no idea how i have taken two stats courses yet i've never heard degrees of freedom explained, let alone with such simplicity. thank you so much, i have my final tomorrow!
you all prolly dont give a damn but does anyone know of a method to log back into an instagram account..? I stupidly lost my account password. I appreciate any tricks you can give me!
It is truly unfortunate that we must rely on talented philanthropists like Zed to providee us with the very thing we should have received for paying so much money to the university or college! Bless Zed for his contribution to the knowledge of the masses out there!
The amount of time I've spent learning statistics has been wasted up until this point. This video is so damn intuitive that all the past learning is very clear. Thank you so much.
Having made high score in statistics, I never felt that I really grasped the meaning of each important terms. After watching 3 of your videos, I started to truly understand statistics first time in life. Thanks so much!
After all these years, I've finally found a decent explanation for the concept of R^2 and df. Can't believe they don't teach stats like this in school. Thank you so much!
You actually did a great job taking a time explaining what those variables are unlike paid college professors who just write out solutions to the book's problems on the board while narrating it nonstop.
Thank you so much for these videos, they are very helpful. Please, please make the 3rd, 4th and 5th video about this topic too! I'm looking forward to them. :)
hi bro, your both videos are awesome. i used to hate this subject but after watching your videos i feels like econometric is easiest subject. it would be great help if you could share the link of your other three videos. thanks.....
Thanks for a great teaching video. You have explained in an elegant way so that everyone can understand. Please continue with your work. Simply fantastic!!
Awesome videos! Just one quick question about this one...you say adjusted R squared decreases as k increases but the last part on the spreadsheet adjusted R squared is increasing as k increases up to the 7th added variable then it decreases. Does this mean that when the adjusted R squared starts decreasing you have too many variables?
In a course in UDEMY the instructor mentioned that we can have a negative R-squared sometimes, but did not explain why or when? So, when can we have a negative R-square? Or did the instructor missed to say ADJUSTED R-SQUARED? Thanks in advance.
R squared can never be negative. It's the result of one sum of squares divided by another sum of squares, and since squares can never be negative (assuming real numbers), then you're just dividing a positive # by a positive # Adjusted R squared can be negative if you add enough explanatory variables. Pretty unlikely to happen but it could
An excellent explanation. Was using this to review an old stats class after someone asked me to explain to them what DF is. Realized I didn't have a simple explanation of what it actually is. Thanks!
i sort of understand degree of freedom from all the reading but could not re-explain it by myself in a simple and easier to understand term! this is perfect thank you so much :)
THANK YOU SO MUCH for noting and acknowledging that the SSE is actual the sum of residuals squared, and SSR is the sum of the explained error squared. Every other stats teacher who fails to explain this should be fired for torturing their students.
Consider the equation x + y + z = 12. I have 3 - 1 = 2 degrees of freedom, meaning I am free to assign values to precisely two of the variables (say x and z). Now let's assume we have four data points and two. Now back to the regression problem, let's assume we have four data points and one variable ( a line we are trying to fit). According to the provided formula, the degrees of freedom is 4 - 2 = 2. Which two variables am I free to assign values to?
This is your second video I am watching and both have given simple and clear explanations to the topics treated. Excellent and thank you. I am learning data science and would ask if you could delve into all the statistics that support the different data science models. Maybe you have, I am not sure.
Why useful variables would increase the adjusted R2? If Adjusted R2 is affected by the number of degrees of fredom (which is affected by n and k), why more variables (even if useful) would increase adj R2? The df would decrease anyway... Plus, at the end of the video you say that Adj R2 gives us the explanatory power of the model. Do you mena by this, the strenght of the relations between the observations and the line of best fit?
Notes for my future revision. *Degree of Freedom in Linear Regression* = No. of data points - No. of data points needed to create the model = No. of data points - No. of parameters needed to create the model Two views 1. Equation: Number of parameters 2. Visual: Number of fitting data DF = N - K - 1
In the top 25 observation example at the end, is that 6th variable really useful? Only increasing Rsquared by less than 1%? Just in terms of parsimony?
