Sir, I am not even in your class, yet the day before the final midterm I'm viewing your videos. That itself should say enough, but just to reiterate: THANK YOU ON BEHALF OF ALL STAT*2040 STUDENTS IN ALL SECTIONS IN EVERY SEMESTER. You are the second prof @ guelph I have encountered that uses TH-cam as a teaching tool, and I must say, it is probably one of the best ideas I have seen. Thanks again, a panicking gryphon.
You're welcome, and thanks for the feedback! I'm just glad to be of help. I'm still working on adding new videos, and I'll have a new set of hypothesis testing videos up in the next couple of weeks.
I really really hope you answer me sir but i still can't understand when do we need to use this low. I mean which type of phenomenons do require this low And please i don't like the answer ( the situations of normal distribution which require this low) because i can't distinguish between the situations normally distributed and the situation that follows other low like Khi2 and other I hope you answer me sir
Thank you. I have a test in this tomorrow and I didn't understand all the concepts. You're a lifesaver, I already recommended your channel to my classmates
9 years later your Chanel still saving my Ass. You should keep working on this channel because during pandemic we are suffering a lot with Online Classes.
Thanks Mate you are a Legend. I have learned so much from my Canadian mates, now I am so eager to visit Canada to learn more. You guys are Awesome. Thanks Again.
I use your video to learn English. Thinks! 0:00 Let's look at an introduction to the normal distribution also known as the Gaussian distribution. The normal distribution is an extremely important continuous probability distribution. It arises very very frequently in theory and practice. 0:18 Here I've plotted out what is approximately the distribution of the height of adult Canadian males and this what we see here is a normal distribution. This is our variable down here and f of X is what we call the probability density function which gives the height of the curve at point X. 0:40 Here's the equation for the probability density function of the normal distribution. We're not going to be working with this directly very often but there's a few points we should note. 0:49 First of all if the random variable X has a normal distribution then it can take on any finite value. 0:57 There are two parameters to this distribution. Mu is the mean of the distribution and the mean can take on any finite value as well. Sigma is the standard deviation and Sigma squared of course is the variance. Sigma has to be some positive value and of course Sigma squared would have to be some positive value as well. 1:24 Here's a normal distribution plotted out. Mu the mean is also right smack dab in the middle of the distribution. The distribution is symmetric about mu. So mu represents the mean of the probability distribution and it's also the median of the probability distribution. Now let's try and get some perspective for what Sigma represents. 1:49 Here I've plotted in one standard deviation below the mean to one standard deviation above the mean and as we'll see a little bit later on this area between one standard deviation below to one standard deviation above is approximately zero point six eight 68% of the area lies within one standard deviation of the mean. 2:13 Here I've plotted in mu minus 2 standard deviations 2 mu plus 2 standard deviations and approximately 95% of the area lies within two standard deviations of the mean and approximately 99.7% of the area lies within three standard deviations of the mean. 2:41 Here's another plot to give us a little perspective on what Sigma represents. Over here this red line represents a normal distribution with a standard deviation of Sigma 2 and this white curve represents a normal distribution with the standard deviation of Sigma 1 but Sigma 2 is double Sigma 1 so the standard deviation on the red curve is double the standard deviation of the white curve and we can see that when the standard deviation is greater there is more area in the tails and a lower peak. 3:16 There are an infinite number of different normal distributions corresponding to all the different possible values for MU and Sigma. If X is a random variable that has a normal distribution with mean mu and variance Sigma squared we write this as the random variable X is distributed normally with a mean of mu and a variance of Sigma squared. X is distributed normally a big N first term is the mean second term is the variance. Be a bit careful here as different sources can have a different term in here sometimes people have the second term representing the standard deviation I have the second term representing the variance. So just be a bit careful with that. 4:07 By definition the standard normal distribution is a normal distribution with the mean of 0 and a variance of 1. We often represent random variables that have the standard normal distribution with the letter Z and yes I am Canadian and we might say if Zedd has a standard normal distribution we might write this as Zedd is distributed normally with a mean of 0 and a variance of 1 that being mu that being Sigma squared and if the variance of is 1 of course Sigma is 1 as well. 4:46 As we will soon see we very often need to find areas under the standard normal curve. Probabilities are simply areas under the curve and very often the question of interest involves finding a probability. 5:01 Finding probabilities and percentiles for the normal distribution requires integrating the probability density function. There isn't a closed form solution and it must be integrated numerically. Fortunately for us we're going to use software or a standard normal table to actually find these values in practice.
