for real though, videos like these are so helpful along with the textbook sometimes even. When professors are explaining it I feel they're going off the top of the head so sometimes its explain oddly and confuses me even further.
This video just made an entire semester of statistics make more sense. Thank you, this information will help out a lot for the final. Much Appreciated!!
What an amazing explanation! I banged my head reading quite a lot, still didn't get it, and watching this video I will remember it forever now! Really great job James! Looking forward to watch more such videos!
I’m on Level 2 of the CFA designation and this blew my mind. Thank you for the clarity and simplicity. I feel like I’m walking away with a better understanding of
I can't believe how simple this was! the brilliance is always in the basics! I've been trying to understand why instead of "thats just how it is" and neither my teacher or my textbook have made sense to me as to why! Thank you so much you made everything click after the coin example!
this is the most intuitive on df, the fact in reality that we never knew value of true population mean, and sd always come after, in most cases n-1 is the reality of our data because as soon as we come up with an estimate of an average' the last piece of information is no longer needed. because that last piece of datapoint violates randomness of data so it has to be tossed out
Thanks for a very clear explanation. I have a question, though. In the example about standard deviation of a sample, since we knew in advance the mean Xbar, then we need to divided by (n-1) since this is the df of this. However, in the formula for sd of a population (assumed that we knew the mean muy) , it is divided by N. Why is this the case? Why can't we use (N-1) as with the sample? Don't we know muy in advance?
this is just so great i must appreciate your effor and the simplicity woth which you explained the concept,Thank You so much please continue to make such contributions
I completely understood your explanation. Having calculated the mean of the sample, the DOF reduces by one, as the nth value gets fixed. But, why can't I make the same argument for the whole population? Shouldn't DOF for its variance also reduce by one, since we already have the mean calculated? So, by that effect population variance shouod also be divided by N-1 ?
Plz is there an answer to this? Same question here!
2 หลายเดือนก่อน
There is a crucial difference between the population mean and the sample mean; only the latter is an estimate, and it is an estimate of the former. The consequence of this fact is that you lose one degree of freedom when you calculate the mean from the sample data, and that's why we divide by n-1 when the mean we are using is only an estimate, and that's also why we divide by n when the mean we are using is a fixed value calculated by a set of fixed data.
I don't know if you'll ever see this but I have a question that relates to the calculation of sample variance versus population variance, where for population variance calculation, you don't need the n-1 degrees of freedom. Given that you know the mean for the population as well as for the sample and so effectively an n-1 dof, which only 'n-1' not used for the calculation of population variance. And by the way, your explanation of degrees of freedom is absolutely brilliant. Thank you so much for this video
This video is of virtue! Degree of reedom is a very hard concept to understand and other internet searches do not help much. I cannot thank you enough for this exceptional explanation.
I understood from the degrees of freedom perspective but I still do not understand why the Nth value do not make any contribution to the value of Standard deviation/Variance
I still don't get why the n-th value doesn't contribute to the standard deviation. How can we say that only n-1 pieces of information contribute to it when in the numerator, we calculate the square of the deviation from the mean for all n values? I understand that (x1-xbar) + ... + (xn - xbar) = 0, so if we know what (x1-xbar)^2 + ... + (xn-1 - xbar)^2 is, we can work out (xn-xbar). What I don't understand is why it matters. I mean, we HAVE all n values, we don't have to work anything out. And we use all n values in the formula, so why act as if we didn't?
@@alijulaeerad5258 Could you give an example of that? Are you talking about not knowing all the scores of the population, and estimating the population standard deviation from the sample standard deviation? If so, the sample scores must still all be known right? Or are you talking about not knowing all the scores in a sample? Then what standard deviation are you estimating? Also practically why do statisticians estimate standard deviation?
@@Han-ve8uh we try to estimate the standard deviation of the population because we do not have all the scores In the population we only have all the scores in the sample. So in order to estimate the standard deviation of the population we assume that we know all the scores but one because that one score doesn't contribute to the standard deviation.
Thanks for clearly explaining. My conclusion about df: Within a formula if you know already other parameters (not include values of samples), how many minimum values of samples do you need to know for imputing all values of entire samples.
The way I've been crying over this for 3 weeks and this guy got me to understand it in a 10 minute video
for real though, videos like these are so helpful along with the textbook sometimes even. When professors are explaining it I feel they're going off the top of the head so sometimes its explain oddly and confuses me even further.
