ch8 - 8-1 Introduction In the previous chapter we...

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8-1 Introduction • In the previous chapter we illustrated how a arameter can be estimated from sample data parameter can be estimated from sample data. However, it is important to understand how good is e estimate obtained the estimate obtained. • Bounds that represent an interval of plausible values for a parameter are an example of an interval estimate . • Three types of intervals will be presented: Confidence intervals rediction intervals Prediction intervals • Tolerance intervals
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8-2 Confidence Interval on the Mean of a Normal Distribution, Variance Known 8-2.1 Development of the Confidence Interval and its Basic Properties
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8-2 Confidence Interval on the Mean of a Normal Distribution, Variance Known 8-2.1 Development of the Confidence Interval and its Basic Properties
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8-2 Confidence Interval on the Mean of a 1 Development of the Confidence Interval and its Normal Distribution, Variance Known 8-2.1 Development of the Confidence Interval and its Basic Properties The endpoints or bounds l and u are called lower- and upper- confidence limits , respectively. • Since Z follows a standard normal distribution, we can write:
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8-2 Confidence Interval on the Mean of a 1 Development of the Confidence Interval and its Normal Distribution, Variance Known 8-2.1 Development of the Confidence Interval and its Basic Properties Definition
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8-2 Confidence Interval on the Mean of a xample 8- Normal Distribution, Variance Known Example 8 1
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8-2 Confidence Interval on the Mean of a Normal Distribution, Variance Known Interpreting a Confidence Interval • The confidence interval is a random interval • The appropriate interpretation of a confidence interval (for example on μ ) is: The observed interval [ l , u ] brackets the true value of μ , with confidence 100(1- α ).
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This note was uploaded on 02/05/2012 for the course ST 315 taught by Professor Staff during the Fall '11 term at S. Alabama.

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ch8 - 8-1 Introduction In the previous chapter we...

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