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Standard Error of the Estimate used in Regression Analysis (Mean Square Error)
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- Опубліковано 4 лют 2012
- An example of how to calculate the standard error of the estimate (Mean Square Error) used in simple linear regression analysis. This typically taught in statistics.
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this is 100000 times more useful than attending lectures
You made this so simple, even non mathematical background students can understand this. You have no idea how your videos helping me in my career. I can't thank you enough for this
x2
Great question! It is "degrees of freedom" or df. The reason we use 2 is because we have 2 variables (x and y) in this case. If we had more than 2 variables like x, y, and z the df would be n-3.
In an earlier video I show how the regression line is forced through the point where the mean of x and y intersect. These "2" points are not included in the "average of the errors" because the error at these 2 points is 0.
I need to create a video on d.f. for sure.
Awesome!
Thank you for this awesome video! I feel we should divide R2 by (n-1) and divide the standard-error-of-the-estimate by n.
R2 uses an average y value from the sample. However, this average y value in the sample might not be the same as the average y value in the whole dataset. Thus, we have to divide R2 by (n-1). Here is the detailed explanation: stats.stackexchange.com/a/87422/318006
For the standard-error-of-the-estimate, since we do not use the average value from the sample, it might not be necessary to divide it by (n-1) or (n-2)
@statisticsfun Can you please explain what's the difference between RMSE and Residual standard error (RSE) ?? We are diving by N for RMSE which gives baised estimate of deviation due to which we are dividing by degrees of freedom . Is that correct ? If yes , Is it better to use RSE in all cases instead of RMSE ?
You are very welcome (remember this channel is call Statisticsfun). Make sure you like MyBookSucks on Facebook (see video description for link). This will help others find the educational videos.
Thank you for the feedback, much appreciated.
Make sure you like MyBookSucks on FaceBook (see link in video description). This will help other students find the educational videos and help them "see" too :).
Every stat student should have to watch something like this so they can understand why they shouldn't be intimidated learning this stuff. You did an excellent job with this series of videos, thank you so much.
The link to the playlist on regression is in the video description (of this video). UA-cam does not allow me to add links in comments.
Bridgette, yes I do. This video is part of a larger playlist. If will see a link to the entire playlist in the video description. In the second video I discuss how to calculate all the coefficients.
I would appreciate you sharing these with your friends and colleagues. This helps me get the word out about the educational videos.
If you have not done so, make sure you like MyBookSucks on FaceBook (see link in video description). This will help me spread the word about the free videos.
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Make sure you give me some love on FaceBook and Like MyBookSucks (see link in video description). This will help others find the educational videos and fall in love with me too.
Thank you and I appreciate the feedback. Make sure you like MyBookSucks on Facebook (see link in video description). This will help others find the educational videos.
May I just say sir that you are marvellous at this. Your explanation style, calm voice, uncluttered wording, steady pace to let it sink in, all works so well for me, and it seems from below, others too. Well done.
this guy explains in 3 minutes what a teacher can't in 1.5 hours. Thank you!
Awesome explanation. My instructor was impossible to follow but you made this very clear and showed how simple it really is. I learned more in the first 3 minutes of this video than I did in the whole hour of my class.
The short answer is this is degrees of freedom, the reason 2 is used is because there are two variables estimated. The y intercept and the coefficient of the independent variable or slope of line. As the number of observations increase this n-2 adjustment becomes less and less important.
Make sure you like MyBookSucks on FaceBook (see link in video description). This will help others find the educational videos.
Very good to hear. Make sure you like MyBookSucks on FaceBook (see link in video description). This will help me spread the word about the videos.
After watching your videos I'm finally learning how to do this and pass my class. Thanks so much!
Of about four videos I tried to watch on this subject yours was by far the most well done and easy to understand - thank you!
Thank you for making this. 8+ years later and still helping others (like myself) understand stats
yes!! esp now that we have ol class
Wow, the simplest and most straightforward way to explain Regression. This is better than everything I've read and seen.
Thanks for pointing that out. I added a link in the video description to the playlist. Hopefully the playlist can give you more insight into the nature of the formula's especially the first video.
I appreciate your feedback too because the pace and detail of the videos is always a struggle for me. Good luck on your classes too.