Thank you very much for your videos! I use them for business mathematics and statistics! I sent you around 30 student to this channel already :) (I also have to add that you speak easily understandable for not native speaker which is a big plus for my classmates in college!
I have a stats midterm test tmr, still dont know whether i can make it through but i grasp a lot from your channel and am more confident about the test. Thank you so much
wow, u are doing some great karma in life. Very clear explanations. These videos are incredibly helpful. Thanks a lot sir. wish you mental peace and physical well being with metta loving kindness Ravindra
So my guess was right with all those Canadian males questions.. you are one :D Great channel.. I wish to meet you. Words fail me. I just can't express how you turned a subject I hated to the one I enjoy the most! Cheers from India
I don`t usually comment on videos, but after this `yes, I`m a Canadian` haha. We`re missing some `eh` eh? By the way, I love these videos very well explained. Please keep it going :)
Normal Distribution (also known as the Gaussian, Gaussian, or Laplace-Gaussian distribution) is a type of continuous probability distribution for a real-valued random variable. Normal Distribution PDF: www.toevolution.com/blog/view/2274758/normal-distribution-pdf
Hi a small doubt We normally plot Roys safety first ratio on Normal distribution for identifying shortfall risk Can we also plot IRR on Normal Distribution Curve ,and say IRR=Max return a customer can earn beyond wich NPV will become negative(As per number scale) So can I conclucdue Because at this point NPV will be zero
+cinqueottavi I wouldn't call that a step back, I'd call that a step forward (i.e. I discuss that later in the course). A common way of assessing whether the observed data is approximately normally distributed is by plotting a normal quantile-quantile plot. I discuss those plots in this video: th-cam.com/video/X9_ISJ0YpGw/w-d-xo.html.
wait what did being Canadian have to do with standard normal distribution and Z? just curious whether it was just randomly answering a question or something else more relevant xD
It has to do with the pronunciation of the letter Z. Americans call it "zee", whereas Canadians (and those in other areas of the world) call it "zed". When I taught at the University of Georgia, some students thought "zed" was a special statistics name for it. When I call it "zed" in the video, most of my American viewers will instantly recognize that I'm not American (if they hadn't figured it out yet from other clues).
No, definitely not. Many variables have a distribution that is approximately normal, but definitely not all. For example, the distribution net worth of US households would be strongly skewed to the right.
Sir, I am not even in your class, yet the day before the final midterm I'm viewing your videos. That itself should say enough, but just to reiterate: THANK YOU ON BEHALF OF ALL STAT*2040 STUDENTS IN ALL SECTIONS IN EVERY SEMESTER. You are the second prof @ guelph I have encountered that uses TH-cam as a teaching tool, and I must say, it is probably one of the best ideas I have seen.
Thanks again,
a panicking gryphon.
Thanks man, after 11 years your videos are still usefull
You're welcome, and thanks for the feedback! I'm just glad to be of help. I'm still working on adding new videos, and I'll have a new set of hypothesis testing videos up in the next couple of weeks.
I really really hope you answer me sir but i still can't understand when do we need to use this low. I mean which type of phenomenons do require this low
And please i don't like the answer ( the situations of normal distribution which require this low) because i can't distinguish between the situations normally distributed and the situation that follows other low like Khi2 and other
I hope you answer me sir
This graph is the cause of all my pain and sadness.
And YES I'M CANADIAN! (with that tone) :DDD
Thank you, very helpful! Better than any professor :)
yes, better than my professor
i am your professor and i know who you are. Your grade this semester will be C-
@@messididit damm u cant end a man like this
@@ytpfan2 🤣🤣🤣
@@messididit 💀😶🙄😭
You have literally saved my grades with your videos. I'm so glad I found your channel! Thank you so much!
Your videos made statistics so much more bearable! Thank you so much, you Canadians are awesome!
I agree -- Canadians are awesome! I'm glad I could be of help!