I just want to say thank you VERY much for the videos. I started a master in the Netherlands and it is being very very very helpful, much more than what I was seeing in the classroom.
@zedstatistics please guide about how to deal with multiple regression .. like equations that have linear, and power trending .. how to deal with it .. looking forward to hearing from you soon. Thanks
Interesting. Degrees of freedom are the number of data points minus the dimensions of data being correlated with a regression model. The minus 1 seems to be one variable you give up by making it you're put variable.
I still don't quite get it. In mathematics, degrees of freedom equals the number of variables minus the number of constraints or equations among those variables. The more information we have about the variables and the relations between them, the lower the degrees of freedom. Here it seems to be the opposite. The more information (data points) we have, the greater the degrees of freedom?!
do you need to do adjusted r squared for a simple linear regression model with only one explanatory variable if you are comparing it with a multiple linear regression? Obviously for the multiple regression but should you do it for the simple one too?
I always thought of degrees of freedom the linear algebra way. If no of variables > the no of equations, we have those many free variables that can take any value.
5:58 - total eureka moment. RUclips videos teaching me more efficiently than my university lectures.
i feel the same :'(
boy i learned linear algebra,calculus and computer science that way. Now im on stats because i need it for machine learning . Unis sucks big time .
no idea how i have taken two stats courses yet i've never heard degrees of freedom explained, let alone with such simplicity. thank you so much, i have my final tomorrow!
you all prolly dont give a damn but does anyone know of a method to log back into an instagram account..?
I stupidly lost my account password. I appreciate any tricks you can give me!
Hey I know it's been a long long time, but I just came across your comment and got curious. How did that final go?
Hey I know it's been a long long long time, but I just came across you guys comments and got curious. So how did that final go?
Brilliant. I have read countless texts and seen countless videos on the subject and this is the 1st time i get an intuitive grasp.
It is truly unfortunate that we must rely on talented philanthropists like Zed to providee us with the very thing we should have received for paying so much money to the university or college!
Bless Zed for his contribution to the knowledge of the masses out there!
Thank you so much. Tears on my face and can’t stop
They're comin :) got a newbie called "WTF?! Normal Distribution?" to check out in the mean time. Thanks Harsh!
zedstatistics this is beautiful explanation of degrees of freedom. Wish I knew this in Uni. Good one
The amount of time I've spent learning statistics has been wasted up until this point. This video is so damn intuitive that all the past learning is very clear. Thank you so much.
Having made high score in statistics, I never felt that I really grasped the meaning of each important terms. After watching 3 of your videos, I started to truly understand statistics first time in life. Thanks so much!
It took you two years to post this second part but the wait was totally worth it.
After all these years, I've finally found a decent explanation for the concept of R^2 and df. Can't believe they don't teach stats like this in school. Thank you so much!
This explanation of Degrees of freedom blew my mind. Always i have heard degrees of freedom is n-2 but i have no clue what that meant until now.
Really enjoyed your 2 videos so far, very nice intuitive explanations. Please continue working on these!
please, don't stop. Your videos are an excellent study material
Multiple regression please...
You actually did a great job taking a time explaining what those variables are unlike paid college professors who just write out solutions to the book's problems on the board while narrating it nonstop.
THANK YOU SO MUCH, gosh statistics and econometrics is so tough :'(
Hey, in that case can you pls tell how will the degrees of freedom for SSR (sum of square of regressions) will be K only?
Amazing and very helpful.
you teach better than teacher at university for which i paid 26,000 pound
This is the best explanation about degrees of freedom I've seen. Thank you very much.
Just wow, amazing presentation, thank you so much
Fantastic explanation... Please upload more of ur videos.. ur really a good teacher
Great video, absolutely fantastic, thank you very much :)
You are such a great teacher. I wish my professors taught like you did...
you know your stuff man keep it up God bless you sir
lovely video very helpful quick catchup
please make video 3-5
Best explanation of degrees of freedom I've found so far!
