notes 25

# notes 25 - Sampling Distribution of the Sample Mean xbar...

This preview shows pages 1–2. Sign up to view the full content.

bar Submitted by gfj100 on Wed, 11/11/2009 - 12:00 The central limit theorem states that if a large enough sample is taken (typically n > 30) then the sampling distribution of is approximately a normal distribution with a mean of μ and a standard deviation of . Since in practice we usually do not know μ or σ we estimate these by and respectively. In this case s is the estimate of σ and is the standard deviation of the sample. The expression is known as the standard error of the mean, labeled s.e.( ) Simulation: Generate 500 samples of size heights of 4 men. Assume the distribution of male heights is normal with mean m = 70" and standard deviation s = 3.0". Then find the mean of each of 500 samples of size 4. Here are the first 10 sample means: 70.4 72.0 72.3 69.9 70.5 70.0 70.5 68.1 69.2 71.8 Theory says that the mean of ( ) = μ = 70 which is also the Population Mean and SE( ) = = = 1.50. Simulation shows: Average (500

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

### Page1 / 4

notes 25 - Sampling Distribution of the Sample Mean xbar...

This preview shows document pages 1 - 2. Sign up to view the full document.

View Full Document
Ask a homework question - tutors are online