I'm so impressed with how in depth and without choking you are capable to teach statistics. They are a pretty complex subject, and thanks to you we are getting to understand them, and who knows, maybe some of us even like them. THANKS!
if you know maths and desrciptive statistcis already, excep degrees of freedom and use of (n-1) instead of n, the critical explanation for you starts at 13.11 and ends at 15th minute but it's kind of explained away without a real explanation. let me look for the sections about regression etc.
Beautifully explained! This is a topic that gets glossed over a lot in statistics courses and I really appreciate the amount of time you devoted to it.
Just commented on your other video, but ended up watching this too by accident, when I was searching information on degrees of freedom. As a medical student doing research on biostatistic heavy subject, you truly are a lifesaver! Stuff like this really helps me to keep on going instead of needing to go through multiple statistic courses in uni on top of all my other studies and research project work!
Thank you for actually explaining the meaning behind DF clearly before jumping in to any abstract analysis of numbers. It is frustrating trying to find clear content so thank you for that.
this is the best video i have found online to tell me the DF, it is the independent pieces of information that exists in a sample to predict the main population. if were to predict, we must know the minimal values of independent pieces of sample to do a prediction over the population, generally, the more df the more accurate the prediction from the sample
Thanks for putting these together man. I'm not a student; I'm just brushing up on my stats. Your explanations are spot on. Had I had a teacher like you, I wouldn't be brushing up on my stats now (and I had some great mathematics teachers).
I'm greateful for your lectures, and can say that this specific topic was always somehow incomplete for me, until now! I'm studying calculus, and statistic is a challenge for me. Thank you, and be always healthy!
Big fan Justin, you really don't know how helpful and mind-blowing are these videos for like 'whole world'. I wish you health and happiness in these extraordinary times. Where are you from, as in country and city? I would be fortunate to meet you someday, you're a great guy!
This is a very helpful explanation on a topic that is all too easily glossed over, but I think it is essential to getting a firm grasp of what we are doing with statistics. Thank you for taking the time to post it.
@@pk-uk5lc Hey there, buddy! It ain't about being Indian or American, it's about the individual's ability to simplify things and make them more understandable. There are tons of examples of amazing Indian educators out there, not just on TH-cam.
Absolutely amazing explanation of degrees of freedom. You gave a very good, simple and easy example of the urchins through which what df is and how its calculated could be immediately understood. Great job.
Could you please explain why we use degrees of freedom to adjust the difference between sample statistics and population parameters? What does that have to do with "independent pieces of information"?
17:13 Only with the third observation that we have a "degree of freedom" such that the regression line can cut through the points, to get the errors and the parameters.
you changed n to n-1 without any mathematical proof. any other proofs? if we want to inflate the estimate, why not make it n-2? i am assuming there is some robust mathematical reasoning.
We use df primarily when estimating variances, because we know that dividing by n underestimates population variance. To my knowledge, ther is no formal mathematical proof to show that n-1 is necessarily "correct". We can never know it is 'correct", because in the real world we never know the true population values. That said, statisticians have demonstrated the concept using toy data sets for which the population values are defined. With these models, n-1 reliably made the variance estimates better. If n-1 had been better at the job, they would have chosen it. It's the mathematical equivalent if having thermometers that reliably read 10% colder than reality. We are simply compensating for a known bias in the tool.
The no. of Degrees of freedom of sum of squares = no. of independent variables in that sum of squares. Let SS= sum of (yi-ybar )^2 ,i=1,2..n here SS is sum of square of n elements (y1-ybar),(y2-ybar)....(yn-ybar). These elements are not all independent bcz sum(yi-ybar)=0 (which condition for dependence of variable). So we use ' n-1 ' degrees of freedom for SS insted of n.