Thank you. I have a test in this tomorrow and I didn't understand all the concepts. You're a lifesaver, I already recommended your channel to my classmates
You are very welcome! All the best on your test!
LOLed at "Yes I am canadian". Ermmm we figured it already when you said "Out" in a few videos. Hahaha
Same here. It's not often one blurts out laughing when watching mathematical videos :-)
I'm laughing at this after 11 years , time man
I came just to learn what normal distribution in general is, but stayed to listen and learn about formulas and the statistical approach :) Thanks!
bro, u cracked me up when u said "and yes I AM Canadian" . Thanks for making my day! xD
You are very welcome :)
9 years later your Chanel still saving my Ass. You should keep working on this channel because during pandemic we are suffering a lot with Online Classes.
Thanks for the kind words! I'm glad to be of help. I am indeed planning on getting back to production soon.
Thanks Mate you are a Legend. I have learned so much from my Canadian mates, now I am so eager to visit Canada to learn more. You guys are Awesome. Thanks Again.
I've got a Statistics exam tomorrow. Your videos are God sent.
I'm glad to be of help!
jbstatistics tomorrow is my exam also :D
after 8 years this video still helps me !!!!! thank youuuuuuuuuuuu
you're videos have helped me immensely, thanks so much for helping me understand concepts in 5 minutes instead of hours with a text book!
What a perfect explanation in 5 minutes.💯
You are very welcome Connor. I'm glad to be of help! Cheers.
Thank you very much! I'll be getting back to adding videos soon!
I use your video to learn English. Thinks!
0:00 Let's look at an introduction to the normal distribution also known as the Gaussian distribution.
The normal distribution is an extremely important continuous probability distribution.
It arises very very frequently in theory and practice.
0:18 Here I've plotted out what is approximately the distribution of the height of adult Canadian males and this what we see here is a normal distribution.
This is our variable down here and f of X is what we call the probability density function which gives the height of the curve at point X.
0:40 Here's the equation for the probability density function of the normal distribution.
We're not going to be working with this directly very often but there's a few points we should note.
0:49 First of all if the random variable X has a normal distribution then it can take on any finite value.
0:57 There are two parameters to this distribution.
Mu is the mean of the distribution and the mean can take on any finite value as well.
Sigma is the standard deviation and Sigma squared of course is the variance.
Sigma has to be some positive value and of course Sigma squared would have to be some positive value as well.
1:24 Here's a normal distribution plotted out.
Mu the mean is also right smack dab in the middle of the distribution.
The distribution is symmetric about mu.
So mu represents the mean of the probability distribution and it's also the median of the probability distribution.
Now let's try and get some perspective for what Sigma represents.
1:49 Here I've plotted in one standard deviation below the mean to one standard deviation above the mean and as we'll see a little bit later on this area between one standard deviation below to one standard deviation above is approximately zero point six eight 68% of the area lies within one standard deviation of the mean.
2:13 Here I've plotted in mu minus 2 standard deviations 2 mu plus 2 standard deviations and approximately 95% of the area lies within two standard deviations of the mean and approximately 99.7% of the area lies within three standard deviations of the mean.
2:41 Here's another plot to give us a little perspective on what Sigma represents.
Over here this red line represents a normal distribution with a standard deviation of Sigma 2 and this white curve represents a normal distribution with the standard deviation of Sigma 1 but Sigma 2 is double Sigma 1 so the standard deviation on the red curve is double the standard deviation of the white curve and we can see that when the standard deviation is greater there is more area in the tails and a lower peak.
3:16 There are an infinite number of different normal distributions corresponding to all the different possible values for MU and Sigma.
If X is a random variable that has a normal distribution with mean mu and variance Sigma squared we write this as the random variable X is distributed normally with a mean of mu and a variance of Sigma squared.
X is distributed normally a big N first term is the mean second term is the variance.
Be a bit careful here as different sources can have a different term in here sometimes people have the second term representing the standard deviation I have the second term representing the variance.
So just be a bit careful with that.
4:07 By definition the standard normal distribution is a normal distribution with the mean of 0 and a variance of 1.
