Support StatQuest by buying my book The StatQuest Illustrated Guide to Machine Learning or a Study Guide or Merch!!! statquest.org/statquest-store/
@Att3mpt4 жыл бұрын
Thank you so much for these videos! I am currently in a Machine Learning class and my teacher does not explain it very well. And I love your optimism. thank you again!
@statquest3 жыл бұрын
Glad I could help!
@donleitoso93224 жыл бұрын
Hi, Josh! I'm an Economics student from Brazil. Even though it's only an undergraduate course (right after high school), it can be very demanding when it comes to Statistics, Econometrics and Machine Learning. Although I'm very new to this world, I can understand it pretty well with your help. Awesome videos!
@statquest4 жыл бұрын
Thank you very much and good luck with your courses! :)
@sureshkm4 жыл бұрын
I first like the video, then watch it! That's the quality of StatQuest.
@statquest4 жыл бұрын
BAM!
@BrianRisk6 жыл бұрын
This method has helped me make an amazing stock prediction algorithm! I'm a millionaire now!! THANKS StatQuest!!!
@statquest6 жыл бұрын
Hooray!!! :)
@exitudio4 жыл бұрын
million Bam!!!
@ThePrimeChannel7803 жыл бұрын
wow, how did you do it? any advice on how to get started?
@lmgpc4 жыл бұрын
Your intro made my day on this pandemic season. Thanks!
@statquest4 жыл бұрын
Hooray! :)
@auzaluis3 жыл бұрын
that's the reason why I became a Patreon supporter, you have saved my life again!!!! And the cherry was the R functions explanations, I love this channel bro!
@statquest3 жыл бұрын
Thank you very much!!!! I really appreciate your support!
@qiyongchung69547 ай бұрын
I'm a masters student and this is helping me a lot with understanding the idea of LOESS. Thank you!
@statquest7 ай бұрын
Happy to help!
@pnhkaaaaa6 жыл бұрын
Great video! Recently I've been working with the "Moving Least Square" concept which is widely used in computer graphics, especially image deformation. This video helps me understand this concept in statistical field. Thank you so much.
@technolady2276 жыл бұрын
That was so helpful! You really break everything down to basics and let the learner soak it in.
@antoniocastan92292 жыл бұрын
thanks i was reviewing The Seasonal and Trend decomposition using Loess , your video is fantastic !
@statquest2 жыл бұрын
Thank you! :)
@sandpiperr5 жыл бұрын
Thank you! The first time I've ever seen loess explained clearly.
@statquest5 жыл бұрын
Hooray! I'm glad the video was helpful. :)
@Paboty4 жыл бұрын
Thank you so much for your intuitive explanation! I'm honestly impressed by how well you explained it. Love the intro jingle as well :)
@statquest4 жыл бұрын
Glad you enjoyed it!
@BigAsciiHappyStar6 ай бұрын
LOESS is a fine-grained sediment, as per Wikipedia or various Scrabble dictionaries if you are into that stuff. Your pronunciation is correct.
@statquest6 ай бұрын
Thanks!
@salemalgharbi88344 жыл бұрын
excellent way explaining this advance concept ... thanks Josh ...
@statquest4 жыл бұрын
Thank you! :)
@baharrezaei56372 жыл бұрын
Most common methods used for local regression are 1- LOESS (locally estimated scatterplot smoothing) 2- LOWESS (locally weighted scatterplot smoothing)
@statquest2 жыл бұрын
My understanding is that LOESS is a multivariable generalization of LOWESS.
@RealMcDudu3 жыл бұрын
Note that I think what you show is Robust LOESS (which also accounts for outliers, and gives the extra weight function for distance between y's and predicted y's). I think the LOWESS function in R does it by default, but the LOESS function (it's successor) doesn't do it by default. You have to set family="symmetric" in the LOESS to get it. Specifically these two models do the same: fit = loess(y~x, span=0.25, degree=1) fit2 = lowess(x, y, f=0.25, iter=0) And so does these: fit = loess(y~x, span=0.25, degree=1, family="symmetric") fit2 = lowess(x, y, f=0.25)
@statquest3 жыл бұрын
Noted
@ericleonhuertamanzanilla42625 жыл бұрын
Excellent explanation, clear as crystal. Thank you very much for sharing it.
@statquest5 жыл бұрын
Thanks! :)
@CainisUponUs7 жыл бұрын
man you somehow make statistics feel intuitive somehow O_o
@alanytics5 жыл бұрын
Cuz stat quest is cool
@govamurali23094 жыл бұрын
Bam!!!!
