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Quantile-Quantile Plots (QQ plots), Clearly Explained!!!

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Quantile-Quantile (QQ) plots are used to determine if data can be approximated by a statistical distribution. For example, you might collect some data and wonder if it is normally distributed. A QQ plot will help you answer that question. You can also use QQ plots to compare to different datasets that you collected to determine if their distributions are comparable. This video shows you how to do both things.
NOTE: The data in this video are measures of gene expression. If "gene expression" doesn't mean anything to you, just imagine that the data represents how tall a bunch of people are, or how much they weigh. Then consider the y-axis to be the height or weight of the people, and the x-axis just represents all of the data you collected on a single day. In this case, all of the data were collected on the same day, so they form a single column.
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Corrections:
4:35 The Uniform Distribution has one extra quantile
5:30 I should have said that Quartiles divide the data into 4 parts.
#statquest #quantile #qqplot

Опубликовано:

20 авг 2024

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Комментарии : 474

@statquest 2 года назад

Support StatQuest by buying my book The StatQuest Illustrated Guide to Machine Learning or a Study Guide or Merch!!! statquest.org/statquest-store/

@maindepth8830 2 года назад

that intro alone, made me forget my hate for statistics and instantly fall in love with it

@statquest 2 года назад

Hooray!

@kittyxing 3 года назад

Thanks sooooooo much! This is the only video I found explained the details of generating QQ plot and also make the concept so clear and easy to understand!

@statquest 3 года назад

Thank you very much! :)

@timonveurink6335 5 лет назад

Haven't seen the video yet, but that intro earned you a subscription

@angiemycine6509 4 года назад

It made me think that the whole video was going to be a song lol. Very interesting nonethless

@marioadiez 4 года назад

I'am not suscribed for the plots, but for the music!

@setsu2221 4 года назад

That intro hit me hard xD

@robertopizziol7459 4 года назад

I was waiting for the "BAAM" all video long, got just a couple of great "HOORAY!". Thank you for the awesome channel Josh!

@statquest 4 года назад

You made me laugh. :)

@kevon217 2 года назад

Couldn’t have asked for more clear explanation, thanks!

@statquest 2 года назад

Glad to help!

@aashishshrivastav9531 6 лет назад

🤔🤔🤔🤔🤔 well I thought that q-q plot was difficult but thanks to you I got it now. thanks and keep it up!!!

@jameswhitaker4357 10 месяцев назад

Not gonna lie, stats is my super weak spot. You've helped me a lot in my Data Models course and interpreting my results. +1

@statquest 10 месяцев назад

Happy to help!

@jameswhitaker4357 10 месяцев назад

@@statquest Thank you! I'm just kicking myself for not taking more stats courses at this point!

@statquest 10 месяцев назад

@@jameswhitaker4357 My stats courses were all pretty terrible, so you never really know what you're going to get. I had to teach myself statistics, and these videos are how I taught myself.

@jameswhitaker4357 10 месяцев назад

@@statquest That's what I'm going through right now! I've been using your videos and a "Intro to Statistical Learning with Applications in R" textbook which has helped a lot. I think when I saw terms like "heteroscedasticity" or the crazy formulas I would get scared and put off the studying, until I took a course that required knowing it LOL. And luckily most of these statistical tests and concepts are now pretty easy to perform in programming. Cheers!

@statquest 10 месяцев назад

@@jameswhitaker4357 I actually wrote a little about heteroscedasticity. Maybe I should record it.

@alisalehi4980 6 лет назад

I really appreciate from your very easy way explanation. I faced with so difficult and rough terminologies that I could not even understand the meaning of them.

@josevaldes7493 2 года назад

Triple BAMM! Serious man your channel is pure art. Thanks

@statquest 2 года назад

Thank you!

@asmaulhosnanisha4657 3 года назад

I could have better grades if i had faculties like you...thank you Josh!!

