In this #statistics lecture we learn about the idea of a percentile/quantile, as well as a few specific percentiles/quantiles often reported (quartiles). There are many different accepted ways to calculate a #percentile/#quantile. Here we will not focus on how to calculate these by hand, but rather focus on what they are conceptually and what info the tell us. 👩🏻💻To calculate Percentiles and Quantiles using #RStats software watch this video ( ua-cam.com/video/ACWuV16tdhY/v-deo.html ) Like to support us? you can Donate ( bit.ly/2CWxnP2 ), Share our Videos, Leave us a Comment, Give us a Like 👍🏼 or write us a review! Either way, We Thank You! 🤓You can find the Best Statistics & R Programming Language Tutorials here: ( goo.gl/4vDQzT )
Greetings, Prof. Marin. I want to thank you for posting all these lectures. They are life savers. I couldn't understand things in class, but your lectures are so detailed that it helped me understand even the difficult concepts. Thank you once again.
thank you so much for explaining the concept. For a person like me who know nothing about statistics, your explanation is so clear that now I understand about the concept
@marinstatlectures thank you very much for such a good video. I learned maximum statistical analysis using R from your videos and lectures. I am very much grateful to you. i published my analysis in many reputed journals after conceptualize from your videos. Thank you very much again.
Do you ever have any problems with large sets of data, where the floating point math the software does doesn't match up with the inputs you intended to put in it? I have to regularly process entries in the thousands; and I end up either not doing the full workups I'd like, or doing most of them by hand, because the accuracy of the calculation suffers to floating point errors, until spreadsheeting and calculator softwares diverge far enough from the input, that they won't sum up enough numbers properly. Would a dedicated statistics software like the one you talked about in the video help, or is it just the nature of the beast when dealing with large datasets?
Thanks for the great explanation. In an online course, I found the following statement as a description of an example “The 90th percentile of incomes is $135,000: 90% of households report an income of $135,000 or less, 10% report more.” is that wrong? Based on your video’s explanation, it should say instead “90% of households report an income of less than $135.000” (i.e. the 90th percentile is not part of the bottom 90% of observations).
Are you actually writing Leonardo style or is this CG? Also, I know its just an example, but why would a grade be high or low depending on others grade(?)
Very nice explanation. Well done! 👏👏👏 Are there any changes in the lighting or after-production? The image is much more clear with much better / true colours. 👍👍👍
sort of....the technicians at UBC Studios who run the Lightboard Studio where I record have, at times, forgotten to turn on the back/side lights which help make the screen more vibrant. we noticed it after one 3-hour recording session,....but too much work had been done to do it all over again :(
Hello sir! Its more than helpful to watch your videos and to learn from you. I had a request kindly do consider it. I need more lectures about RDA (redundancy analysis) the basics and the ways to carry out. I dont have any mentor for guidance. Please help me in this regard.. i shall be thankful to you. I hope you will consider my request ASAP...
thanks! yes, we have plans to create a series of videos for a few different topics, like: using ggplot2, using dplyr, maybe a bit about RMarkdown, and a few others. the challenge is to find the time to make them :)
In this #statistics lecture we learn about the idea of a percentile/quantile, as well as a few specific percentiles/quantiles often reported (quartiles). There are many different accepted ways to calculate a #percentile/#quantile. Here we will not focus on how to calculate these by hand, but rather focus on what they are conceptually and what info the tell us. 👩🏻💻To calculate Percentiles and Quantiles using #RStats software watch this video ( ua-cam.com/video/ACWuV16tdhY/v-deo.html ) Like to support us? you can Donate ( bit.ly/2CWxnP2 ), Share our Videos, Leave us a Comment, Give us a Like 👍🏼 or write us a review! Either way, We Thank You! 🤓You can find the Best Statistics & R Programming Language Tutorials here: ( goo.gl/4vDQzT )
Greetings, Prof. Marin. I want to thank you for posting all these lectures. They are life savers. I couldn't understand things in class, but your lectures are so detailed that it helped me understand even the difficult concepts.
Thank you once again.