@@niemand262 there are mathematical proofs as to what gives the best estimate on average (i.e. an unbiased estimate). In terms of standard deviations, its called "Bessel's correction" and there is a proof as to why we use n-1. As for using n-p, i.e. some other number of degrees of freedom, I THINK these are calculated by seeing how many of the data points are free to vary and still give us the same statistic. For example, if we calculate the mean of [x1, x2, x3], if we vary any of them, we can just move another one so that the mean stays the same. As we can move any of them, we have n=3 degrees of freedom. If we are estimating population variance from a sample without knowing the population mean, we are solving 2 equations (one for mean and one for variance) with n unknowns. As such, we can "replace" one of the n data points in the equation for standard deviation with some function of the sample mean whilst still technically expressing the standard deviation in the same way. As we can do this replacement of 1 of the data points with one of our statistics, we have n-1 degrees of freedom. This could be slightly wrong (I came to this video hoping for a full mathematical explanation) but I'm fairly sure its the gist of it.
@@henrysorsky thanks for pointing out "Bessel's correction". Your intuitive explanation for degrees of freedom makes sense, but why doesn't the same intuition also apply for the standard deviation of the population? After all, given a set of N observations, if you know the standard deviations of n-1 points around the mean, you know the value of the Nth standard deviation.
That is a very explanatory, cool sound, and step-by-step lecture. I really like it because it is very easy even for new beginners of statistics. Good job! many thanks.
From 17:00, for the next minute: that's where it clicked for me, and I (somewhat) understood what degrees of freedom means. Thank you! Great breakdown.
. Is that you ? . Nice to see a face to a name . Been watching a few of your videos . Thank you . Hope to use the skills into my retirement... touching 60 .
So, I have a question. What if the Population mean, bz chance happens to lie right on the the place where our sample mean is calculated. Then wouldnt we be unnecessarily inflating the variance? Might be a silly question but I am understanding the concept so well that just wanted to ask. :)
At 10:00, isn't it the case the numerator must be zero as well as the denominator since x -x_bar is also zero. So its not "an explosion" but undefined.
Thank you so very much for this thorough and well delivered explanation of a complex concept that many educators try to breeze over. The type of explanation you provided is rare and I can't believe how smoothly and clearly you delivered your content. I am super impressed and very inspired as I tutor statistics to my fellow students. Thank you again. Liked, Subscribed and hit the bell 😊🙏
In a regression, when you have three observations (n = 3), based on formula, degrees of freedom is 1. What does the number 1 mean? (based on your definition at the 6 minute mark on the video?)
The reason n-1 is used in calculating s^2 is because n-1 is unbiased estimator of sigma^2 or the variance. You did some wishy-washy hand waiving. If you don't want to work through the math, just say it can be shown that if n is used to calculate s^2, the bias is ((n-1)/n)*sigma^2 so by using n-1 you instead end up (n-1)/n-1) all times sigma^2. which is unbiased
23:38 What confuses me about this line of reasoning is that it requires you to know the total number of samples. You can't derive the number of samples for the missing category if you don't know the total. So isn't the total a piece of information in itself? To me it looks like you have four pieces of information: category 1, category 2, category 3 and the total. So you have 4 pieces of information. But one of those doesn't contribute anything new and is therefore obsolete which would leave you with 4 - 1 = 3 independent pieces of information and not 2. What am I missing?
@14:25 OMG, all this time, I took DoF as an abstract peculiarity of the equation. So the Sample Mean is just an estimate of the Population Mean. Reducing n by 1, you decreasing denominator and therefore inflating the variance to get a better estimate of the Population Variance, thereby including sampling error. Even though, I still don't know why you will use 2,3,4 DoF, I feel must more relieved now that this major stumbling block is removed. Thank you for breaking it down!
It couldn't be a better explanation! Unfortunately, when we ask ourselves: "what is, again, a "degree of freedom?", a half-hour response would not come to mind... 😢
17:45 The explanation of having k "X" variables was a bit confusing, I had to go through a second time to understand. I am not sure calling all the variables "X" variables is correct??? Wouldn't we just say "the number of variables?" One of the independent variables is perhaps on an X axis, the other independent variable on the Y. The dependent variable is on the Z. Good explanation everywhere else - the Chi-square examples were especially interesting.