@nysnys31004 жыл бұрын
It's the intro
@christopherrichardson23982 жыл бұрын
Double, wobble bam! With some ham cooked with eggs and spam on a frying pan sprayed with Pam! Wham! Now we have green eggs, spam, and ham! Enjoy! Mr. Sam I am.
@ReesCatOphuls3 ай бұрын
Nice video. I now realise i made something similar with centre moving averages and different (linear/quadratic) best fit per point. As ever i suspected that a sign of a good idea, is that someone else has done it 10x better already.
@statquest3 ай бұрын
:)
@manuelargos2 ай бұрын
Gracias profesor. Usted es el mejor
@statquest2 ай бұрын
¡Muchas gracias!
@ClaudiaBiguetti4 жыл бұрын
I love this channel!!! Thanks for the videos!
@statquest4 жыл бұрын
Thank you! :)
@BrianRisk6 жыл бұрын
Dude! This is exactly what I need!
@statquest6 жыл бұрын
Sweeeeet!!!!
@posthocprior Жыл бұрын
This was a great explanation. Thanks.
@statquest Жыл бұрын
Glad it was helpful!
@yazadjabbarc6 жыл бұрын
Hello, thats so nice of you to teach us the Lowess fitting, I have a specific parameter which needs to be fitted. Yet i need to change the weighing relationship, can you help with some explanation or some material to study for it. Thankyou.
@콘충이4 жыл бұрын
How could this vid get dislike? this video is awesome
@statquest4 жыл бұрын
BAM!!! Thank you very much! :)
@jiweihe34132 жыл бұрын
Thanks for the clear explanation. Just wonder in the 2nd iteration, how are the two different sets of weights used together. Are they multiplied?
@statquest2 жыл бұрын
To be honest, I don't know the answer to that question off the top of my head.
@nelsonjma6 жыл бұрын
Subscribed, many thanks to the information and how you present it.
@statquest6 жыл бұрын
Hooray! :)
@statsdept94167 ай бұрын
Here is something I found on wikipedia: LOESS (LOcally Estimated Scatterplot Smoothing) and LOWESS (LOcally WEighted Scatterplot Smoothing)
@statquest7 ай бұрын
nice
@bu82917 жыл бұрын
Your videos are amazing! I can finally understand statistics :)
@ramanmurty14985 жыл бұрын
Could you please upload some videos clearly explaining Non linear methods such as polynomial regression,step function ,splines,Basis function & GAM . Thanks for making such awesome videos.👌
@statquest5 жыл бұрын
To quote the Wikipedia article on Polynomial Regression ( en.wikipedia.org/wiki/Polynomial_regression ): "Conveniently, [polynomial regression] models are all linear from the point of view of estimation, since the regression function is linear in terms of the unknown parameters β0, β1, .... Therefore, for least squares analysis, the computational and inferential problems of polynomial regression can be completely addressed using the techniques of multiple regression. This is done by treating x, x2, ... as being distinct independent variables in a multiple regression model." That means you can watch my videos on Linear Regression to learn about Polynomial Regression: kzbin.info/aero/PLblh5JKOoLUIzaEkCLIUxQFjPIlapw8nU
@davidgl1988peru4 жыл бұрын
Great video man! Thanks for sharing.
@statquest4 жыл бұрын
Thank you! :)
@thatipelli15 жыл бұрын
Thanks a lot for the succinct explanation!!
@statquest5 жыл бұрын
You're welcome! :)
@yildizkoca88785 ай бұрын
thanks for the great content!
@statquest5 ай бұрын
Thanks!
@kartikkamboj2955 жыл бұрын
Thanks ! Keep posting such videos🙏🏻. Regards :)
@jingying62476 жыл бұрын
Crystal clear!! Thank you so much!
@statquest6 жыл бұрын
Hooray! :)
@serafeiml10412 жыл бұрын
Great video.
@statquest2 жыл бұрын
Thanks!
@sau0024 жыл бұрын
Great explanation
@statquest4 жыл бұрын
Thanks! :)
@WalkwithSid6 жыл бұрын
Saved my Time !! Thank you very much
@statquest6 жыл бұрын
Hooray! :)
@winniewu85815 жыл бұрын
what will be a normal window size?default? or do we look at other characteristic to determine? what is the relationship between window size and fit?
