so much you don't even know.
Very nice explanation ma'am.Keep posting such videos. Simple and clear explanation 👌
Thanks this helped a lot. Had no idea what an outlier was because my professor forgot to go over it with us, and then gave us an assignment on it. So i resorted to youtube to teach me lol.
Well explained. Clear and to the point Prof. I wish you could also post a lecture on Dixon Q-test, will really appreciate.
simple and to the point, thank you :)
Thank so much MathJaxx. i learnt a new way to count Q1 and Q3
The dog carried the video 😌
4:55 when the EARTH STOOD STILL
Thank you so much! This was really helpful, and helped prepare me for my math test!
thank u very much you have done a brief explanation and this video solved all my confusion about this topic keep it up may allah bless you.
what a perfect explaination. really helped me understand it. thankyou. 👌
Excellent video
Thank you for your lesson!
Year 2023 but still best
Aw this teaching is good for me I needed It so bad for my next exam.
Thank you great helpful video!
Dog: OOOOOOOOOOOOOOOO
Everyone: Lol thats funny😂
Me: IM TRYING TO LEARN HERE DUDE!😡😡😡😡😂😂
good and concise to refresh concepts.. :)
it is very informative and descriptive teaching on Box plot, Quartiles and Outliers study
Cheers mayo 🙌
great video
Your alarm was really annoying😂😂😂. By the way I find your explanation very helpful. Thanks. Love from india
Really useful thanks
Thank you so much
Omgg thank u so much this is so helpful!!
GO ON 4:55 that's when it howls
Thanks, means a lot...
I like your voice ... falling asleep...
Then dog howls
🤣🤣🤣
You liked it after 2 years ... Why 😅😅
Tell me
my math teacher put this video for assignment so i wanted to see comments
very nice, thants
amazing video really its help me alot
thats as awesome explanation...
awesome
Great!
thank you so much.... why we choose 1.5 in finding outliers
Very helpful
Thanks
Thnx ❤
The video is very helpful. thank you. I have a question about the max value, in general per the video the max value is 50, but when the max value becomes the outlier then what will be the max value where the whisker will be plotted. Because 50 is outlier, does it mean any value near but less than or equal to 36.5 becomes the max value.
+Jagadish Katam Thank you for your question. In the example you referenced, the maximum value, 50, turned out to be an outlier. However, it is still the maximum value of the data set and will be the end point of the right whisker in the boxplot. We do not dismiss it as the max value because it is an outlier.
From my understanding, you can’t dismiss 50 as the max value. That is why we do the spread analysis in the first place in order to determine if our data or sample is skewed by an outlier if there is one present in the data set. And so, that is why we first try to find things such as range, variance, standard deviation and quartiles in order to see if the MEAN is a good representative of the population sample. That’s my inference from my stats class
Tnq so much
shes good
Yey!!
salute to u madam
Median does not always = mean (average)
may be i think its formula 1.5(iqr) is it right?
which sofware is this
Are you Devasenadhipati's Daughter ???
It was explained well up until the equation to determine outliers. In the video, we are expected to learn the equation by rote, rather than understanding it. I think the video would be better if you explained what you were doing by using a diagram and also giving an explanation as to why the mathematical community accept this method rather than any other alternative. Otherwise, it's mindless.
You said it perfectly. I was hoping there would be some explanation not just listing formulas.
Can both minimum and maximum be outliers?
Yes, you can have multiple outliers - very large, very small, or both!
is it posible to have all of the data as the outliers? becouse when i do calculate my data, all my data are outside the outlier
No, this is not possible. Perhaps you have a calculation error.
hmmb, thank you, i'll check it again,
+MathJaxx yes, my calculation was wrong, thanks a lot madam, its help me doing my assignment
Hi Ms Hu
hey
Step 3 and step 4 are not correct equation should be [Q1 -1.5 (IQR) , Q3 + 1.5 (IQR)]. But they just did [Q1(IQR) , Q3(IQR)] then got the value set from that.
]
why we use 1.5 while testing outliers.?
+Santhosh Sreshta dats ma question as well lol
+gole rural We use 1.5 to create an interval. The interquartile range (IQR) represents the middle 50% of the data values. When we multiply the IQR by 1.5, and then add/subtract this value to/from the IQR, we are creating a larger interval around the IQR, that is twice the size. This larger interval is centered (geometrically speaking) around the median. Any value outside of this interval is considered too small or too large and not descriptive of the data set. One can visualize this by imaging the box in the boxplot as a strip of paper that can be cut in half and then opened up like window shutters.
When you test a data point and find that it's "good", can we say that it's "usual"?
4:54
What in the hell