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6.1: Introduction to Confidence Intervals
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In conducting inferential statistics, we are interested in understanding facts about a population without studying the entire population. From our work with sampling distributions, we know that we cannot expect a sample statistic to equal the population parameter. Instead, we estimate the population parameter by developing an interval estimate, called a confidence interval, based on the sample statistics and the sampling distribution.
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6.2: Confidence Intervals for Proportions
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When we compute a confidence interval for a proportion, we first collect a random sample from our population, compute the proportion of that sample in order to estimate the proportion of the population. We recognize that our one sample's proportion is not likely to be the same value as the population's proportion, that the sample's measure will differ from that measure of the population. So, to determine the likely amount of error in a randomly chosen sample, we use our sampling distribution co
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6.3: Confidence Intervals for Means (Sigma Known)
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When you compute a confidence interval on the mean, you compute the mean of a sample in order to estimate the mean of the population. Clearly, if you already knew the population mean, there would be no need for a confidence interval. However, to explain how confidence intervals are constructed, we are going to work backwards and begin by assuming characteristics of the population. Then we will show how sample data can be used to construct a confidence interval.
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6.4: Confidence Interval for Means (Sigma Unknown)
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Having developed a construction technique for confidence intervals for mean with σ known, we now drop our simplifying assumption and address the common case when we do not know much about the population; i.e. we do not know the population mean or population standard deviation. We must know that the sampling distribution of sample means has either a normal parent population or the sample size n is greater than 30. Now we create our confidence intervals when population parameters are unknown.
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6.5: Confidence Intervals for Variances - Optional Material
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We now have a method of constructing confidence intervals for variances which is quite different from the forms for means and proportions. The sample statistic is no longer the center of the interval. Critical values still play an important role in the construction of the interval, and computing these critical values is the last aspect of construction that we need to hammer out. In the section on sampling distributions of sample variances, we introduced the CHISQ.DIST accumulation function.