@statquest 3 года назад

Thanks!

@kusocm 4 года назад

Best intro song, it can be used as a 'mnemonic' for what QQ plots are used for =)

@navatagames 2 года назад

Nice video. Explained everything in just under 7 mins. Awesome. 😄👍👍

@statquest 2 года назад

bam!

@navatagames 2 года назад

@@statquest bam indeed. 😁

@Clarin3t1 2 года назад

You had my like at the beginning with the jingle. Thanks for explaining this so well!!

@statquest 2 года назад

Glad you liked it!

@gianlucalepiscopia3123 2 года назад

This is very very cool, more likely to learn on RU-vid than in a classroom. Grazie

@statquest 2 года назад

Glad it was helpful!

@jorenmaes498 2 месяца назад

I just noticed when you said "please subscribe" at the end of the video, the subscribe button lit up:)

@statquest 2 месяца назад

bam! :)

@heplaysguitar1090 3 года назад

Explained like a pro. Tripple BAM!!!

@statquest 3 года назад

Thank you! :)

@Dekike2 5 лет назад

Hi!!! Great video!!!! It was very helpful to understand Q-Q Plots!!!! But just one question, how do you calculate the quantiles for your dataset?? I mean, the first observation of your dataset is 0.6, but I don't understand why, since the first observation leaves 0 observations on one of its sides. Should the quantile be 0? In the video where you explain how to calculate quantiles, you explained that the quantile for each observation is calculated dividing the number of observations that this value leaves below between the total number of observations... So, for the first point... 0/15 = 0. Why 0.6??

@statquest 5 лет назад

I think I see the confusion here. The x and y-axes on the QQ-plot (on the right side) are labeled "Normal Quantiles" and "Data Quantiles". This is a little misleading - what we are plotting are the values at each quantile, not the quantile name itself. So if the first quantile is called "quantile 0", but it represents -1.5 in the normal distribution and 0.6 in the data, then we draw a dot at -1.5, 0.6 to represent the first quantile. Does that make sense?

@Dekike2 5 лет назад

@@statquest Perfectly. I understood this after watching some more videos. I would suggest you to clarify this if you make a new version!! As I already told you, congratulations for your videos and of course, your quick reply!! You explain really well, and the videos are perfect (easy to follow and to understand). I'm doing my Ph.D and it is really helpful people like you. Thanks a lot.

@Fan-fb4tz 2 года назад

@@statquest Thank you very much for all your videos! They help me a lot. Just a follow-up question on this: how can we decide where to start as smallest quantile value in the theoretical distribution? Like you mentioned, "quantile 0" value in the sample distribution is 0.6, but how can it represent -1.5 in the normal distribution? My confusion is normal distribution doesn't technically have "quantile 0" value because it's infinity on the both tails.

@statquest 2 года назад

@@Fan-fb4tz On the left side the first quantile is defined for the first point of 15 data points, meaning that 1/15 of the data is equal to or less than that point. Thus, we find the corresponding point on the normal curve such that 1/15th of the area under the curve is to the left of it.

@yangyu5525 7 месяцев назад

@@statquest strictly speaking, the 15 lines(15 data points) divide the whole data into 16 equal groups or parts，So corresponding to normal distribution should be divided into 16 bins so that every bin has the same probability of 1/16 ,right?

@alecvan7143 4 года назад

Best intro by far so far

@statquest 4 года назад

Hooray!!!! :)

@user-or7ji5hv8y 4 года назад

great video. a video on the intuition on why q-q plot works might be interesting.

@statquest 4 года назад

I'll keep that in mind.

@matavalamuttej841 3 года назад

You made it very clear man !!! Great doing

@statquest 3 года назад

Glad to hear that!

@Danielbassist13 Год назад

phenomenal explanation and really cool intro music man!

@statquest Год назад

Thanks!

@dominicj7977 5 лет назад

Can you do a video on normality tests like shapiro wilk and anderson darling? If not anytime soon, can you share link to some good materials?