Hello, great job on all the explanations. But my question is: I understand that we need to "inflate" the computation of the variance from estimate of the population mean by x bar, why should this "inflation" be by dividing by n-1? Why not divide by (n/2)? I did not see the answer to: where did n-1 come from? I will listen to the rest of the video in case the answer is in the remaining part...
14:20 why do we inflate the estimate by choosing to go from n to n-1. Why not n to n-2 for example? I'm baffled at all the responses saying how everything is clear to them, just because the denominator n-1 is smaller than n. Well n-2 and n-3 etc are also smaller than n. There's a mention of Bessel's correction in the comments below but nothing else anywhere in the video about what's really going on here.
I still don't understand why it's (n-1) to inflate your variance? Why isn't it (n-N), where N=1,2.... How does 1 make a sample variance into a population variance?
Shouldn't the DF of x bar in the descriptive statistics section be 4 instead of 5? When calculating the mean of 5 values, only 4 of those can vary. The last one can only take one possible value.
Justin, I had a doubt. Is it okay to say, in simple regression analysis, y hat has n-2 dof because beta1 and beta2 are estimates of the population coefficients that we don't know ??
For Descriptive Statistics: 1) Why does the variance use the squares of the deviations instead of their far more intuitive absolute values? The variance considers points farther from the mean as much more important for no particular reason I can see. 2) You say the n-1 term is included to make the sample standard deviation "bigger". But that explains nothing. Why not sqrt(n)-1? (you still get undefined for the case where n=1). Why is it n-1 specifically? In short, how can I look at any system and determine the degrees of freedom, even when they are other than n-1?
hi justin, nice and useful video you got here. I'd like to request if you could make one about bias, and what does it mean when you say unbiased estimate. thank you.
@zedstatistics Dear Sir, when you explained about the n-1 in the denominator of S.D., it was more of an empirical observation. I would like to know if we have a mathematical deduction of this formula. Thank you
For a sample of size 1, the formula for the d.f = n - 1 = 1 - 1 = 0, so why do we have still one degree of freedom, that allows us to estimate as parameter at least the mean? The only sample value of 213 has no freedom to vary, it cannot be any other value, so it is not an independent piece of information, therefore no d.f., right?
What softwares are you using to make these excellent videos? They are amazingly crisp and clean! Under the current pandemic and wanting to create better videos, I would love to know what tools you use. If you could share, that would be great! Thank you for making these videos!
great explanations at the beginning....I would suggest to define what things are at the beginning of the section, such as what the heck is a Chi squre test for. I had to stop the video and go out and google to see that it's useful when you have a model or hypothesis first to compare against observed data. So apparently you use it when you have a hypothesis. Also the table at the end seems backwards- I would visualize with the unbleached coral on the left if that's showing expected healthy distribution because it seems backwards to explain the other way..... maybe I'm wrong thinking about this... Now I have no idea why you are multiplying 60 x 50...what exactly do you mean by the marginal values?... oh well, People with not good math sense need all the help we can get. This is why I stopped paying attention in math when I was in middle school. The teacher would say something and go right on as if we all knew what they were talking about. Prior to the Chi Square section everything was making sense. Thank you. I probably need to go watch the actual Chi Square video if I want to know about that.
