Statistics: Sampling Distributions (02:41)
Statisticians use samples to make inferences about a larger population. To test the reliability of a sample, more samples are taken and the means established. The means are put into a distribution and the mean of the sample distribution is found.
Statistics: Central Limit Theorem (03:10)
The central limit theorem says that the mean of a normal sampling distribution will be nearly the same as the mean of the whole population. This theorem is demonstrated in a hypothetical situation.
Deviations for a Sampling Distribution: Reliability Measurement (04:07)
The standard deviation is equal to the square root of the variance. After finding the squares of all the deviations, it is possible to find the variance. Standard deviation can be used to compare the reliability of the means of different samples.
Statistics: Large Samples (03:19)
The two main types of estimators include point and interval estimators. Both types take a statistic from a sample and use it to calculate a value relative to the population parameter. Graphics illustrate how to test for reliability.
Statistics: Confidence Coefficient and Error of Estimation (03:34)
To build a confidence interval and see how accurate a sample mean really is, two things are required: confidence coefficient and error of estimation. Both are illustrated and explained.
How to Determine Error of Estimation (02:35)
To determine the error of estimation, first determine the confidence interval, find the z-score using the z-score table, and then complete the formula. The formula is illustrated and explained.
Statistics: Confidence Interval (04:01)
Once the error of estimation is determined, the confidence interval can be formed. This discussion includes the concepts of upper and lower limits of the confidence interval. This segment includes a review of material presented in this film.
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