@robertocannella1881 2 года назад

Thanks for all the videos! Great music BTW. Also I'm looking forward to rockin' my new SQ hoodie!

@statquest 2 года назад

TRIPLE BAM! Thank you for your support!

@bin4ry_d3struct0r Год назад

I always wondered how statisticians choose a distribution to which to fit the data when eyeballing it is insufficient. Now I know the answer: QQ-plots. Thank you for this!

@statquest Год назад

bam! :)

@lashlarue7924 2 года назад

It's a party time with Josh Starmer and his quantiles! 😆🤘 Party on, Wayne!

@statquest 2 года назад

@pradiptithakur3655 4 года назад

Awesome video. Explained so clearly. Really helped me a lot!

@statquest 4 года назад

Hooray! :)

@biancafeitoza4030 2 месяца назад

Thank you for your help! Greetings from Brazil.

@statquest 2 месяца назад

Muito obrigado! :)

@vlakrunn Год назад

You simply saved my life

@statquest Год назад

Bam!

@padraiggluck5633 4 года назад

Really excellent presentation, Josh. ⭐️

@statquest 4 года назад

Thank you! :)

@user-hv9wx5kd9u 10 месяцев назад

Best Explanation ever!!! 🎉🎉🎉

@statquest 10 месяцев назад

Thanks!

@sarrae100 2 года назад

How beautiful and simple is that explaination 🥳

@statquest 2 года назад

Thank you!

@sirisudweeks9334 5 лет назад

very nicely explained. it was a tricky concept until this video! thanks!

@statquest 5 лет назад

Hooray! I'm glad the video helped. :)

@tawkameyu 4 года назад

It just saved me, the person who did this => you're the best

@statquest 4 года назад

Thank you! :)

@fkhan4504 6 лет назад

Crytal clear explanation

@statquest 6 лет назад

Thanks! :)

@anlerkul2988 2 года назад

Dear Josh, thank you for this informative video. I have a one question: we are dividing the gene expression data into fifteen and also dividing the normal curve into fifteen. So for example lets take the 3rd data point: 1.9. It is the 3/15 percentile which is 0.2 So in the normal curve when we look at the z-table it should have been -0.84. In your calculations, I am observing that it is -0.89 which is actually 0.1867 percentile in the z-table. Am I missing something?

@statquest 2 года назад

If we want 15 equal sized portions of the normal curve, then, we actually need 16 slices, the extra slice is at positive infinity. This makes it so that the area under the curve to the left of the first vertical bar in the graph is equal to the area under the curve between the 1st and 2nd vertical bar which is equal to the area under the curve between the 2nd and 3rd vertical bar (etc. etc. etc.) So when we do 3/16, we get 0.1875, and the normal quantile for that is -0.8871

@anlerkul2988 2 года назад

@@statquest thank you very much! Now it seems clear

@ehsans2135 3 года назад

so clear, so good , so nuce thank you , Josh

@statquest 3 года назад

Thanks!

@ThuyPham-yu7cw 4 года назад

wow, now I can clearly understand it ! thanks alot !

@statquest 4 года назад

Hooray!!! :)

@BossCock17 6 лет назад

du hast zerfetzt bro, danke

@statquest 6 лет назад

Bitte!!!

@81-jdowlwp 5 лет назад

@@statquest quick question to 2:04 in your video: if we have 15 data points and we divide the dataset into 15 quantiles, then shouldn't the smallest quantile be 0.06666 so around 0.07? because in your video you are saying that it is 0.7, which would mean, that 70% of all data is covered by just one datapoint. Thank you for your video :)

@vineetkaur1667 4 месяца назад

Very well explained !

@statquest 4 месяца назад

thank you!

@Shuffellove 5 лет назад

i love statquest!

