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Parameter

Is a number that describes the population.

Statistic

a number that can be computed from the sample data without making use if any unknown parameters.

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Law of large Numbers

the larger the sample is the more accurately it is to the population mean. Draw observation at random form any population with finite mean. As the number of observations drawn increases, the mean of the observed values get closer to the men of the populations

Population distribution

of a variable is the distribution of values of the variable among all the individuals in the population

Sampling distribution

is the distribution of values taken by the statistic in all possible samples of the same size from the same population

Mean and standard deviation of a sample

The mean of a sample of and SRS of size n drawn form a large population with mean mu and standard deviation sigma. The sampling deviation of the mean has a mean mu and standard deviation of sigma/sqrt(n)

Unbiased estimator

Because the mean X bar is equal to mu. There is no systematic tendency

Central Limit theorem

when *n *is large the sampling distribution of the sample mean is approximately normal.

Sample means

means of random samples are less variable than individual observations are more normal than indivdual observations

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