@razzlfraz4 жыл бұрын
This is why I love R. It has all sorts of fun functions like this. For anyone who is playing with timeseries data, eg sensor data, lowess/loess are smoothing functions and are great for preprocessing and cleaning data. To find out more checkout: en.wikipedia.org/wiki/Smoothing
@anzar4203167 жыл бұрын
you are a champion. Great video
@SteveSolun6 жыл бұрын
Thank you for the great explanation of the LOWESS! Please explain the last iteration, after I have found the first new points you say that I have new weights, can you please explain in depth how do I use them for the next set of points?
@SteveSolun6 жыл бұрын
Thanks a lot for you deep explanation. I am learning Data Science and wonder if you are familiar with good statistics courses that will also show me why Normal Distribution has such formula, kernels and other topics like CI's and all I need for ML. Please advise.
@SteveSolun6 жыл бұрын
Would you be so kind to add boosting algorithms like xgboost? Your explanations are super simple and I would like to get an explanation from you how to use these algorithms.
@nievsbest4 жыл бұрын
Mindblowing.
@statquest4 жыл бұрын
bam!
@Ratchet20225 жыл бұрын
Thumbs up just for the intro.
@statquest5 жыл бұрын
Thanks! :)
@aligatorbhai6 жыл бұрын
Could you make a video about fractional polynomials and splines please?great videos.Thanks
@statquest6 жыл бұрын
I'll put that on the to-do list. :)
@Coucou19815 жыл бұрын
@@statquest pleaaaaaaase :)
@statquest5 жыл бұрын
@@Coucou1981 OK, I've bumped it up a spot on the to-do. :)
@paulinefaber9973 жыл бұрын
Maith an buchaill, StatQuest.
@statquest3 жыл бұрын
Thank you! :)
@random-ds4 жыл бұрын
Hello Josh, thanks for this video, it's awesome. I was just wondering if you had time to finally make a video about splines? Otherwise can you give me links of any documents that can help. I need especialy the part where we plot K+m graphs (K for knots and m for the order). Thanks in advance :)
@Y45HV1N3 жыл бұрын
Thanks so much for this!! Will we have more videos on curvilinear fitting? Do p values and inference work the same with curves ? In what way fiiting a curve is better than fitting a line?
@statquest3 жыл бұрын
Statistics (like p-values) are much harder to calculate for non-linear fits than linear fits.
@ranieri27003 жыл бұрын
The best theme song so far
@statquest3 жыл бұрын
Thanks!
@dinajankovic75566 жыл бұрын
Fantastic video! thanks a lot.
@leesweets41102 жыл бұрын
5:34 Did you forget to move the window? I am confused by how you choose your points and windows.
@statquest2 жыл бұрын
I may have forgotten to move the window. Sorry for the confusion.
@stefank42864 жыл бұрын
This was very helpful, but how do I get the actual regression function? Say I wanted to compute the approximated value for a x which is not a data point? In linear regression, you would get two parameters which define a line and use that line to compute the estimated y=f(x) for an x which is not a data point
@statquest4 жыл бұрын
I believe you just just connect each point that lowess creates with a straight line. So, for example, I have two consecutive x-axis values, x1, and x2, and corresponding lowess values y1, and y2, I just draw the line between (x1, y1) and (x2, y2) and if I want to make a prediction between x1, and x2, I plug the x value in to that equation for a line.
@stefank42864 жыл бұрын
@@statquest Thanks for your reply, I did some further reading and a possibilty is to just use the method for an x which is not a data point. I.e. given a x which is not a data point select the region of data points z_i you want to include into the regression and calculate the weights based off the distance between the x and the z_i. From this you can calculate the regressionweights (beta_0, beta_1) just like you would with a data point z_i. For further reference see www.itl.nist.gov/div898/handbook/pmd/section1/dep/dep144.htm
@statquest4 жыл бұрын
@@stefank4286 BAM! :)
@Bornleadr5 жыл бұрын
How are we choosing the new points here? I am talking about all the "Red Xs". Will we just consider a point on the regression line that is perpendicular to the focal point?
@statquest5 жыл бұрын
The Red Xs are chosen after fitting a line to the data using weighted least squares. If you would like to learn about least squares, check out this video: kzbin.info/www/bejne/hpKpgZWYa5t3rrM
@kavanshah95865 жыл бұрын
What kind of a curve do you fit to the new-new points? Does the curve have to pass through all the new-new points?
@콘충이4 жыл бұрын
Thank you so much!