@statquest 5 лет назад

Hooray! :)

@rionaalmeida7376 3 года назад

The name of the channel should be "Dumbing Down Probability for Dummies". I don't know whether I like the intro song better or that simple explanation.

@statquest 3 года назад

Personally, I like to think that rather than dumbing down the material, I bring people up so that they can understand the tools and techniques in data analysis. I think "dumbing down" suggests watering down the content, as if I am presenting a simplified version of how statistics really works. That's not what happens in my videos. This is the real deal. It's just explained in a way that is relatively easy to understand and that brings people up.

@rionaalmeida7376 3 года назад

@@statquest Understood. The way you teach not only makes it comprehensible but also ensures it sticks to the head!

@MasterMan2015 6 лет назад

Step 3 is not very clear. How do you put the lines on the normal distribution. How do you start putting the lines ? and How about the distance between each two lines ?

@statquest 6 лет назад

The quantiles for the normal distribution divide it so that the area under the curve between two lines is equal for all of the divisions. Since the normal distribution isn't as tall on the edges, there is more space between lines then in the middle, where the distribution is tall. Thus, the spacing between lines makes the area under the curve between the middle two lines is the same as the area under the curve between lines on the edges.

@MasterMan2015 6 лет назад

Thanks! It is easy to see that in the case of Uniform distribution. How about the starting point ? I think it's randomly that you started by -1.5 but I can start from -2 or -1 or ..

@statquest 6 лет назад

The starting point is defined by the need for each unit between lines to have the same exact area under the curve. To understand what this means, imagine you had to divide a normal distribution with a single line so that 50% of the area under the curve was on the left side of the line and 50% of the area under the curve was on the right side of the line. Where would you draw that line? Well, there is only one choice - right down the middle of the normal curve. If you drew it anywhere else, there would either be more area under the curve on the left side or the right side. Now imagine you had to divide the area under the curve into 4 equal amounts. Again, there is is only one option - you put a line in middle, and then you put another line so that the area under the curve on the left side is divided in half and then a third line so that the area under the curve on the left side is divided in half. Any other locations for those lines will result in the areas under the curve not being equal to each other. Thus, in this example, we have no choice about where to put the lines - they have to be put in the one configuration that makes the area under the curve between every pair of lines equal.

@MasterMan2015 6 лет назад

Perfect! got it!

@statquest 6 лет назад

Hooray!!! :)

@guillemvia6813 5 лет назад

Awesomely explained! Good job!

@statquest 5 лет назад

Thank you! :)

@sumayyakamal8857 3 года назад

THANK YOU!!!!!!

@statquest 3 года назад

Thanks!

@response2u 2 года назад

Legendary explanation! Fantastic!

@statquest 2 года назад

Thank you!

@julesd3115 2 года назад

Awesome video - thank you SO much for saving my sanity.

@statquest 2 года назад

Thanks!

@geogeo14000 3 года назад

And again, thank you for another amazing video ! A little question : most of the points have to fit in the straight line for the data to be considered as normally distributed and at 4:15 you said it is not the case. Althought the intersection points are really close to the line, it does not matter, most of the point have to be strictly ON the line, right ? The fact that other intersection points are close or far from the line does not give any relevant information ?

@statquest 3 года назад

I'm not sure I understand your question. For more details on how to interpret QQ-plots, see: stats.stackexchange.com/questions/101274/how-to-interpret-a-qq-plot

@geogeo14000 3 года назад

@@statquest ok thank you !

@tymothylim6550 4 года назад

Thank you for the video! It was short and easy to understand :)

@statquest 4 года назад

Thanks! :)

@fantube7511 2 года назад

Best intro 🔥

@statquest 2 года назад

bam!

@robertb-l5422 5 лет назад

Very well explained, thanks so much

@alanpdrv 2 года назад

Thanks for this! Finally I understand

@statquest 2 года назад

Hooray!

@bingxinyan8103 2 года назад

Helpful and easy to undderstand.

