Bourgeois Learning Objectives Upon successful completion of this chapter, you will be able to:

What Is Central Limit Theorem?

For practical purposes, the main idea of the central limit theorem CLT is that the average of a sample of observations drawn from some population with any shape-distribution is approximately distributed as a normal distribution if certain conditions are met.

In theoretical statistics there are several versions of the central limit theorem depending on how these conditions are specified. These are concerned with the types of assumptions made about the distribution of the It based information systems leading to improved population population from which the sample is drawn and the actual sampling procedure.

One of the simplest versions of the theorem says that if is a random sample of size n say, n larger than 30 from an infinite population, finite standard deviationthen the standardized sample mean converges to a standard normal distribution or, equivalently, the sample mean approaches a normal distribution with mean equal to the population mean and standard deviation equal to standard deviation of the population divided by the square root of sample size n.

In applications of the central limit theorem to practical problems in statistical inference, however, statisticians are more interested in how closely the approximate distribution of the sample mean follows a normal distribution for finite sample sizes, than the limiting distribution itself.

Sufficiently close agreement with a normal distribution allows statisticians to use normal theory for making inferences about population parameters such as the mean using the sample mean, irrespective of the actual form of the parent population. It is well known that whatever the parent population is, the standardized variable will have a distribution with a mean 0 and standard deviation 1 under random sampling.

Moreover, if the parent population is normal, then it is distributed exactly as a standard normal variable for any positive integer n.

It is generally not possible to state conditions under which the approximation given by the central limit theorem works and what sample sizes are needed before the approximation becomes good enough.

As a general guideline, statisticians have used the prescription that if the parent distribution is symmetric and relatively short-tailed, then the sample mean reaches approximate normality for smaller samples than if the parent population is skewed or long-tailed.

In this lesson, we will study the behavior of the mean of samples of different sizes drawn from a variety of parent populations.

Examining sampling distributions of sample means computed from samples of different sizes drawn from a variety of distributions, allow us to gain some insight into the behavior of the sample mean under those specific conditions as well as examine the validity of the guidelines mentioned above for using the central limit theorem in practice.

Under certain conditions, in large samples, the sampling distribution of the sample mean can be approximated by a normal distribution. The sample size needed for the approximation to be adequate depends strongly on the shape of the parent distribution.

Symmetry or lack thereof is particularly important. For a symmetric parent distribution, even if very different from the shape of a normal distribution, an adequate approximation can be obtained with small samples e.

For symmetric short-tailed parent distributions, the sample mean reaches approximate normality for smaller samples than if the parent population is skewed and long-tailed. In some extreme cases e. For some distributions without first and second moments e.

Many problems in analyzing data involve describing how variables are related. The simplest of all models describing the relationship between two variables is a linear, or straight-line, model.

The simplest method of fitting a linear model is to "eye-ball'' a line through the data on a plot. A more elegant, and conventional method is that of "least squares", which finds the line minimizing the sum of distances between observed points and the fitted line.

Realize that fitting the "best'' line by eye is difficult, especially when there is a lot of residual variability in the data.

Know that there is a simple connection between the numerical coefficients in the regression equation and the slope and intercept of regression line. Know that a single summary statistic like a correlation coefficient does not tell the whole story.

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