Such an amazing Simplification..I am learning machine learning and I have to start building machine models but I didn't how regression line was being calculated. You made it so clear for me. Kudos to you brother.
Thank my professor Dr. David, you make the statistic more fun for me.
I never forget your amazing teaching at Avila University
I usually don't make comments on any videos but I must say you are an amazing teacher, Stats never made sense to me and you have done an exceptional job!
@Norfeldt Very good question. The reason is we are estimating two variables b1 and b0. The larger the sample size the less impact this subtracting two will have.
That is what I am talking about! "Party More Study Less!!!!"
Why -2 in denominator i.e. n-2, why not some else number
@@priyandumbajpayee4398 "Great question! It is "degrees of freedom" or df. The reason we use 2 is because we have 2 variables (x and y) in this case. If we had more than 2 variables like x, y, and z the df would be n-3."
Thank You so much, I've been struggling with this chapter, and getting behind in class, because, I couldn't move one until I figured this out. With your awesome video, I got it!!!!!! Excellent Teacher!!!!
The Standard Error of the Slope is what you are trying to find. You take the standard error of the estimate divided by sum of the differences of the x values squared.
Sb1=Syx/Square Root(SSX)
Im at working trying to dig this stuff out of my brain from my old statistics class no one has been able to help. 1:30 into this video and I got it down. Thank you
@skibumanne Typically both the standard error of the estimate and R^2 are used. Of course if R^2 = 1, standard error of the estimate (SE) would be 0. They both tell us how good of fit, but R^2 tells us a bit more. For example an R^2 of .45 means the regression explains about 45% can be explained by the linear relationship. You would use the SE if you were going to show the upper and lower bounds (or interval of the regression line).
Good to know about the m and b too -- thanks.
great video, I'm been searching all over trying to figure out all plots on a straight line and it's standard error of estimate. You didn't even tell us and I figured it out :) Thanks so much.
Thank you for the tutorials. My text book truly does suck and your tutorials are saving me in my online course. Very clear, concise and simple. This non-math minded person greatly appreciates them.
Megan Magnuson Megan, thank you so much for taking the time to write me -- it is much appreciated. If you get a chance like, share the videos. It helps get the word out. Liking our FB page helps too. www.FaceBook.Com/PartyMoreStudyLess
You will see R2 (r squared) most of the time and in fact there is a strong relationship between R2 (r squared) and standard error of the estimate.
This is how people will make this. Extremly instrucitve and good. My friends will know about this vids. Thanks man.
@Norfeldt The b0 is the y intercept and b1 is the slope of the line. If you had three variables (x, y, and z), then you would have n-3. And Yes, it does have to do with the degrees of freedom.
Degrees of freedom are the number of variables that are "free to vary." As samples sizes get large there is less of an impact of this goofy denominator.
Thanks for clarifying the difference between R squared and Mean Squared Error. Great video!
Yes I believe the MMSE (minimum mean square estimate) is the same thing as least squares used in linear regression.
The color coding and animations are really helpful!
these tutorials make my day.It helps me understand statistics in a easy way.thanks a lot
The standard error of estimate is very similar to the standard deviation. So comparing standard error the estimate is similar to comparing the standard deviation and the mean. In the case of a regression you have a lot of means (or actual values).
You would want to compare 14.220 to the actual predicated value at that point as well. Keep in mind the standard error of the estimate is the "average error" not the specific error rate.
Hope that helps.
Thanks Allah you were sent to help us . Thank you very much it is much easier with you . never stop making outstanding videos and we will never stop liking it
@Norfeldt That is good information for me to know, btw, what country do you live in?
Thank you!
Sir what is r2 after 3.20 what u did??kindly explain
best video for understanding, before this I could not understand for one semester now it is clear in 10 minutes
I know! I was just joking around as well. I do appreciate the comment and I understood it perfectly.
you just saved my semester. Its like you just took the burden away
Hi. This is very good video, thank you for that. Can i ask a question.
In R square, the rate should lie between 0 and 1. The more value R square is, the better. How about standard error of estimate? is it the same?
Thanks
lesser the standard error the better it is
Party more study less is what 'm doing because of your videos! Inspiring!!! :) Ty
Thanks for pointing that out. I had a typo in the link that I have corrected now.
