Lab, Seminar, Lecture 4.
Behavior of the sample average
X-bar
The topic of 4th seminar&lab is the average of the
population that has a certain characteristic. This average is the population parameter of interest, denoted by the greek letter mu. We estimate this parameter with the statistic x-bar, the average in the sample.
Probability and statistics - Karol Flisikowski
X-bar Definition
1 x xi n i 1
Probability and statistics - Karol Flisikowski
n
Sampling Distribution of x-bar
How does x-bar behave? To study the behavior,
imagine taking many random samples of size n, and computing an x-bar for each of the samples. Then we plot this set of x-bars with a histogram.
Probability and statistics - Karol Flisikowski
Sampling Distribution of x-bar
Probability and statistics - Karol Flisikowski
Central Limit Theorem
The key to the behavior of x-bar is the central limit
theorem. It says: Suppose the population has mean, m, and standard deviation s. Then, if the sample size, n, is large enough, the distribution of the sample mean, x-bar will have a normal shape, the center will be the mean of the original population, m, and the standard deviation of the x-bars will be s divided by the square root of n.
Probability and statistics - Karol Flisikowski
Central Limit Theorem
If the CLT holds we have,
Normal shape
Center = mu
Spread = sigma/sqroot n.
Probability and statistics - Karol Flisikowski
When Does CLT Hold?
Answer generally depends on the sample size, n,
and the shape of the original distribution. General Rule: the more skewed the population distribution of the data, the larger sample size is needed for the CLT to hold.
Probability and statistics - Karol Flisikowski
CLT
Previous overhead shows the original population distribution in (a),
and increasing sample sizes through graphs (b), (c), and (d). Notice that it takes