11:40 "using absolute values is clunky, statistically [which is why we use the variance instead]". I'm curious what the clunkiness you're alluding to is. I was taught that variance/stddev includes outliers a bit more than if we used absolute values, and that that was typically something we wanted in statistics, but that never made much sense to me. Great video, thank you! I'm a stats tutor in college and the prof for this class definitely handwaved DF.
i think it has to do with absolute values functions not being differentiable everywhere. finding optimums is not as easy as with squared functions, which are differentiable everywhere
Hi, thanks for nice explanation! I have a question about calculating degrees of freedom in chi-squared test. In population genetic study, the degrees of freedom is calculated as "the number of categories - the number of parameters" when we do chi-squared test for testing Hardy-Weinberg equilibrium. For example, if there are 6 genotypes(AA, BB, CC, AB, BC, AC), the number of categories(genotypes) is 6, and the number of parameters(alleles) is 3(A,B,C). So the degrees of freedom is 3. Do you know why is this? Additional description : Sum of allele frequencies is 1. And the expected genotypes are calculated from product of allele frequencies. If number of observed genotypes are 30,40,30 for AA, AB, BB respectively, allele frequencies are 0.5, 0.5 for A, B respectively. So the expected genotype will be 25(0.5*0.5*100), 50(2*0.5*0.5*100), 25(0.5*0.5*100) for AA, AB, BB.
Noice explanation, but I am confused. You mentioned that skewness has DF=3 and kurtosis has DF=2 given that n=5. But I think both skewness and kurtosis have DF=3 because, In your other video of moments, you were explaining the higher moments. For the second moment, we need to standardize the first moment, and for the third moment, we need to standardize the second moment. Till here all is good, but you mentioned for the fourth moment (i..e. Kurtosis), we don't need to standardize the third moment, it can be derived by the second moment itself. Am I going in the right direction? 🤣🤣🤣🤣. Hope so. Just reply to me once, what should be the DF of Kurtosis with 5 data?
Blessings. I’m a 79 yr old grad student. This stuff is rocking my self image
I'm so impressed with how in depth and without choking you are capable to teach statistics. They are a pretty complex subject, and thanks to you we are getting to understand them, and who knows, maybe some of us even like them. THANKS!
😊😊😊😊😊😊😊😊😊😊😊😊😊😊😊😊😊😊😊😊😊😊😊😊😊😊😊😊😊😮😊😊😊😊😮😊😅😊😊😮😊😮😊😊😊😅😊
P
Please f
if you know maths and desrciptive statistcis already, excep degrees of freedom and use of (n-1) instead of n, the critical explanation for you starts at 13.11 and ends at 15th minute but it's kind of explained away without a real explanation. let me look for the sections about regression etc.
Beautifully explained! This is a topic that gets glossed over a lot in statistics courses and I really appreciate the amount of time you devoted to it.
Hi! Your teaching style is exceptional! You really know how to approach the weaknesses of the students! Keep on the good work!
Just commented on your other video, but ended up watching this too by accident, when I was searching information on degrees of freedom. As a medical student doing research on biostatistic heavy subject, you truly are a lifesaver! Stuff like this really helps me to keep on going instead of needing to go through multiple statistic courses in uni on top of all my other studies and research project work!
Probably one of the BEST instructor I have run into in my career or lifetime - most people cannot teach statistics - this gentleman is awesome!
Thank you for actually explaining the meaning behind DF clearly before jumping in to any abstract analysis of numbers. It is frustrating trying to find clear content so thank you for that.
Liked the video as soon as i heard his introduction. Summed my feelings about the topic up Perfectly
so good. also his choice of words to explain concepts is really good
this is the best video i have found online to tell me the DF, it is the independent pieces of information that exists in a sample to predict the main population. if were to predict, we must know the minimal values of independent pieces of sample to do a prediction over the population, generally, the more df the more accurate the prediction from the sample
Thanks for putting these together man. I'm not a student; I'm just brushing up on my stats. Your explanations are spot on. Had I had a teacher like you, I wouldn't be brushing up on my stats now (and I had some great mathematics teachers).
I'm greateful for your lectures, and can say that this specific topic was always somehow incomplete for me, until now! I'm studying calculus, and statistic is a challenge for me. Thank you, and be always healthy!
I have never seen anyone describe degrees of freedom so clearly, thanks!
