Statistics Using Python Tutorial Part 8 | Central Limit Theorem | Data Science Tutorial #8

Statistics Using Python Tutorial Part 8 | Central Limit Theorem | Data Science Tutorial #8
Hello and welcome back to another session of statistics tutorial using Python Powered by Acadgild. In the previous video, you have learned the Types of distributions in statistics that is mainly about Bernoulli’s theorem and how to implement it using jupyter. If you have missed the previous, please check the links as follows.
In this video, you will be able to learn the central limit theorem and sample distribution.
The Central Limit Theorem states that sampling distribution of the mean of any independent or random variable will be normal if the sample size is large. So how large is called as large enough. According to the rule of thumb, the size should be 30 it also requires accuracy. The more closely the sample distribution needs to resemble a normal distribution the more sample points are required. The closer to the original population resembles the normal population the fewer sample points are required.
Kindly, go through the complete video to learn the implementation part in the jupyter notebook. Please like, share and subscribe the channel for more tutorials.
#centrallimittheorem, #Statistics, #datascience

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[RajaSekharaReddyKaluri]

I'm confused.
Does n represent total number of samples taken or does it represent size of each sample?

Please reply.