Finding the Standard Error of the Slope Estimator for a LSRL
HTML-код
- Опубликовано: 11 ноя 2019
- In this video, we calculate the standard error for the slop estimator in a least squares regression model. We perform all of the calculations by hand in this example.
This video is part of the content available for free at www.statsprofessor.com
been looking for this formula for days , LOL.. thanks so much dude you're a HERO! god bless!
You are the king of all kings, i don't know how much youtube pay you for your videos but is not enough.
Thank you 🙏 I appreciate the support!
Very helpful video, thank you!
Thank you very much.
Thank you. Concise and clear.
Glad it was helpful!
Very helpful, do you have a video on how to find the standard error of the y intercept
thnakssss.
I always use R, summary(lm(y~x)). It was interesting to find out what the math was.thanks!!
I understand that the result of the video calculation will be the same as the result of the lm, but is there any way to understand this calculation (calculation of the beta error) in the diagram(2Dplot)?
The standard error of the slope estimator tells us how the slope estimate will vary from sample to sample. In other words, a different sample would produce a different value for the slope because the measurements in your sample would be different. For every new sample of the same size taken from the same population, you would get a different value for the estimate of the slope. This quantity, the standard error lets us know how much variation exists in that estimate. Take the standard error of something like a sample proportion. If the sample proportion from one sample was 10%, is it reasonable to think a second sample of the same size from the same population could give you a sample proportion of 25%? Well, we can look at the standard error to know. If the standard error was 1.5%, then you might have the proportion be 11.5% or 13% or maybe 7% ..., but you are very unlikely to ever get a sample that gives a value of 25%, since that would be ten standard errors from the proportion you found in one of your samples.
@@dmcguckian
Thank you.I'm glad your explanation is so easy to understand.I can see that the coefficients vary using bootstrap, but I was curious how we could find the variation of the coefficients without using bootstrap.
We are finding coefficients for each data point (one dataframe line), and these are candidates for the coefficients found in the data at hand-data, and the hand-data variation is the SD of the coefficients.
That's what I understood from your explanation, but I'd be happy to let you know if I'm wrong.
Hello HI! Great vid! I am wondering if u are familiar with genetic analyses, particularly with heritability. In breeding, heritability (h^2) is estimated as the slope of a reg line. And h^2 is usually obtained thru methods like ANOVA/regression since we are dealing with variances of parents and offspring. Do you think it is OK to treat h^2 as a slope since IT IS, and compute for SE of its estimate using this kind of method as you have shown? Thanks!
Based on what you are describing, it sounds like this should work, but I am not familiar with the analysis you described. For this reason, I cannot say for certain.
Youareanangel!!!Thankyou!!
Thanks!
Hy. I really need help with some of these concepts . Any help will be appreciated.
GIRIRAJ DARAK check out the chapter 11 section of STATSprofessor.com. It’s free. No registration required or anything like that. Click STATSII, then Chapter 11.
it's also = slope/sqrt(F) for some reason
6:59
never seen anyone write 5 like that
I explained on another video that I never knew I wrote fives differently until I tried to write a 5 on a mental math app. It wouldn’t take my perfectly displayed 5 no matter how nice I made it look. Then it hit me, and I quickly scribbled an s on the screen. The app accepted my answer. Most people write a 5 in the order an s is written, so I realized two things that day: 1) the app used path to recognize the numbers not the final image and 2) I wrote my 5s in a strange way
Is this the same as finding the standard error for a regression line?
So, yes??
Most likely no. You might be thinking of SSE or MSE for the regression line.
I think you multiply that by the t-star value (degrees of freedom and confidence level) but I'm probably wrong 😬