Big fan Justin, you really don't know how helpful and mind-blowing are these videos for like 'whole world'. I wish you health and happiness in these extraordinary times. Where are you from, as in country and city? I would be fortunate to meet you someday, you're a great guy!
It is even cool to watch stats with you as a teacher! Bravo.
DUUUUUDE!!!!! THANKS FOR ALL THE SIMPLE EXPLANATIONS. Appreciate it a lot.
1:26 - Relief, I thought that music was going all the way though the video. Awesome video. Best explanation I've found so far.
Your pace of delivering is perfect. Can't thank you enough!
Best explanation of Chi Square so far. Best use of my 27 minutes
This is a very helpful explanation on a topic that is all too easily glossed over, but I think it is essential to getting a firm grasp of what we are doing with statistics. Thank you for taking the time to post it.
I wish you were my professor
That's something most of us indians want -better education
It's better if we adapt the Vedic methods😂😂
He is your professor by choice.
Support the idea
@@pk-uk5lc Hey there, buddy! It ain't about being Indian or American, it's about the individual's ability to simplify things and make them more understandable. There are tons of examples of amazing Indian educators out there, not just on TH-cam.
you are a life saver for stats students...
Absolutely amazing explanation of degrees of freedom. You gave a very good, simple and easy example of the urchins through which what df is and how its calculated could be immediately understood. Great job.
Could you please explain why we use degrees of freedom to adjust the difference between sample statistics and population parameters? What does that have to do with "independent pieces of information"?
17:13 Only with the third observation that we have a "degree of freedom" such that the regression line can cut through the points, to get the errors and the parameters.
Very helpful. Thank you for helping me master this subject.
you changed n to n-1 without any mathematical proof. any other proofs? if we want to inflate the estimate, why not make it n-2? i am assuming there is some robust mathematical reasoning.
We use df primarily when estimating variances, because we know that dividing by n underestimates population variance. To my knowledge, ther is no formal mathematical proof to show that n-1 is necessarily "correct". We can never know it is 'correct", because in the real world we never know the true population values.
That said, statisticians have demonstrated the concept using toy data sets for which the population values are defined. With these models, n-1 reliably made the variance estimates better. If n-1 had been better at the job, they would have chosen it.
It's the mathematical equivalent if having thermometers that reliably read 10% colder than reality. We are simply compensating for a known bias in the tool.
The no. of Degrees of freedom of sum of squares = no. of independent variables in that sum of squares.
Let SS= sum of (yi-ybar )^2
,i=1,2..n
here SS is sum of square of
n elements
(y1-ybar),(y2-ybar)....(yn-ybar).
These elements are not all independent bcz
sum(yi-ybar)=0 (which condition for dependence of variable).
So we use ' n-1 ' degrees of freedom for SS insted of n.
please correct me,if am wrong
@@niemand262 there are mathematical proofs as to what gives the best estimate on average (i.e. an unbiased estimate). In terms of standard deviations, its called "Bessel's correction" and there is a proof as to why we use n-1. As for using n-p, i.e. some other number of degrees of freedom, I THINK these are calculated by seeing how many of the data points are free to vary and still give us the same statistic. For example, if we calculate the mean of [x1, x2, x3], if we vary any of them, we can just move another one so that the mean stays the same. As we can move any of them, we have n=3 degrees of freedom. If we are estimating population variance from a sample without knowing the population mean, we are solving 2 equations (one for mean and one for variance) with n unknowns. As such, we can "replace" one of the n data points in the equation for standard deviation with some function of the sample mean whilst still technically expressing the standard deviation in the same way. As we can do this replacement of 1 of the data points with one of our statistics, we have n-1 degrees of freedom. This could be slightly wrong (I came to this video hoping for a full mathematical explanation) but I'm fairly sure its the gist of it.
@@henrysorsky thanks for pointing out "Bessel's correction". Your intuitive explanation for degrees of freedom makes sense, but why doesn't the same intuition also apply for the standard deviation of the population? After all, given a set of N observations, if you know the standard deviations of n-1 points around the mean, you know the value of the Nth standard deviation.