@statquest 2 года назад

Thanks!

@Brockdorf 3 года назад

the song alone is worth it

@statquest 3 года назад

bam! :)

@ananyaagarwal7108 2 года назад

Hi Josh, Amazing Video there :) Just want to understand the intuition behind the working of QQ plots ? Is it the fact that quantiles of every normal distribution are just scaled up values of a standard normal distribution and that is why we expect a straight line ?

@statquest 2 года назад

Pretty much

@ananyaagarwal7108 2 года назад

@@statquest Thanks for the response ;) Would really appreciate if you could make something on the same or share some content that could explain the intuition behind QQ Plots.

@Cozmaus 10 месяцев назад

Actuary studies is something else bro

@statquest 10 месяцев назад

Noted!

@yairshalev2674 4 года назад

For all the other fans, the chords are Bm, D. You're welcome :)

@statquest 4 года назад

That's awesome! :)

@jalbertomendivil 2 года назад

I know it may sound dumb but i just got it when i understood that theoretical quantiles were the quantiles of a normal standard distribution or Z-value.

@statquest 2 года назад

bam! :)

@florence2523 23 дня назад

thank you very much for this video. Please a have a few questions to ask. 1. From your previous video on quantile and percentile the first line was 0% quantile, why does it have a value of 0.6 in this video? 2. How are you getting the values for the x- axis, and why did it have to range from -2 to 2? Thank you

@statquest 23 дня назад

1) In this video we are plotting the actual values on the y-axis, rather than their quantiles. 2) The values come from a standard normal distribution (a "standard" normal distribution is a normal distribution with mean = 0 and standard deviation = 1). There are excel functions that will generate the x-axis coordinates from a standard normal distribution for you.

@user-wx4vf5gj2f 4 года назад

Thanks for saving my life

@statquest 4 года назад

Hooray! :)

@joaovasconcelos5360 2 года назад

Your videos are awesome, thank you so much!

@statquest 2 года назад

Glad you like them!

@piotrszocik7775 4 года назад

Great explanation, have a nice day :)

@thechickendiet 4 года назад

very clear with great examples!

@statquest 4 года назад

Thanks!

@alexgimeno170 4 года назад

Understand it now - thank you!

@statquest 4 года назад

Hooray! :)

@MeWatchingYouTubeVideos Год назад

How helpful! Thanks a lot for your amazing videos

@statquest Год назад

Thanks!

@urjaswitayadav3188 6 лет назад

Thanks for the great explanation as always! So QQ is just a way to plot and visualize the similarity of two distributions? Are there any other scenarios when these can be used? Thanks!!

@TheAbhimait 4 года назад

QQ is mostly used to check tail conditions. Density plots and cumulative plots are the best way to check distribution symmetry.

@joerich10 6 лет назад

is there a statistical test we can do to determine how far away the dots are allowed to deviate, rather than just eyeballing it? Or is eyeballing good enough? I.e. a stat test that could say 'the chance of these 2 distributions being the same is less than X%

@statquest 6 лет назад

The "K-S Test" is what you want. However, it is very strict and tends to reject the null too easily. It's one of the few statistical tests where a large p-value (suggesting no difference) is more convincing than a small one. en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test

@kissapeles 2 месяца назад

@@statquest How were the lines drawn? Least Squares? Maybe doing R^2 calculations can provide an idea? Still trying to grasp my statistics a bit better :( :)

@khanghuy7384 2 месяца назад

u saved my life

@statquest 2 месяца назад

bam! :)

@Shred427 11 месяцев назад

such an awesome video, thanks!

@statquest 11 месяцев назад

Glad you liked it!

@paulpaschert6215 5 лет назад

QQ is really handy

@user-ii5ch8nw6s 6 лет назад

It's so clear! Thanks a lot for your video.

@hebaebrahem7893 5 лет назад

Your videos are cool and concise , thank you .

