Skip to content

Latest commit

 

History

History
16 lines (12 loc) · 859 Bytes

README.md

File metadata and controls

16 lines (12 loc) · 859 Bytes

Central-Limit-Theorem

It is the sampling distribution of the sampling means approaches a normal distribution as the sample size gets larger, no matter what the shape of the data distribution. An essential component of the Central Limit Theorem is the average of sample means will be the population mean. Similarly, if you find the average of all of the standard deviations in your sample, you will find the actual standard deviation for your population.

μ_sample = μ               # mean
σ_sample = σ / sqrt(n)     # standard deviation
σ_sample ^2 = σ^2 / n      # variance

Where, (n>= 30) and μ = Population mean, σ = Population standard deviation, μ_sample = Sample mean, σ_sample = Sample standard deviation, n = Sample size

Note:- Fully Commented code to get it better And Created using Colaboratory for more clearity. Thanks!