@statquest4 жыл бұрын
Thanks! :)
@wenqianchang34716 жыл бұрын
Thx!pretty intuitive
@statquest6 жыл бұрын
You're welcome! :)
@cyrilbaudrillart96904 жыл бұрын
I am becoming a big StatQuest fan! Congrat for this great video. Just one question... According to what I understand the last points can be misleading because as we get more data, they will change. First we calculate last value of the curve, let's say corresponding to last point X(10), from points on the left, so X(5) to X(10). But as we get new points, let's say X(11), X(12), the value of the curve corresponding to X(10) will change because we now use points X(8) to X(12) in calculation. Am I correct? I hope this is clear... Thanks
@statquest4 жыл бұрын
Yes, as you add more data, the curve will change.
@2und2sind44 жыл бұрын
Thank you!
@statquest4 жыл бұрын
You're welcome!
@elrishiilustrado95923 жыл бұрын
Very well explained as always! Thanks! but i have a little question: when should we use this method? it looks like it overfits the data.
@statquest3 жыл бұрын
It can be useful when your data has some unknown shape and you want to find the "top" or "bottom" of the shape, rather than just the maximum or minimum values.
@Geologist9972 ай бұрын
I'm watching this video because loess and lowess are discussed in statistical method in water resources. Loess and lowess are pronounced the same.
@statquest2 ай бұрын
Thanks!
@Geologist9972 ай бұрын
@@statquest do you have a video showing implementation of loess and lowess in R?
@statquest2 ай бұрын
@@Geologist997 All I have is this code that demonstrates how to use those functions in R: github.com/StatQuest/lowess_loess_demo/blob/master/lowess_loess_demo.R
@aragaorenan7 жыл бұрын
Thank you for this!! :)
@vzinko Жыл бұрын
How can a non-parametric LOESS regression be used for extrapolation?
@statquest Жыл бұрын
Presumably once you have the curve you can use it to make predictions.
@josuevervideos5 жыл бұрын
Excellent!!!
@ramenmachinegun6 жыл бұрын
You just earned a sub! ☺
@statquest6 жыл бұрын
Thank you!!!! I really appreciate it! :)
@liamhoward22082 жыл бұрын
Hey Josh, Is Lowess and Loess the same thing as splines? If not and if you are taking suggestions, I think some content on interpolation or cubic and polynomial splines would be great!
@statquest2 жыл бұрын
I believe they are different from splines, and I'll keep those topics in mind.
@Doctorpopets3 жыл бұрын
So, are these regressions used for better trend visualization only, or there are any other reasons?
@statquest3 жыл бұрын
You could use them to make predictions with future data. That's essentially what a neural network does (however, it uses a different method to fit lines to the data).
@aifuli30886 жыл бұрын
Hi, at 5'34'' when the focal point changes to 6th, why doesn't the window move forward? Thank you!
@statquest6 жыл бұрын
The window, since it is set to "size = 5", contains the four closest points to the focal point. I explain this at 3:30.
@aifuli30886 жыл бұрын
I see. Thank you Josh!
@aifuli30886 жыл бұрын
I see. Thank you Josh!
@ananyajoshi25942 жыл бұрын
So, you obtained the new-new points based on the distance of the y axis between the new points and old points? is that so?
@statquest2 жыл бұрын
What time point, minutes and seconds, are you asking about?
@bibiworm3 жыл бұрын
I have a probably way too broad question here. I am reading ESL these days. And local linear regression and cubic smoothing spline are throwing around everywhere in the book. But I can't seem to have a good grasp of their relations and differences. What about them vs. neural network? Essentially, all of them are function approximators. Thank you very much!
@statquest3 жыл бұрын
Yes, they are all used for approximating non-linear functions. In theory Neural Nets are more flexible and can easily approximate more complicated non-linear surfaces. But I don't know all the details.
@bibiworm3 жыл бұрын
@@statquest thanks!
@sachinrana75546 ай бұрын
This lecture was little confusing, how did you make new parabola adding new weight?
@statquest6 ай бұрын
What time point, minutes and seconds, are you asking about?
@sachinrana75546 ай бұрын
@@statquest from 5:53 sec to 7:00, the question was how did you make that parabola a smooth parabola?
@statquest6 ай бұрын
@@sachinrana7554 Presumably you are asking for the formula that takes into account the weights, since I've already provided you with the intuition for what that formula does. Here's the formula: en.wikipedia.org/wiki/Local_regression
@spearchew2 жыл бұрын
great
@statquest2 жыл бұрын
Thanks!