@aj_actuarial_ca Год назад

Thanks a lot for the wonderful explanation!

@statquest Год назад

Thank you!

@richardanderton 5 лет назад

Josh, Great video... very helpful. It looks like you might have a slight error when comparing the 2 dataset distributions however. I could be wrong but I think your second plot is incorrect on the chart.

@statquest 5 лет назад

I think I know what you're talking about. The second point should be at 5.1 on the x-axis but is only at 4.1. Is that it? That's a typo.

@statquest 5 лет назад

By the way, the long term plan is to correct/update these videos. Just like textbooks have "new and revised editions", I'd like to have new and revised additions of these videos - so your feedback is helpful and appreciated. I hope that once the channel grows, youtube will give me some options for how to release revised videos - right now I have no options, but I'm also relatively small potatoes. So I can't fix the video right now, but one day I will.

@richardanderton 5 лет назад

@@statquest Yes that's it.

@richardanderton 5 лет назад

@@statquest No problem. Your viewers would certainly appreciate your easy to understand videos. I just wanted to check I understood and maybe other users will find the note in the comments useful.

@schiu867 2 года назад

It helps a lot. Thanks!

@statquest 2 года назад

Glad it helped!

@xoda345 2 года назад

Two questions: 1. How does having more points at the middle make the histograms narrower and the opposite at the ends? If the histograms have more width doesn't that mean that more data points can be placed in that histogram ? 2. the last example you took 4 data points and said that the distribution has 4 quartiles. Did you say such because there are 4 points ?

@statquest 2 года назад

1. You might want to review the concept of the normal distribution. ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-rzFX5NWojp0.html The hight of the curve indicates the likelihood of observing a point there. So the wider parts have lower likelihoods of observing points and the taller have a higher likelihood. To make each region have an equal probability of observing a point, we have to have wider regions where the likelihoods are lower and narrower regions where the likelihoods are higher. 2. yes.

@12copablo 3 года назад

Hoorray! Thx for the video :)

@statquest 3 года назад

BAM! :)

@arneoosterlinck7590 5 лет назад

Great explanation, thanks!

@statquest 5 лет назад

Thank you! :)

@pfever 4 года назад

4:35 I think there is an extra quantile drawn on the Uniform distribution

@statquest 4 года назад

You are correct. Thanks for spotting that.

@raulbeienheimer 2 года назад

I wish I could characterize this video with infinite likes 👍

@statquest 2 года назад

BAM! :)

@user-id1rf6gt6h 4 года назад

Helped a lot! Thank you :D

@statquest 4 года назад

Hooray! :)

@hanhan2360 4 года назад

QBUS?

@richardbarton9076 5 лет назад

This was super helpful!

@statquest 5 лет назад

Thank you! :)

@stevehe5713 5 лет назад

one confusion is drawing the straightline. you should draw the data quantiles's line plot

@statquest 5 лет назад

That's true, and this is something I may have misstated in another comment. I think the standard thing to do is draw line that connects the first and third quantiles.

@jacksonh634 5 лет назад

@@statquest Hi Josh, what do the first and third quartiles refer to specifically in terms of the (x,y) coordinates? Are you referring to the theoretical quantiles or the sample quantiles (or both) for the (x,y) coordinates the line?

@statquest 5 лет назад

@@jacksonh634 I believe it depends on what you have on the x and y axes. If you have sample quantiles on the y, and theoretical quantiles on the x, then you mix. If you have sample quantiles on the x and y, then you use sample quantiles for both.

@pankajverma3842 3 года назад

what a nice lecture!

@statquest 3 года назад

Thanks! :)

@danieldavieau1517 4 года назад

Josh Starmer = Awesomeness

@statquest 4 года назад

Thank you! :)

@IGragon 2 года назад

What a great song :)

@statquest 2 года назад

@RamanGatekeeper 5 лет назад

Hi, thanks for video but could you please add in the description wtf is gene expression and what mean x and y on the 0:37 graph. For the time being I see 15 data points and no idea why they are shown in this way, thank you. Maybe some simpler example instead of gene expression?

