I have watched all your playlist from 1 to this one and will finish the remaining. I have learned more, with great depth and understanding of fundamentals, in one month with your videos, than what my MBA program taught me about data science in 2 years.
I'm a masters student in statistics that's taking a first semester of intro probability theory and linear regression. Honestly, this series is actually making everything click on the application. Direct proofs of definitions and the textbook wasn't doing it at all. Brings motivation to answer why we're proving what we're proving
You have explained and illustrated one of the most important topics in statistics really well! It’s a great video and highly useful.. Thank you so much!
Lecture was great but one thing i didn't understand that we calculated variance of x and y (bill and tip) by dividing "n" in the denominator (and not n-1, since it is a sample), but while calculating MSE we are dividing it by (n-2) because we considered it as a sample.
These days, I don't usually open UA-cam. But if I do , it's for completing this playlist and the others you've uploaded. Thank you for saving a distraught student.
I love it !! learning so much, just a bit confused on the excel, and how to do that , is there videos with more explanation on how to do data analytics for regression in excel?
nice video! I actually find the R-square (coefficient of determination from your other video ) =74.93% , where the correlation r= .866 is actually square root of "R-square". is that a coincidence ?! the correlation of simple linear regression is actually square root of SSR/SST!
what does this significance value tell us? I mean the significant difference is between what? Also, what is adjusted R square? What is the difference between R-squared and adjusted R-squared
Can anyone clarify me this point? At 12:50 Brandon says "MSE is an estimate of sigma square, the variance of the error epsilon". But isn't sigma square usually used to represent the variance of the population data? Why it is used now to represent the variance of the error?
I'm confused. Residuals have always been explained to be the difference between observed value to the predicted value. Here you say it's the observed value to the mean. SST = SSR + SSE, in which the SSR is the one that looks at the squared sum of residuals.
@13:08 you are mentioning degrees of freedom as 2.. should'nt it be 1?? The Anova Table @5:18 shows 1 as degree of freedom for the model and 4(n-p-1 = 6-2 ) as degree's of freedom for errors..
for Simple linear regression, the degrees of freedom for SSE is n-2 because there are 2 quantities estimated(the slope and the intercept) which limit the "freedom" of the data points(in this case the squares of the error terms). So MSE = SSE/n-2
Almost certainly yes! :) Different software can name it differently but root mean square error and standard error are almost certainly referring to the same thing.
Hello! They are correct. Since they add up to the correct SSE and I do those calculations in Excel later in the video and they add up to the correct SSE they therefore are correct. If there is a specific issue you are having let me know! :) Thanks for watching.
What is F and Significance F?? The videos are great but it all falls apart when you assume that knowledge. Are we supposed to have watched all previous 13 playlist in full?
Sample is a part of the population. For example, if our waiter served 20 tables that night, that would be our entire population. Here we are analyzing a sample of six tables.
I have watched all your playlist from 1 to this one and will finish the remaining. I have learned more, with great depth and understanding of fundamentals, in one month with your videos, than what my MBA program taught me about data science in 2 years.
Same for me
"it's simple math, don't freak out" yeah i felt that
I'm a masters student in statistics that's taking a first semester of intro probability theory and linear regression. Honestly, this series is actually making everything click on the application. Direct proofs of definitions and the textbook wasn't doing it at all. Brings motivation to answer why we're proving what we're proving
You have explained and illustrated one of the most important topics in statistics really well! It’s a great video and highly useful.. Thank you so much!
Lecture was great but one thing i didn't understand that we calculated variance of x and y (bill and tip) by dividing "n" in the denominator (and not n-1, since it is a sample), but while calculating MSE we are dividing it by (n-2) because we considered it as a sample.
These days, I don't usually open UA-cam. But if I do , it's for completing this playlist and the others you've uploaded. Thank you for saving a distraught student.
Hands down the best statistics video I have seen on UA-cam!
I've never left a comment on UA-cam but I had to this time. I'm so in love with your channel. Good content amazingly explaned. Thanks!