That is a very explanatory, cool sound, and step-by-step lecture. I really like it because it is very easy even for new beginners of statistics. Good job! many thanks.
Superb explanation. This was stuck in my head. You just cleared the concepts. Grateful to you.
You are really an excellet teacher! I love the way you explain these concepts!
From 17:00, for the next minute: that's where it clicked for me, and I (somewhat) understood what degrees of freedom means. Thank you! Great breakdown.
First time i understand why it’s n-k-1. Thanks!
Happy 2021...Thank you Justin for the immense effort you put into this video...Love from Kerala...🙂
Thaaaankk you! No one ever properly explained it to me!
.
Is that you ?
.
Nice to see a face to a name
.
Been watching a few of your videos
.
Thank you
.
Hope to use the skills into my retirement... touching 60
.
So, I have a question. What if the Population mean, bz chance happens to lie right on the the place where our sample mean is calculated. Then wouldnt we be unnecessarily inflating the variance? Might be a silly question but I am understanding the concept so well that just wanted to ask. :)
At 10:00, isn't it the case the numerator must be zero as well as the denominator since x -x_bar is also zero. So its not "an explosion" but undefined.
Thank you so very much for this thorough and well delivered explanation of a complex concept that many educators try to breeze over. The type of explanation you provided is rare and I can't believe how smoothly and clearly you delivered your content. I am super impressed and very inspired as I tutor statistics to my fellow students. Thank you again. Liked, Subscribed and hit the bell 😊🙏
this explanation is simply fantastic, thank you so much!
Thank you so much sir. Please keep up the good work. I'm learning a lot.
This was very informative! I will be sharing this with my students.
great video acutally for the first time I can say I understand DFs
In a regression, when you have three observations (n = 3), based on formula, degrees of freedom is 1. What does the number 1 mean? (based on your definition at the 6 minute mark on the video?)
The three-dimensional explanation of degrees of freedom in regression was really a light bulb moment. Awesome stuff.
Detailed coverage , kudos .Finally What i have been looking for. Appreciate
The reason n-1 is used in calculating s^2 is because n-1 is unbiased estimator of sigma^2 or the variance. You did some wishy-washy hand waiving. If you don't want to work through the math, just say it can be shown that if n is used to calculate s^2, the bias is ((n-1)/n)*sigma^2 so by using n-1 you instead end up (n-1)/n-1) all times sigma^2. which is unbiased
A sea urchin has some many spikes?!
Wow...this is really awesome...you did in 30 mins what my lecturer couldnt do over the whole semester...LOL. THANK YOU!!
🤣🤣🤣
23:38
What confuses me about this line of reasoning is that it requires you to know the total number of samples. You can't derive the number of samples for the missing category if you don't know the total. So isn't the total a piece of information in itself? To me it looks like you have four pieces of information: category 1, category 2, category 3 and the total. So you have 4 pieces of information. But one of those doesn't contribute anything new and is therefore obsolete which would leave you with 4 - 1 = 3 independent pieces of information and not 2. What am I missing?
@14:25 OMG, all this time, I took DoF as an abstract peculiarity of the equation.
So the Sample Mean is just an estimate of the Population Mean. Reducing n by 1, you decreasing denominator and therefore inflating the variance to get a better estimate of the Population Variance, thereby including sampling error. Even though, I still don't know why you will use 2,3,4 DoF, I feel must more relieved now that this major stumbling block is removed. Thank you for breaking it down!
I would be interested in your perspective on how degrees of freedom should be considered for nuisance parameters.
9:10 you said standard deviation is undefined but mathematically both numerator and denominator are zero so why it is still undefined?
0/0 = undefined, not zero, believe it or not!
It couldn't be a better explanation!