This video is fantastic and much needed on the internet. Please carry on making content like this. Thank you so much!
Nothing can beat this way of explaining degrees of freedom. Thank you sir
This video just made an entire semester of statistics make more sense. Thank you, this information will help out a lot for the final. Much Appreciated!!
TH-camrs like you makes TH-cam such a great place! thanks a lot for nice interpretation!
After watching this video, there is a portion of my brain which is unlocked.
An outstanding video James. Bring more videos like this.
This was the best explanation of degree of freedom I have ever seen on TH-cam or any other reading material., thanks Mr James.🙏
OMG! This is the best explanation I've ever heard! Thank you, Mr. Gilbert!
What an amazing explanation! I banged my head reading quite a lot, still didn't get it, and watching this video I will remember it forever now! Really great job James! Looking forward to watch more such videos!
AMAZINGLY clear & well-done. You are the best!!!!! Thank you, thank you, thank youuuuuu.
Make so much sense now, I'm really thankful for this video of yours. Thankyou so so much Mr. Gilbert.
Incredible content. I can always count on some youtuber to explain something better than the academic course that got me here.
I’m on Level 2 of the CFA designation and this blew my mind. Thank you for the clarity and simplicity. I feel like I’m walking away with a better understanding of
you don't understand how happy i am to have come across this video
Absolutely BRILLIANT! Thank you!
I can't believe how simple this was! the brilliance is always in the basics! I've been trying to understand why instead of "thats just how it is" and neither my teacher or my textbook have made sense to me as to why! Thank you so much you made everything click after the coin example!
This is the best video for Degrees of freedom.
this info literally made me one step further in life! now I can pass the dynamic lab defend.
Thank you for posting this! Clarifies so much!
this is the most intuitive on df, the fact in reality that we never knew value of true population mean, and sd always come after, in most cases n-1 is the reality of our data because as soon as we come up with an estimate of an average' the last piece of information is no longer needed. because that last piece of datapoint violates randomness of data so it has to be tossed out
Certainly the best explanation of df I have come across. Thanks for your effort and ingenuity.
Excellent explanation... It got too clear for me now. Thanks.
After watching 7 videos on degree of freedom...finally got the perfect video....I cant thnx enough man....God bless u
this video should have 119 BILLION views, literally, thank youuuu
Nicely explained, simple but effective examples
your voice tho!! increases the probability of easier and faster understanding for pretty much any concepts.
The best explanation on degree of freedom so far , I really appreciate your knowledge on stat.
Thank you. 20 years after leaving college with a B- in statistics, I finally got an idea what "df" is. Thank you.
Incredibly intuitive!! Thank you, sir, for such a lucid illustration.
Thanks for a very clear explanation. I have a question, though. In the example about standard deviation of a sample, since we knew in advance the mean Xbar, then we need to divided by (n-1) since this is the df of this. However, in the formula for sd of a population (assumed that we knew the mean muy) , it is divided by N. Why is this the case? Why can't we use (N-1) as with the sample? Don't we know muy in advance?
this is just so great i must appreciate your effor and the simplicity woth which you explained the concept,Thank You so much please continue to make such contributions
the best explanation of degree of freedom, really appreciate
I read the comment first, then watched the video - I was not disappointed. This guy is good.
W
Amazing Video for conceptual understanding
I completely understood your explanation. Having calculated the mean of the sample, the DOF reduces by one, as the nth value gets fixed.
But, why can't I make the same argument for the whole population? Shouldn't DOF for its variance also reduce by one, since we already have the mean calculated? So, by that effect population variance shouod also be divided by N-1 ?
Plz is there an answer to this? Same question here!
There is a crucial difference between the population mean and the sample mean; only the latter is an estimate, and it is an estimate of the former. The consequence of this fact is that you lose one degree of freedom when you calculate the mean from the sample data, and that's why we divide by n-1 when the mean we are using is only an estimate, and that's also why we divide by n when the mean we are using is a fixed value calculated by a set of fixed data.
man you should make video a lot about statistics,the way how you explain the thing is really simple and good
Best explanation on DF ever! Thank you so much.
That was beautiful.
Beautifully explained! Now it makes 100% sense why Variance and Standard is divided by N-1 instead of N.
I watched a few videos and only yours helped me understand the concept. I like graphics!
extremely well-explained! Thank you James.