You’re welcome! Thanks for this, it’s nice to hear :)
that child's voice at the end of the videos is the cutest thing ever🥺 also, great explanation, thank you!!😃
thank you so much for explaining the concept. For a person like me who know nothing about statistics, your explanation is so clear that now I understand about the concept
I couldn't get some details from my textbook and other videos, but finally understood it through your explanation. That helped. Thank you!
I loved the voiceover (of your child I would assume) at the end of the video. And great explanation too. THANK YOU!
Pretty compelling. Great class
@marinstatlectures thank you very much for such a good video. I learned maximum statistical analysis using R from your videos and lectures. I am very much grateful to you. i published my analysis in many reputed journals after conceptualize from your videos. Thank you very much again.
And that little baby voice at the end.. just made my day! Thank you very much..
Thanks, appreciate that!
Thank you so much. You are an amazing teacher!
Sir you made statistics very easy for me, very usefull course, Even in paid courses we cannot see such kind of explanation,
wish I had discovered this channel sooner
subscribed! Thanks for this video it helped me a lot!
If n =2,3,6,7,1/2 ,8/3 and square root of 16
Find summation x2
(Summation x)to power2
The real-life focus of the teaching.
concepts over calculations :)
Do you ever have any problems with large sets of data, where the floating point math the software does doesn't match up with the inputs you intended to put in it?
I have to regularly process entries in the thousands; and I end up either not doing the full workups I'd like, or doing most of them by hand, because the accuracy of the calculation suffers to floating point errors, until spreadsheeting and calculator softwares diverge far enough from the input, that they won't sum up enough numbers properly.
Would a dedicated statistics software like the one you talked about in the video help, or is it just the nature of the beast when dealing with large datasets?
Thank you
Thanks for the great explanation. In an online course, I found the following statement as a description of an example “The 90th percentile of incomes is $135,000: 90% of households report an income of $135,000 or less, 10% report more.”
is that wrong? Based on your video’s explanation, it should say instead “90% of households report an income of less than $135.000” (i.e. the 90th percentile is not part of the bottom 90% of observations).
Either statement is fine. For a continuous variable the probability of any particular value is 0 eg P(income=135k)=0, and P(X
How should I convert this r code to sql
As. Data. Frame(quantile (probabilty),c(0.5,0.3,0.7)) ??
Helpful video 👍
Great! Concepts is the most important thing
agree 100% :)
Before you find the value that cuts the data in half, shouldn't you order the data first?
Yes, that’s what written there in the explanation of the median. You can also see that the data on the screen is already ordered.
osm explanation sir
?
Im I the only one person that enjoys the Leonard'stlyle writing?
Superb platform. Which software you are using for this?
We have it available to us at UBC Studios, you can search “light board” to get a sense of how it works
Are you actually writing Leonardo style or is this CG? Also, I know its just an example, but why would a grade be high or low depending on others grade(?)
How the hell this gentleman writes backward? Witch?
This much sufficient for data science statistics???
Very nice explanation. Well done! 👏👏👏
Are there any changes in the lighting or after-production? The image is much more clear with much better / true colours. 👍👍👍
sort of....the technicians at UBC Studios who run the Lightboard Studio where I record have, at times, forgotten to turn on the back/side lights which help make the screen more vibrant. we noticed it after one 3-hour recording session,....but too much work had been done to do it all over again :(
Top top..
Hello sir! Its more than helpful to watch your videos and to learn from you.
I had a request kindly do consider it. I need more lectures about RDA (redundancy analysis) the basics and the ways to carry out. I dont have any mentor for guidance. Please help me in this regard.. i shall be thankful to you. I hope you will consider my request ASAP...
he didn´t how to calculate the exact value of 40% quantile or 90% etc..
excellent video! I am wondering whether you could do more videos for R application? That would be really helpful !
thanks! yes, we have plans to create a series of videos for a few different topics, like: using ggplot2, using dplyr, maybe a bit about RMarkdown, and a few others. the challenge is to find the time to make them :)
@@marinstatlectures Can't wait!
thank you