Unfortunately, when we ask ourselves: "what is, again, a "degree of freedom?", a half-hour response would not come to mind... 😢
Loving your videos so far... Really helpful 🎉
the best stats explanation that I ever had!!!
17:45 The explanation of having k "X" variables was a bit confusing, I had to go through a second time to understand. I am not sure calling all the variables "X" variables is correct???
Wouldn't we just say "the number of variables?" One of the independent variables is perhaps on an X axis, the other independent variable on the Y. The dependent variable is on the Z.
Good explanation everywhere else - the Chi-square examples were especially interesting.
Hello, great job on all the explanations. But my question is: I understand that we need to "inflate" the computation of the variance from estimate of the population mean by x bar, why should this "inflation" be by dividing by n-1? Why not divide by (n/2)? I did not see the answer to: where did n-1 come from? I will listen to the rest of the video in case the answer is in the remaining part...
Same exact question! Thank you
Search for Bessel's Correction, there's a tough math deduction behind n-1.
Awesome explanation! thanks!
14:20 why do we inflate the estimate by choosing to go from n to n-1. Why not n to n-2 for example?
I'm baffled at all the responses saying how everything is clear to them, just because the denominator n-1 is smaller than n. Well n-2 and n-3 etc are also smaller than n.
There's a mention of Bessel's correction in the comments below but nothing else anywhere in the video about what's really going on here.
I still don't understand why it's (n-1) to inflate your variance? Why isn't it (n-N), where N=1,2....
How does 1 make a sample variance into a population variance?
Shouldn't the DF of x bar in the descriptive statistics section be 4 instead of 5? When calculating the mean of 5 values, only 4 of those can vary. The last one can only take one possible value.
Super clear explanation!
Brilliantly explained
Seriously, u are the greatest, I love u man 🤍🤍
Why isn't total counted in one of the independent pieces of information? After all we need that too??
Clearly explained, excellent.
This is very helpful 😃
AT 25:20, what is the purpose of marginal values? The calculation will change if the row total and column total is different right?
Justin, I had a doubt.
Is it okay to say, in simple regression analysis, y hat has n-2 dof because beta1 and beta2 are estimates of the population coefficients that we don't know ??
Probably the best explanation about df on TH-cam, well done!
yeah? but whats the explanation really?
23:53 Ooh, that's a dangerous assumption in 2022 haha. Thanks for the great lecture!
I also like to think of n-1 as reminding us that we just have xbar and hence only 1 piece of information.
For Descriptive Statistics:
1) Why does the variance use the squares of the deviations instead of their far more intuitive absolute values? The variance considers points farther from the mean as much more important for no particular reason I can see.
2) You say the n-1 term is included to make the sample standard deviation "bigger". But that explains nothing. Why not sqrt(n)-1? (you still get undefined for the case where n=1). Why is it n-1 specifically? In short, how can I look at any system and determine the degrees of freedom, even when they are other than n-1?
Great concise presentation!
Much appreciated!👍
Incredible explanation!
This is like the 8th video am watching on this channel today !! Where had you been all this while !!!!?
hi justin, nice and useful video you got here. I'd like to request if you could make one about bias, and what does it mean when you say unbiased estimate. thank you.
Oooo! I like this idea. Might even do a series on bias. Though I'm behind on other series at the moment so STAY TUNED :)
It's so good!!!! It's the way of how statistics should be taught!
Loved the line about dividing by zero, "mathematically speaking, an explosion." Made me laugh of loud.
I noticed that too but it didn’t cause me to lol...just a chuckle.😏
Fascinating Work you are doing ... Keep it up Plz
Perfect! But i still want to know, Why the DF for Skewness is N-3, and the DF for Kurtosis is N-2 ?
I now fully understood the rationale of the Degrees of Freedom.
Quite clearly explained.
great explanation. Keep up your good work!
You're very talented. Hope you figure a way to get wealthy off it.
@zedstatistics
Dear Sir, when you explained about the n-1 in the denominator of S.D., it was more of an empirical observation. I would like to know if we have a mathematical deduction of this formula.