Dude you did a fantastic job . I tried every where but I was not getting the concept . You did it in one go. Really thanks. Apppreciate it.
I loved it, thank you so much! Really impressive how you put it so simple
I don't know if you'll ever see this but I have a question that relates to the calculation of sample variance versus population variance, where for population variance calculation, you don't need the n-1 degrees of freedom. Given that you know the mean for the population as well as for the sample and so effectively an n-1 dof, which only 'n-1' not used for the calculation of population variance.
And by the way, your explanation of degrees of freedom is absolutely brilliant. Thank you so much for this video
Very clear explanation of DF, Thanks, James ! :)
The best of the best explanation of DF. Thanks!
the only video on youtube that cleared the topic for me! thank you!
excellent illustration and explanation of the topic, kudos
I paid thousands of dollars for my tuition and ended up searching video tutorials here...I hope every professor can make things clear in this way
3 hours of complicated over explained knowledge, condensed down into a 10 minute video. Thank you!
Best vedio on youtube on this topic. Thanks a lot.
just simply wonderful explanation
Thank u so much!!!! STAY BLESSED!!!!
This video is of virtue! Degree of reedom is a very hard concept to understand and other internet searches do not help much. I cannot thank you enough for this exceptional explanation.
Thank you for this video.
The best explanation on youtube.
Its really great and easy to understand.
the best and easiest explanation ever
PLEASE keep doing what you're doing
I understood from the degrees of freedom perspective but I still do not understand why the Nth value do not make any contribution to the value of Standard deviation/Variance
Excellent content.
This video is world-class. The likes to dislike ratio says it all!
This is one of the best explanation👋
Tremendous video; the best video
Simplicity at it's best, I wish I had known this video when I was still at college 🙏
Thank you for the easy explanation!!!
Fantastic your video! It is the explanation I exactly looked for to show my students
Best description I've seen so far.
By far the best explanation in You tube
Thank you!!!! I was SO confused by my textbook and I finally get it!
I love your explanation and the examples you gave. Thank you for your help 😊
super Excellent video
very good..thanks James!
Your videos are very helpful! Thanks a lot!
It's really sad that you didn't continue make more videos though =[
This is very helpful. I teach an introductory stats class and this will help me explain df to my class.
Thank you! This was very helpful and straight to the point :)
best explanation so far,
Great video! Thank you
Amazing...
Wow! Great content. Thank you.
this is the best video on the internet!
This really helped me have a better grasp, thank you!
Great video.
Best explanation
I still don't get why the n-th value doesn't contribute to the standard deviation. How can we say that only n-1 pieces of information contribute to it when in the numerator, we calculate the square of the deviation from the mean for all n values? I understand that
(x1-xbar) + ... + (xn - xbar) = 0,
so if we know what
(x1-xbar)^2 + ... + (xn-1 - xbar)^2
is, we can work out (xn-xbar). What I don't understand is why it matters. I mean, we HAVE all n values, we don't have to work anything out. And we use all n values in the formula, so why act as if we didn't?
sometimes in statistical tests we actually don't know all the scores and we need to "estimate" the standard deviation.
@@alijulaeerad5258 Could you give an example of that? Are you talking about not knowing all the scores of the population, and estimating the population standard deviation from the sample standard deviation? If so, the sample scores must still all be known right? Or are you talking about not knowing all the scores in a sample? Then what standard deviation are you estimating? Also practically why do statisticians estimate standard deviation?
@@Han-ve8uh we try to estimate the standard deviation of the population because we do not have all the scores In the population we only have all the scores in the sample. So in order to estimate the standard deviation of the population we assume that we know all the scores but one because that one score doesn't contribute to the standard deviation.
Fantastic explanation!
Awesome explanation!
This is amazing!
This video made me understand this concept well. Thank you!
Absolutely efficiently and clearly explained! Thank you!
Excellent!!
Thanks for clearly explaining. My conclusion about df: Within a formula if you know already other parameters (not include values of samples), how many minimum values of samples do you need to know for imputing all values of entire samples.
This video is amazing
Excellent explanation. Thank you
Great video 🫶🎉🧡🤩🙌
A magician at 4:22
and when you try with the blue one ..... taraaaaaaa there is no thing too
Very well done, thank you!
Very nice explanation, thank you
Good video. Thank you!
Super sir