Central_Limit_Theorem

Central_Limit_Theorem - The Central Limit Theorem Author...

Info iconThis preview shows page 1. Sign up to view the full content.

View Full Document Right Arrow Icon
The Central Limit Theorem Author: John M. Cimbala, Penn State University Latest revision: 20 August 2007 Introduction It is rare that one can measure something for an entire population – instead, a sample (or several samples) of the population is measured, and the population statistics are estimated from the sample. The Central Limit Theorem is an extremely useful tool when dealing with multiple samples. Multiple samples and the Central Limit Theorem Consider a population of random variable x (we assume that variations in x are purely random – in other words, if we would plot a PDF of variable x , it would look Gaussian or normal). The population mean μ and the population standard deviation σ are not known, but are instead estimated by taking several samples. We take N samples, each of which contains n measurements of variable x , as indicated in the sketch to the right. We define the sample mean for sample I as 1 1 n I i i x x n = = , where index I = 1, 2, 3, N (one for each sample). In other words, we calculate a sample mean in the
Background image of page 1
This is the end of the preview. Sign up to access the rest of the document.

This note was uploaded on 04/05/2008 for the course ME 345 taught by Professor Staff during the Spring '08 term at Penn State.

Ask a homework question - tutors are online