You´re really the best, thanks a lot Brandom
Thank you for great video series. Sorry, in 13:08 , maybe you mean SSE divided on the difference of sample size and DF? )
Yea here 'n' represent the sample size. And 2 is the dof
The content is so neat, you make stat simple and easy. Thank you Brandon :)
Thank you for making good informativ videos on this topic. It's hard to come by.
I really miss the motivation you used to give at the start of every video. Please include that motivation in every video lecture.
the explanation is very detailed. I like it very much.
Making this vid the same length along with the same title would be cool
I love it !! learning so much, just a bit confused on the excel, and how to do that , is there videos with more explanation on how to do data analytics for regression in excel?
nice video! I actually find the R-square (coefficient of determination from your other video ) =74.93% , where the correlation r= .866 is actually square root of "R-square". is that a coincidence ?! the correlation of simple linear regression is actually square root of SSR/SST!
it is true, not a coincidence
it is very clear about regression basics.
what does this significance value tell us? I mean the significant difference is between what? Also, what is adjusted R square? What is the difference between R-squared and adjusted R-squared
Nicely Explained. Thank you Brandon!
Great explanations! thanks a bunch!!
Can anyone clarify me this point? At 12:50 Brandon says "MSE is an estimate of sigma square, the variance of the error epsilon". But isn't sigma square usually used to represent the variance of the population data? Why it is used now to represent the variance of the error?
Merci Monsieur
13:01 how did we figure out degrees of freedom as 2? Also 14.10.shouldn't it be n-1?
One way Anova ;for MSE calculation ,Degree of freedom : N-C .In this case C=2 , N-2 is our Degree of freedom for calculation of MSE.
I'm confused. Residuals have always been explained to be the difference between observed value to the predicted value. Here you say it's the observed value to the mean. SST = SSR + SSE, in which the SSR is the one that looks at the squared sum of residuals.
tnx
Do you teach Data Mining as well?
Ur the best dude
@13:08 you are mentioning degrees of freedom as 2.. should'nt it be 1?? The Anova Table @5:18 shows 1 as degree of freedom for the model and 4(n-p-1 = 6-2 ) as degree's of freedom for errors..
Hi, I have the same question...
for Simple linear regression, the degrees of freedom for SSE is n-2 because there are 2 quantities estimated(the slope and the intercept) which limit the "freedom" of the data points(in this case the squares of the error terms). So MSE = SSE/n-2
May I ask, is the RMSE same as Standard Error of the Estimate?
Almost certainly yes! :) Different software can name it differently but root mean square error and standard error are almost certainly referring to the same thing.
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Thank you..
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When can we see the videos on time series?
17:00 why does the model have 1 degree of freedom?
what statistics software you use to calculate ANOVA and model error and F etc?
how do u get the F value at 20:55 ?
thank you!!!!!!!!!!!
Perfect!
Geat!!!
Your amazing...
how do you get the standardized coefficient beta
what is mean of response?
09:25min: the numbers of the squared errors are not correctly calculated I guess. Could you please confirm.
Hello! They are correct. Since they add up to the correct SSE and I do those calculations in Excel later in the video and they add up to the correct SSE they therefore are correct. If there is a specific issue you are having let me know! :) Thanks for watching.
Thank you for your reply and your videos, they are really good!!
Hey Brandon,
In some lectures they calculate r_squares as ,
r_square = 1 - (SSR/SST)
But,
You say r_square = SSR/SST
Does this both contradict ?
Degrees of freedom is 4 not 2. Looks like the explanation for degrees of freedom needs correction.
What is F and Significance F??
The videos are great but it all falls apart when you assume that knowledge. Are we supposed to have watched all previous 13 playlist in full?
You didn't explain, what's Adjusted R^2
I made an entire video on it in June 2021. Unfortunately I sometimes mention things that are present in output that I haven't gotten to yet.
@@BrandonFoltz oh yes I found it...thanks ✌🏻
is it correct if i say that standard error is identic to standard deviation?
Sir, What does it mean by Sample data and Population data? Can you please clarify my doubt?
Sample is a part of the population. For example, if our waiter served 20 tables that night, that would be our entire population. Here we are analyzing a sample of six tables.
Try playback on 2x normal speed
It's not difficult but it's kind of hard to see the big picture
Merci !
I really appreciate it! 🙏