We often represent random variables that have the standard normal distribution with the letter Z and yes I am Canadian and we might say if Zedd has a standard normal distribution we might write this as Zedd is distributed normally with a mean of 0 and a variance of 1 that being mu that being Sigma squared and if the variance of is 1 of course Sigma is 1 as well.
4:46 As we will soon see we very often need to find areas under the standard normal curve.
Probabilities are simply areas under the curve and very often the question of interest involves finding a probability.
5:01 Finding probabilities and percentiles for the normal distribution requires integrating the probability density function.
There isn't a closed form solution and it must be integrated numerically.
Fortunately for us we're going to use software or a standard normal table to actually find these values in practice.
Good work! Ваш английский намного лучше моего русского.
I'm in Ghana and I'm loving jb stats. awesome work !!! keep it up
I'm glad I could be of help, and thanks for the compliment! All the best from Canada!
Thanks for helping me out on my finals. I'm a fan! :D You make statistics bearable! I finally understand after all these years.
Thanks! I'm glad you find them helpful.
Guelph, cool! I'm currently taking a stats course at the University of Waterloo and your videos have helped a lot. Nice to know you're so close!
its 2018, how was the ride??
@@derrickokello6277 you go to waterloo? lol the hustle is real
taking online courses rn cause of covid, havent gone to a single live lecture yet but still staying afloat thanks to these :P thank you!
You have the best stats videos around. Keep up the good work!
I was recently introduced to your channel. Just wanted to say thank you for the clear presentations.
You are very welcome. Thanks for the compliment!
You are very welcome! Best of luck on your exam.
Amazing explanation! It helped me a lot even after 10 years your videos are helping us thank you so much sir 🙏❤
I'm glad to be of help!
Thank you very much for your videos! I use them for business mathematics and statistics! I sent you around 30 student to this channel already :) (I also have to add that you speak easily understandable for not native speaker which is a big plus for my classmates in college!
I have a stats midterm test tmr, still dont know whether i can make it through but i grasp a lot from your channel and am more confident about the test. Thank you so much
I'm glad to be of help! Best of luck on your test!
Hey Mai, Its been 3 years but I'm curious. How did your test go?
these videos are such a blessing, thank you!
You are very welcome! I'm glad to be of help.
"And yes, I am Canadian" hahaha! Great video thanks :)
You are welcome Gary!
Yes I am Canadian. Does the JB stand for Justin Beiber then? :D
I like to think it stands for (Just Basic) like Just Basic Statistics. Because he explains it so well, it seems basic. :P
I think it's "Jeremy Balka" statistics since that's his name lol
@@supriyavangipuram7063 Seems likely...lol
Oh my GOSH!!!! It all somehow just clicked! You are the dude! DUDE!!!!
Thanks Laura! The base is a Latex/Beamer presentation, and I record and edit in Screenflow.
wow, u are doing some great karma in life. Very clear explanations. These videos are incredibly helpful. Thanks a lot sir.
wish you mental peace and physical well being
with metta loving kindness
Ravindra
You are welcome. And thanks for the well wishes!
Tanks man best tutorial I've seen bout this on TH-cam now wanna learn how to use it in data science
Thanks for the compliment! I'm glad to be of help.
So my guess was right with all those Canadian males questions.. you are one :D
Great channel.. I wish to meet you. Words fail me. I just can't express how you turned a subject I hated to the one I enjoy the most!
Cheers from India
Thanks a lot. It is amazingly fine. Keep it up. Getahun from Ethiopia
thanks from Egypt , you really helped me
Great Videos! I am revising the subject through your videos, very helpful! :)
"Yes I am canadian" loll...just killed it! although i knew that already. Great videos! only reason i am doing good in stats. So thank you!!
You are very welcome!
You teach better than my teacher thank you
thanks a lot!All your videos are helpfull.You never let me down.,,
Amazing videos,i love them. which tool/software are u using to write on the screen?
Things got so much clear now.. Thanks a lot :)
Thank you from Canada!
I don`t usually comment on videos, but after this `yes, I`m a Canadian` haha. We`re missing some `eh` eh? By the way, I love these videos very well explained. Please keep it going :)
Thanks for the compliment! We really do say 'eh', but I try to avoid it in the videos :)
Clear and simple...good job
You're the man, JB
thanks I'm passing my stats exams because of u
I'm glad to be of help!
