Suppose that the population distribution of a variable (X) has a mean (μ) and a standard deviation (σ). If you randomly draw an infinite number of samples from the population, and if the sample size (N) is big enough, then the distribution of the sample means (X) has a normal distribution with a mean (μx = μ) and a standard deviation (σx =σ/√N).

MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
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Chapter1: Starting With Matlab
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What does this explain? Sampling frame, Measure of central tendency, Sampling Distribution, Central Limit Theorem, or Confidence interval estimation

 

 

Suppose that the population distribution of a variable (X) has a mean (μ)
and a standard deviation (σ). If you randomly draw an infinite number of
samples from the population, and if the sample size (N) is big enough, then
the distribution of the sample means (X) has a normal distribution with a
mean (μx = μ) and a standard deviation (σx =σ/√N).
Transcribed Image Text:Suppose that the population distribution of a variable (X) has a mean (μ) and a standard deviation (σ). If you randomly draw an infinite number of samples from the population, and if the sample size (N) is big enough, then the distribution of the sample means (X) has a normal distribution with a mean (μx = μ) and a standard deviation (σx =σ/√N).
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