@statquest 5 лет назад

OK. I added this to the description: NOTE: The data in this video are measures of gene expression. If "gene expression" doesn't mean anything to you, just imagine that the data represents how tall a bunch of people are, or how much they weigh. Then consider the y-axis to be the height or weight of the people, and the x-axis just represents all of the data you collected on a single day. In this case, all of the data were collected on the same day, so they form a single column.

@RamanGatekeeper 5 лет назад

@@statquest thanks for your effort!

@brayanmurillo4427 Год назад

thanks for the explanation, can you clarify this please?: if we have 15 quantiles, then I thought you should plot 14 red lines in the normal distribution and the 15th line should reside in +infinite. and a little question: is the straight line generated by linear regression?

@statquest Год назад

Plotting a line at infinity would be hard to do and you can fit the line with regression.

@alifia276 3 года назад

Thank you! Awesome explanation

@statquest 3 года назад

Thank you! :)

@bharathkumar5870 3 года назад

i have a doubt...why to use this method,instead just plot the points and see if it forms a bell curve....correct me

@statquest 3 года назад

@@bharathkumar5870 I'm not sure I understand your question. Are you asking, "why don't we just create a histogram with the data and see if the histogram looks like a normal distribution"? If so, histograms can be very tricky in terms of selecting the correct bin size. In contrast, with a q-q plot we don't have to worry about optimizing a bin size or anything else.

@bharathkumar5870 3 года назад

@@statquest thank you sir ..u cleared my doubt. Different bins give different distributions😀

@km2052 6 лет назад

thanks , awesone , this is useful in measuring gene expression effect

@wildgorilla1205 Месяц назад

Thanks man!

@statquest Месяц назад

No problem!

@wanhope3660 6 лет назад

Sweet, its not that difficult to grasp anymore! Thanks

@younasha8686 Год назад

世界上为什么会有你这么有趣的人

@statquest Год назад

Thanks!

@averkij 4 года назад

Thank you, Josh.

@statquest 4 года назад

Thanks! :)

@alexandergarcia-yo6kw 3 года назад

you are the best!

@statquest 3 года назад

Thanks! :)

@Yambaization 5 лет назад

5:30 I am confused... I thought that quartiles are three (not four) values, which divide the dataset into four equal numbers of data points. In your example you say that four data points are quartiles? 🤔

@statquest 5 лет назад

Oops. That's a mistake. Quartiles divide the data into 4 parts.

@robderon 3 года назад

precious help, thank you

@statquest 3 года назад

Thanks!

@apoostle 2 года назад

Thanks! It helps.

@statquest 2 года назад

Wow!!! Thank you very much for your support! BAM! :)

@Atomflinga 5 лет назад

What's the approach for determining which distribution has the best fit for the data? Would the r-squared of the data against the straight line be a suitable measure for how well the distribution describes the data?

@statquest 5 лет назад

This is a good question, and, to be honest, I'm not sure what the answer is. I like your idea, but it may oversimplify the problem. i.e. you could get a high R^squared value, but still have some real obvious problems if you looked at it visually.

@gayathrikurada3315 4 года назад

Hi Josh, can we use percentiles in place of quantiles to plot QQ plot ? If so, in case of percentiles we can only have upto hundred percentile no matter how big our data is then how to have a definitive answer whether or not the 2 datasets have similar distributions as mention in the video at 6:30 ?

@statquest 4 года назад

The terms "quantiles" and "percentiles" are often used interchangeably, and in this case you can swap out quantiles for percentiles. And you can have as many percentiles as you want - however, the largest percentile is always 100. For example, you could have the 0.5 percentile, or the 1.23 percentile.

@gayathrikurada3315 4 года назад

@@statquest Thanks Josh.