These tutorials make statistics easier to handle, thanks very much for the time to share your knowledge and skills. :)
***** You are very welcome. Good luck in your studies too.
@@statisticsfun negative values how to avoid sir in linear regression
Very good video; easy to understand and takes step by step / slow
short, clear and straightforward ! keep going mate !
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Wonderful that you are catching on to statistics.
Thank you! Simple and easy to understand example.
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BEST! I cannot thank you enough. I'll be studying in the next semester but for research paper, I needed to understand it well, and you take all the credits :D
Thank you, sir, for your gorgeous services. I will appreciate if you please make a video of the drawing and understanding the box plots that are ubiquitous in research articles.
You helped me explain standard error in my report for university thanks
Thanks so much for the clear video. What is the equation of the standard error of the mean? Thanks in advance.
Thank you and is always good to hear the videos are helpful and informative.
Better than 95% of my lectures in uni !!!!
@statisticsfun Thank you once again for the reply. In my country we normally use "a" as the slope and "b" as the interception which is why i didn't recognized it. I'm all set with your answer and looking forward to your next video :-)
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Very good presentation - clear and simplified. Thank you.
Great to hear and thanks for the video back. I have thought for sometime now that animation is the way to teach.
Thank you very much your tutorial is very useful and things become much easier when I watched your video series.
wow, thanks so much for your quick and understandable response! It's been my goal for years to learn this and understanding it is so very rewarding.
All you’re your videos are very helpful. I can grade these
as excellent.
learned way more watching your playlist than in have in actual class
Honestly, you teach better than most lecturers. You should have more subscribers 👍. Just one question, in what situation would you use the regression line?
I am really very thankful to you for such a very good explanation. I would recommend these lectures to others to clear their basics. Now I am going to see all videos with the relevant topics on youtube. Please let me know if you have more and other topics on logistics regression and other regression models used in financial and statistical modeling. Thanks and Regards - Chirag Patel
I would like to thank you for posting this video now i have a more clear view of what i have been doing
You speak clearly and slowly. Your slides are well augmented with illustrations. Thanks for this refresher.
Are Standard Error (SE) and Residual Standard Error (RSE) the same?
Very well explained, thanks so much.
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I really really appreciate your effort to make such a so useful and brilliant video like this, also other your videos. thank you so much, indeed.
MashAllah, your presentation in the form of a video is great and the manner of explanation is very explicit and crystal clear. Would request to have access to more these educational videos through links in response to this comment. I'm preparing for the CMA course and this video was just awesome!
+statisticsfun Could you please explain why we have to subtract 2 from number of observations 2:45. I know it's based on a derivation, but there should be reason behind it. Appreciate your help. Thank you!
Precise, to-the-point ! Loved it..
best explanation ever
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Very convincing...wish to see such teachers
wonderful! Happy Holidays!
Nice, short and practical
Your videos are really awesome and superb animation will explain deep-rooted concepts in just plain terms is an art
Thanks for your effort to make the animation part to easily understandable. Goodos to you
Thank you for this video! It was clear and easy to comprehend. Can you explain how the standard error of the estimate has a direct relationship with the SD of the criterion and an indirect relationship with validity?
this is super... sir... really fantastic... very clear and upto near perfection... where r square is 0.999999999
Thanks for the detailed explanation. May I ask why the degree of freedom for this is -2 ? like instead of -1 ?
Thanks a lot for this straightforward tutorial video!
best tutorial i've ever seen... thanks a lot.. what best method should i use if i need to find the relationship between two variables?
Thank you for video! It was very helpful but can you please also share link/playlist to Regression analysis from first using the least square method? Also what’s is scatter plot in regression??
Thank you Mr. Fun, you are a great teacher. God bless. Please make more videos and party more.
Thank you excellent job of explaining. Broke it down so it was easily understandable.
Thank you for the videos, these are great!
Very nice explanation sir...
If we were using a quadratic form instead of a linear one, should we put N-3 in the denominator since we would have to estimate 3 constants a, b and c?
Awesome video!! very very helpful.
Today is my quiz and you saved me sir
Excellent tutorials. Thank you for clear explanations !