@glaswasser4 жыл бұрын
I'm German so I always call it "Löss" :P
@statquest4 жыл бұрын
Awesome!
@razzlfraz4 жыл бұрын
Hey Josh great video! Any idea why loess (locally estimated scatterplot smoothing) and lowess (locally weighted scatterplot smoothing), are called a local regression ( en.wikipedia.org/wiki/Local_regression ) instead of a local autoregression?
@statquest4 жыл бұрын
I have no idea.
@govamurali23094 жыл бұрын
Please do hypothesis testing.
@statquest4 жыл бұрын
That is coming out in March.
@govamurali23094 жыл бұрын
@@statquest Thanks,I think only hypothesis testing and poisson distribution was not included besides that everything is included.
@JayMinuti6 жыл бұрын
HI! Great video. I want to fit a confidence interval band to my loess curve. Any advice on how I do this?
@JayMinuti6 жыл бұрын
Thank you! Easier than I thought :)
@statquest6 жыл бұрын
Hooray!!! That's great. :)
@navaneethansanthanam79703 жыл бұрын
Hi Josh, thanks for the video. Does LOWESS allow us to use set of external values to help with smoothing? Let's say I want to smooth some time-series data for one individual in a population. I'd like to do this while keeping in mind the overall population - so, when I smooth this individual, I'm doing it "along with" the population's mean time-series. Is something like this possible with LOWESS? Could I use the population's mean as a some kind of weight?
@statquest3 жыл бұрын
That's a good and interesting question. Unfortunately I don't know the answer to it.
@manuelenriquelunaalegria2744 жыл бұрын
Graciasss
@statquest4 жыл бұрын
De nada. :)
@iwtwb87 жыл бұрын
Rafael Irizarry pronounces it the same way in his videos: "low-ess"
@sau0022 жыл бұрын
Is Loess susceptible to outliers?
@statquest2 жыл бұрын
It depends on how wide the widow is.
@lilyha24704 жыл бұрын
Hello Josh, yes StatQuest is cool but do you have anything about weighted least squares?
@statquest4 жыл бұрын
Unfortunately I do not. :(
@lilyha24704 жыл бұрын
@@statquest please please make one, lol
@statquest4 жыл бұрын
@@lilyha2470 :)
@lilyha24704 жыл бұрын
@@statquest well, when I listen to your videos and go to the class I am way ahead of everyone even the teacher, hahahha
@statquest4 жыл бұрын
@@lilyha2470 That is totally awesome!!! :)
@somakkamos5 жыл бұрын
hmmm this is interesting... i thot curves are fit using the kernel trick every time. this is new information. are you aware of any python library that can perform the same trick. and how does this compare to kernel tricks?
@statquest5 жыл бұрын
I just did a quick google search and it seems that some people use the statsmodels package to do Lowess curves in python: www.statsmodels.org/dev/generated/statsmodels.nonparametric.smoothers_lowess.lowess.html
@aldoraine78482 жыл бұрын
How big should n be for this method?
@statquest2 жыл бұрын
That depends on your data.
@alvaromorales68284 жыл бұрын
Bam!!!
@statquest4 жыл бұрын
:)
@cw92492 жыл бұрын
i have a question: why does the lowess line look different near the end of my data when i change where my data ends?
@statquest2 жыл бұрын
Probably because there are different datapoints influencing it.
@cw92492 жыл бұрын
@@statquest thanks. is there anything that attempts to produce similar results, but only uses data points from the past at each step of the calculation?
@statquest2 жыл бұрын
@@cw9249 Not that I know of, but I'm no expert in this area....
@TwoandaHater4 жыл бұрын
I was always told it was pronounced "Low-Ess"
@statquest4 жыл бұрын
Cool.
@apoorvshrivastava35445 жыл бұрын
what is the meaning of weight here ?
@2und2sind44 жыл бұрын
A weight describes how much a certain factor is concidered in relation to the others. "Importance" is another way to put it.
@MarioBlancoVilchez3 ай бұрын
What is going on with closed captions in the first minute of the video haha
@statquest3 ай бұрын
I think you can turn them off.
@sau0024 жыл бұрын
What is the source of the data for the noisy curve at 8:10 ? Thanks.
@statquest4 жыл бұрын
x
@alexander1912972 жыл бұрын
Lois!
@statquest2 жыл бұрын
YES!
@alexander1912972 жыл бұрын
@@statquest That’s how my marketing statistics instructor used to pronounce it, and that’s how we still pronounce it at the agency where I’m currently working as an analyst! 😁