Central Limit Theorem & Sampling Distribution Concepts | Statistics Tutorial | MarinStatsLectures
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- Опубликовано: 3 окт 2024
- Central Limit Theorem and Sampling Distribution Concepts: What does the central limit theorem (CLT) tell us? What is a normal sampling distribution? what is the Standard Error of the mean? Learn these concepts and more with examples! 👉🏼Link to Web Visualization Tool ( bit.ly/2XDHr87 ); Normal Distribution with R Video: ( • Normal Distribution, Z... ) 👍🏼Best Statistics & R Programming Language Tutorials: ( goo.gl/4vDQzT )
►► Like to support us? You can Donate (bit.ly/2CWxnP2), Share our Videos, Leave us a Comment and Give us a Like! Either way, We Thank You!
In this statistics video tutorial, we will learn the concept of central limit theorem (CLT), the sampling distribution of the mean and the standard deviation of the mean using examples. We will also build up the concept of the standard error of the mean.
The central limit theorem (CLT) tells us that, under certain conditions, the sampling distribution of the mean is approximately normally distributed. The sampling distribution helps us understand what sorts of sample estimates are likely to show up when we collect some data if we knew the true values for their entire population.
We will work through some of the calculations, although the focus will be on the concept of central limit theorem (CLT) and Sampling Distribution of the Mean, not the calculations, as the calculations are simply mechanical, and can usually be done using the software.
►► Watch More:
► Intro to Statistics Course (Complete Course): bit.ly/2SQOxDH
►Data Science with R Complete Course): bit.ly/1A1Pixc
►Getting Started with R (Series 1): bit.ly/2PkTneg
►Graphs and Descriptive Statistics in R (Series 2): bit.ly/2PkTneg
►Probability distributions in R (Series 3): bit.ly/2AT3wpI
►Bivariate analysis in R (Series 4): bit.ly/2SXvcRi
►Linear Regression in R (Series 5): bit.ly/1iytAtm
►ANOVA Concept and with R bit.ly/2zBwjgL
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Content Creator: Mike Marin (B.Sc., MSc.) Senior Instructor at UBC.
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These videos are created by #marinstatslectures to support some statistics and R programming language courses at The University of British Columbia (UBC) (#IntroductoryStatistics and #RVideoTutorials for Health Science Research), although we make all videos available to the everyone everywhere for free.
Thanks for watching! Have fun and remember that statistics is almost as beautiful as a unicorn!
👋🏼 Hi everyone! In this video we will lean the concept of the Central Limit Theorem (CLT) and Sampling Distribution in statistics. We will also build up the concept of the Standard Error of the Mean. If you like to support us, you can Donate (bit.ly/2CWxnP2), Share our Videos, Leave us a Comment and Give us a Like 👍🏼! Either way We Thank You!
love your work
Hands down, the best statistics lectures on YT that I have found!
Apart from wonderful videos you make cute endings 😂😂. I like this approach. It refreshes the mind after stats and keeps one happy and positive throughout the series.
Thanks! Our son wanted to contribute to our videos :)
I am working my way through your videos before beginning a statistics class this summer to hopefully get a better grasp of the concepts in advance. Thanks for the videos!
hats off sir. Starting from very basics of stats to operating all functions in R is incredible.
Best lectures of stats....better then many paid courses out there....thank you sir...
i like the conceptual approach you have here. I have to revisit this topic over 50 times for the past 20 years and still forgot about it until now it is more clear !!! Very appreciate for the great lecture
Many variables to handle. Mr Marin is a good teacher, but the topic is indeed challenging. I had to revisit it 3 times before getting comfortable with it.
Simply amazing sir!! Thanks for your great efforts to make every concept so easy to understand. And of course, thanks to the little one too who makes me smile every time I end a video:)
No errors here! Great Video! Thanks Marin.
Sir you made statistics very easy for me, very usefull course, Even in paid courses we cannot see such kind of explanation,
Hey Marin.
Recently came across your playlist.
Very detailed, yet very simple to grasp.
Thanks.
this part was very confusing but thanks to you I finally get it
great Visualization ....awesome video
Extremely important and helpful video. Thanks for your tremendous effort
You put out some really nice videos ... thank you!
than you sir . your lectures are amazing !
thanks for the video, and yeah loved the ending))
Thank you... finally I understand
YOU ARE JUST GREAT 🤯🤯😜🥰👍🏻👍🏻👍🏻
awesome videos！
awesome!
thanks :)
Will you put a number on all lectures?
Hi Mike,
I really appreciate your work.
Is there any chance you could make the study material available for the concepts you teach here. It would be very helpful.
Hi, not at the moment, but i am working on taking a lot of the material i use in my courses, and trying to revise it (and remove any of the course-specific content) so that i can make it available.
I don't get the coin analogy at all, can somebody explain why throwing a coin 100 times and expecting 50 to be head is as impossible as expecting the Sample Mean of 25 to equal the population Mean of 125? I get that that the population mean is a average sum of all Sample means. But how is that relating to a coin toss??? I cant even imagine how to calculate a Sample Mean for 100 tries for a coin toss.
How does the skewness of the distribution affect the sampling distribution of the mean? Even though the SBP is skewed to the right, are we assuming that the distribution is approximately normal, so that the CLT (as stated at 2:22 ) works?
Suppose the distribution is not approximately normal, and is skewed heavily to the right, will the sampling distribution of the mean also have a similar shape?
The sampling distribution of the mean is always normal, regardless of the population distribution.
So... If I watched a sample of RUclips videos -- say... 2700-- that hopefully will explain Statistics in simple terms... I would "expect" One fucking video to help me. What is the probability that I have stumbled on another video that sucks and doesn't explain shit, goes to fast, or otherwise is not helpful.
in science (and statistics especially) we use data to help in our decision making. having watched such a large number of videos, and still failing to grasp the concepts suggests that videos may not be the most effective learning tool for yourself, and you may want to consider alternate approaches to learning, as you have plenty of data to suggest that this approach isn't working
@@marinstatlectures thug life.