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 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
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
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!
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
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 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.
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.
![[DRIP] ‘CLIK CLAK’ PREVIEW](http://i.ytimg.com/vi/bcxkcOSbpac/mqdefault.jpg)
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
TH-camrs like you makes TH-cam such a great place! thanks a lot for nice interpretation!
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!!
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.🙏
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!
OMG! This is the best explanation I've ever heard! Thank you, Mr. Gilbert!
Make so much sense now, I'm really thankful for this video of yours. Thankyou so so much Mr. Gilbert.
AMAZINGLY clear & well-done. You are the best!!!!! Thank you, thank you, thank youuuuuu.
Nicely explained, simple but effective examples
Incredible content. I can always count on some youtuber to explain something better than the academic course that got me here.
Amazing Video for conceptual understanding
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!
That was beautiful.
Certainly the best explanation of df I have come across. Thanks for your effort and ingenuity.
Thank you for posting this! Clarifies so much!
man you should make video a lot about statistics,the way how you explain the thing is really simple and good
Absolutely BRILLIANT! Thank you!
you don't understand how happy i am to have come across this video
Excellent explanation... It got too clear for me now. Thanks.
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
This is the best video for Degrees of freedom.
the best explanation of degree of freedom, really appreciate
The best explanation on degree of freedom so far , I really appreciate your knowledge on stat.
your voice tho!! increases the probability of easier and faster understanding for pretty much any concepts.
this info literally made me one step further in life! now I can pass the dynamic lab defend.
After watching 7 videos on degree of freedom...finally got the perfect video....I cant thnx enough man....God bless u
Incredibly intuitive!! Thank you, sir, for such a lucid illustration.
just simply wonderful explanation
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
Best explanation on DF ever! Thank you so much.
best explanation so far,
Excellent content.
super Excellent video
I loved it, thank you so much! Really impressive how you put it so simple
Best explanation
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.
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.
The best of the best explanation of DF. Thanks!
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
Best vedio on youtube on this topic. Thanks a lot.
Thank you for this video.
I read the comment first, then watched the video - I was not disappointed. This guy is good.
W
the only video on youtube that cleared the topic for me! thank you!
Tremendous video; the best video
Very clear explanation of DF, Thanks, James ! :)
excellent illustration and explanation of the topic, kudos
Beautifully explained! Now it makes 100% sense why Variance and Standard is divided by N-1 instead of N.
extremely well-explained! Thank you James.
this video should have 119 BILLION views, literally, thank youuuu
This is one of the best explanation👋
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?
By far the best explanation in You tube
Amazing...
Its really great and easy to understand.
Great video.
I watched a few videos and only yours helped me understand the concept. I like graphics!
Thank u so much!!!! STAY BLESSED!!!!
Thank you. 20 years after leaving college with a B- in statistics, I finally got an idea what "df" is. Thank you.
Fantastic explanation!
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.
Great video! Thank you
PLEASE keep doing what you're doing
Thank you!!!! I was SO confused by my textbook and I finally get it!
The best explanation on youtube.
This video is amazing
Awesome explanation!
This is amazing!
the best and easiest explanation ever
Excellent!!
This video is world-class. The likes to dislike ratio says it all!
Thank you for the easy explanation!!!
Best description I've seen so far.
I love your explanation and the examples you gave. Thank you for your help 😊
Fantastic your video! It is the explanation I exactly looked for to show my students
3 hours of complicated over explained knowledge, condensed down into a 10 minute video. Thank you!
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
very good..thanks James!
Excellent explanation. Thank you
Wow! Great content. Thank you.
nice video
just one query why we are doing for the standard deviation for the population i mean n-1
Absolutely efficiently and clearly explained! Thank you!
Very nice explanation, thank you
excellent explanation
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 :)
Simplicity at it's best, I wish I had known this video when I was still at college 🙏
This video made me understand this concept well. Thank you!
this is the best video on the internet!
Brilliant!
awesome!😀
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.
Your videos are very helpful! Thanks a lot!
It's really sad that you didn't continue make more videos though =[
Very well done, thank you!
This really helped me have a better grasp, thank you!
Good video. Thank you!
Great video 🫶🎉🧡🤩🙌