4:25, and I realized you were Canadian before you said Zed... Thanks for your help professor..
turns out Zorro is Canadian
Thank you very much for these videos.
Please Upload some videos of Triangular Distributions
very good explanation and very helpful. thank you!
You are very welcome!
This was very well done.
Thanks!
You have just earned a subscriber my friend. Thank you a ton!
You are awesome for making this video thank you so much
Wonderful ......your videos are really helpful ....Iam kindly requesting more calculation ...,..specially part of poison and binomial
Thanks Brent!
Awesome Video clearly understand
I love Canada
+kalmuraqab Me too! It's a wonderful place :)
thank you for saving my life
You are very welcome!
I would watch a hundred ads for you king
these are great :)
what program are you using?
Love your videos!
Thanks!
You are the best... What writing board are you using? :-)
4:22 "with the letter Z, and yes I am Canadian" LOL 😂😂💔
If it makes you feel any better, we Indians also say Z as 'Zed'.(I found it weird at the beginning when I heard Americans say Z as 'Zee')
1:02 it has been so long since i did stats that when he said *mean* my mind registered it as *meme*
Very helpful. Thank you!
Great explanation
that was awesome Canadian! ;)
Thanks very much sir. Appreciated.
Hello sir! is there any difference between normal distribution and gaussian distribution ?
Btw your videos are awesome!! Thank you so much!
how do you create these video which tool did you use to make such kind of video.
Normal Distribution (also known as the Gaussian, Gaussian, or Laplace-Gaussian distribution) is a type of continuous probability distribution for a real-valued random variable.
Normal Distribution PDF: www.toevolution.com/blog/view/2274758/normal-distribution-pdf
Hi a small doubt
We normally plot Roys safety first ratio on Normal distribution for identifying shortfall risk
Can we also plot IRR on Normal Distribution Curve ,and say IRR=Max return a customer can earn beyond wich NPV will become negative(As per number scale)
So can I conclucdue
Because at this point NPV will be zero
should it be any "infinite value"? 5:26
eventhough, I am an arab(my english is not good),I understood this nicely.
"And yes, I'm canadian" haha
And making a step beck: How do I know that the data I collected are normally distributed? I do I came to the bell curve with my data?
+cinqueottavi I wouldn't call that a step back, I'd call that a step forward (i.e. I discuss that later in the course). A common way of assessing whether the observed data is approximately normally distributed is by plotting a normal quantile-quantile plot. I discuss those plots in this video: th-cam.com/video/X9_ISJ0YpGw/w-d-xo.html.
Thank you so much!
Zed and I am Canadian. Represent! From Quebec but Canadian nonetheless.
Very Nice... Than you...
Could you please make a video for Laplace distribution!
0:45 wtf is that
A function, yo.
wait what did being Canadian have to do with standard normal distribution and Z? just curious whether it was just randomly answering a question or something else more relevant xD
It has to do with the pronunciation of the letter Z. Americans call it "zee", whereas Canadians (and those in other areas of the world) call it "zed". When I taught at the University of Georgia, some students thought "zed" was a special statistics name for it. When I call it "zed" in the video, most of my American viewers will instantly recognize that I'm not American (if they hadn't figured it out yet from other clues).
Ahh that makes much more sense haha. Thank you so much for making these well explained videos. You're a great teacher and keep up the great work. (Y)
Thanks for the kind words!
"And yes I am Canadian" hahahahaha 😂 best country in the world!!
How did you make these illustrations, I plan to teach a course as well
check out the manim python library. A bit complex but definitely the best tool for the job
Is every data set following normal distribution ?
No, definitely not. Many variables have a distribution that is approximately normal, but definitely not all. For example, the distribution net worth of US households would be strongly skewed to the right.
D Histogram of Simulations from a Mixture of 3 Bivariate Gaussian Distributions, grazie. Stefano Caser
My saviour !!!
"And yes, I am Canadian."
I’m def failing my stats class and every other class business wasn’t a joke
Thank you Sir.
+jbstatistics i do not understand why x-u/sigma =1 ?
Saved my ass. Really thankful.
I'm glad I could be of help!