Notes pg 730 - Notes pg 730-769 I. Confidence Interval for...

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Notes pg 730-769 I. Confidence Interval for the Mean a. We can’t simply calculate a sample mean and assume that is equals the mean of the population. A sample mean might equal the mean of the population, but we can’t assume that it will. b. There are two different approaches to the construction of confidence intervals for the mean 1. One approach is used when we know the value of the population standard deviation 2. Another approach is used when we don’t know the value of the population standard deviation. (This one is used more frequently) II. The Central Limit Theorem tells us that the standard error of the sampling distribution will equal the standard deviation of the population divided by the square root of our sample size. a. Ex: the standard deviation for the general population is 100. Thus, we divide 100 by the square root of our sample size to get the value of the standard error. b. We can obtain the standard error of the mean by dividing the standard deviation (100) by the square root of our sample size (15). Thus : 100/15 = 6.67. c. With this formula, it is possible for us to miss our mark 1. This method that we use will produce an interval that contains the true mean of the population 99 times out of 100 (99% of the time). 2. If it were repeated 100 times, we’d find ourselves working with many different sample means, which would result in different final answers.
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This note was uploaded on 07/12/2010 for the course MIS 311 taught by Professor Cleveland during the Spring '10 term at University of Texas at Austin.

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Notes pg 730 - Notes pg 730-769 I. Confidence Interval for...

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