:)
Prof. Alex Excellent & Brilliant
Till R2 and degrees of freedom I was able to understand, but got confused when it comes to Adjusted - R2
Thank you so much for these videos, they are very helpful. Please, please make the 3rd, 4th and 5th video about this topic too! I'm looking forward to them. :)
Very beautifully explained...!!! Thanks a lot sir...please continue good work.
You're my absolute hero for putting these concepts into such simple terms! I think you should be a teacher!
Now three years into being a highschool teacher thanks to comments like yours :)
@@zedstatisticswhat were you doing earlier? Were you working for company?
hi bro,
your both videos are awesome. i used to hate this subject but after watching your videos i feels like econometric is easiest subject. it would be great help if you could share the link of your other three videos.
thanks.....
Thanks for a great teaching video. You have explained in an elegant way so that everyone can understand. Please continue with your work. Simply fantastic!!
Excellent. Helped me to understand degrees of freedom and R2 which I was struggling with so long
Brilliant video.. Would appreciate if you could come out with a view advanced topics on regression too!
Very good video. Clear, assertive and very well presented. Thumb up 103 is mine.
Thank you very much. I never understood Degree of freedom but with the help of this video I got it. Thanks a lot. Please continue posting more videos.
Awesome videos! Just one quick question about this one...you say adjusted R squared decreases as k increases but the last part on the spreadsheet adjusted R squared is increasing as k increases up to the 7th added variable then it decreases. Does this mean that when the adjusted R squared starts decreasing you have too many variables?
In a course in UDEMY the instructor mentioned that we can have a negative R-squared sometimes, but did not explain why or when?
So, when can we have a negative R-square? Or did the instructor missed to say ADJUSTED R-SQUARED?
Thanks in advance.
R squared can never be negative. It's the result of one sum of squares divided by another sum of squares, and since squares can never be negative (assuming real numbers), then you're just dividing a positive # by a positive #
Adjusted R squared can be negative if you add enough explanatory variables. Pretty unlikely to happen but it could
An excellent explanation. Was using this to review an old stats class after someone asked me to explain to them what DF is. Realized I didn't have a simple explanation of what it actually is. Thanks!
This is genius stuff. Thanks Zed!!
I've heard others talk about degrees of freedom... but like always, you Zed it best.
you and khan academy should definitely have lifelong advantages in evereything
Please upload remaining parts of this series. They are really great :)
Very clear explanation on degrees of freedom and its effect on R squared, thank you
Very Informative Sir. you have great command over regression analysis
Its pretty useful, and the way you explain d.f. is much easier than most of other people
you're a life-saver :) good job, and many thanks!
One of the best explanation of degrees of freedom and R squared that I came across internet... Cheers!
Waiting for moore! :) this was really fun to watch especially because of the simple visualization !
i sort of understand degree of freedom from all the reading but could not re-explain it by myself in a simple and easier to understand term! this is perfect thank you so much :)
Hi, Great videos ! I was just wondering if Part 3 will be uploaded soon ?
Thanks so much! It was the best video, I've ever seen about the Degree of Freedom.
Never came across a better explanation than this one for dof and adjusted R2. Thanks a lot
15 minutes of this video taught so much more than hours of university classes
Great explanation Justin! Really helped to refresh my memory about these ideas.
THANK YOU SO MUCH for noting and acknowledging that the SSE is actual the sum of residuals squared, and SSR is the sum of the explained error squared. Every other stats teacher who fails to explain this should be fired for torturing their students.
Best explanation of df that I've seen. Thank you.
nice job. really helpful! thanks
Consider the equation x + y + z = 12. I have 3 - 1 = 2 degrees of freedom, meaning I am free to assign values to precisely two of the variables (say x and z). Now let's assume we have four data points and two. Now back to the regression problem, let's assume we have four data points and one variable ( a line we are trying to fit). According to the provided formula, the degrees of freedom is 4 - 2 = 2. Which two variables am I free to assign values to?