Thank you
For a sample of size 1, the formula for the d.f = n - 1 = 1 - 1 = 0, so why do we have still one degree of freedom, that allows us to estimate as parameter at least the mean? The only sample value of 213 has no freedom to vary, it cannot be any other value, so it is not an independent piece of information, therefore no d.f., right?
Great video, as always. I could not find anything specifically about F-distribution, is it in the pipeline? Thank youy
What softwares are you using to make these excellent videos? They are amazingly crisp and clean! Under the current pandemic and wanting to create better videos, I would love to know what tools you use. If you could share, that would be great! Thank you for making these videos!
obvious that it's Prezi software
@@gazzzada Is it? I have used Prezi and wouldn't have guessed that's what was used to create this video. Thanks.
current version it gives even more options
great explanations at the beginning....I would suggest to define what things are at the beginning of the section, such as what the heck is a Chi squre test for. I had to stop the video and go out and google to see that it's useful when you have a model or hypothesis first to compare against observed data. So apparently you use it when you have a hypothesis. Also the table at the end seems backwards- I would visualize with the unbleached coral on the left if that's showing expected healthy distribution because it seems backwards to explain the other way..... maybe I'm wrong thinking about this... Now I have no idea why you are multiplying 60 x 50...what exactly do you mean by the marginal values?... oh well, People with not good math sense need all the help we can get. This is why I stopped paying attention in math when I was in middle school. The teacher would say something and go right on as if we all knew what they were talking about. Prior to the Chi Square section everything was making sense. Thank you. I probably need to go watch the actual Chi Square video if I want to know about that.
11:40 "using absolute values is clunky, statistically [which is why we use the variance instead]". I'm curious what the clunkiness you're alluding to is.
I was taught that variance/stddev includes outliers a bit more than if we used absolute values, and that that was typically something we wanted in statistics, but that never made much sense to me.
Great video, thank you! I'm a stats tutor in college and the prof for this class definitely handwaved DF.
i think it has to do with absolute values functions not being differentiable everywhere. finding optimums is not as easy as with squared functions, which are differentiable everywhere
What great explanation! I thank you.
Hi, thanks for nice explanation!
I have a question about calculating degrees of freedom in chi-squared test. In population genetic study, the degrees of freedom is calculated as "the number of categories - the number of parameters" when we do chi-squared test for testing Hardy-Weinberg equilibrium. For example, if there are 6 genotypes(AA, BB, CC, AB, BC, AC), the number of categories(genotypes) is 6, and the number of parameters(alleles) is 3(A,B,C). So the degrees of freedom is 3. Do you know why is this?
Additional description : Sum of allele frequencies is 1. And the expected genotypes are calculated from product of allele frequencies.
If number of observed genotypes are 30,40,30 for AA, AB, BB respectively, allele frequencies are 0.5, 0.5 for A, B respectively. So the expected genotype will be 25(0.5*0.5*100), 50(2*0.5*0.5*100), 25(0.5*0.5*100) for AA, AB, BB.
I'm very glad I subscribed to this channel
Kudos to you Bud! Great Explanation!
Noice explanation, but I am confused. You mentioned that skewness has DF=3 and kurtosis has DF=2 given that n=5. But I think both skewness and kurtosis have DF=3 because, In your other video of moments, you were explaining the higher moments. For the second moment, we need to standardize the first moment, and for the third moment, we need to standardize the second moment. Till here all is good, but you mentioned for the fourth moment (i..e. Kurtosis), we don't need to standardize the third moment, it can be derived by the second moment itself.
Am I going in the right direction? 🤣🤣🤣🤣. Hope so. Just reply to me once, what should be the DF of Kurtosis with 5 data?
Nice & illustrative
May I ask what software do you use to produce such attractive and informative video ?
Great explanation
Great content. I am starting a statistics channel. Any recommendations?