This is your second video I am watching and both have given simple and clear explanations to the topics treated. Excellent and thank you. I am learning data science and would ask if you could delve into all the statistics that support the different data science models. Maybe you have, I am not sure.
How have your studies been going?
Why useful variables would increase the adjusted R2? If Adjusted R2 is affected by the number of degrees of fredom (which is affected by n and k), why more variables (even if useful) would increase adj R2? The df would decrease anyway...
Plus, at the end of the video you say that Adj R2 gives us the explanatory power of the model. Do you mena by this, the strenght of the relations between the observations and the line of best fit?
Thanks a ton. Amazing explanation!!! Your videos have helped me understand concepts way better than any other platform.
Notes for my future revision.
*Degree of Freedom in Linear Regression*
= No. of data points - No. of data points needed to create the model
= No. of data points - No. of parameters needed to create the model
Two views
1. Equation: Number of parameters
2. Visual: Number of fitting data
DF = N - K - 1
Thank you, it was really good and helpful.
thanks a lot
Can we have part III, this is really helpful!
Great explanation and visualization - thanks!
In the top 25 observation example at the end, is that 6th variable really useful? Only increasing Rsquared by less than 1%? Just in terms of parsimony?
great video. specially the degrees of freedom.. helped a lot
I just want to say thank you VERY much for the videos. I started a master in the Netherlands and it is being very very very helpful, much more than what I was seeing in the classroom.
I'm just curious, how is your Master's going?
@zedstatistics please guide about how to deal with multiple regression .. like equations that have linear, and power trending .. how to deal with it .. looking forward to hearing from you soon. Thanks
Thank you so much for this, helped a lot :)
Hoped to see the next one too :( it's so good and clear. Thanks for your effort
Wish I found you earlier! You're a legend!
in wooldridges econometrics book SSE is written as explained sum squared and SSR written as residual sum squared
I tried plugging in numbers to compute ADJ R2 at the end and could not get your numbers - could anyone help- thanks for the amazing channel
Thank you, sir, for this amazing lecture....:)
❤
@10:00 If you decrease DOF means you decrease the number of variable, right ?
The video says on the contrary!
thank you very much for a straightforward explanation of econometrics.
Why does R-squared increase when degrees of freedom is decreased? It's not obvious to me why based on the formula for R-squared.
keep up the good work dude thanks it was useful a lot
Thank you for this. Was looking online for intuition and only got "n - k - 1" with no explanation. This makes complete sense.
Got an exam in two days and these videos have already really helped with the statistical part of it! Thank you very much!
Hey, I know it's been years, but how did that exam go?
Please post more! Your videos are so helpful!
Please is the k equal to the number of explanatory variables or simply the number of variables?
Your explanation is way beyond awesome.... Thanks
Interesting. Degrees of freedom are the number of data points minus the dimensions of data being correlated with a regression model. The minus 1 seems to be one variable you give up by making it you're put variable.
I still don't quite get it. In mathematics, degrees of freedom equals the number of variables minus the number of constraints or equations among those variables. The more information we have about the variables and the relations between them, the lower the degrees of freedom. Here it seems to be the opposite. The more information (data points) we have, the greater the degrees of freedom?!
Thank you somuch for the video. Complete revelation.
Insane good. Why wasting 3h in lectures, if it can be taught in 15min?
wouldn't degrees of freedom increase when you add more variables (9:55)?
do you need to do adjusted r squared for a simple linear regression model with only one explanatory variable if you are comparing it with a multiple linear regression? Obviously for the multiple regression but should you do it for the simple one too?
Thank you very much for explaining it so well with an example.
To calculate degree of freedom
Suppose we have condition n=k
Then what's the degree of freedom for such case
I hope you realise that you're a gem, love from India ❤️
Btw, where are you from?
first of all why r squared is required instead adjusted r squared can be kept . Anyway, nobody uses single predictor in the real world
Awesome and thank you for explaining degree of freedom.... at last i got it.
I always thought of degrees of freedom the linear algebra way. If no of variables > the no of equations, we have those many free variables that can take any value.