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Interval Estimation

Interval Estimation. Method. Inductive Reasoning Generalizing from a specific idea/notion/data-point Start: a statistic is used as a “best point estimate” for a corresponding parameter, when it is computed using a random sample of data .

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Interval Estimation

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  1. Interval Estimation

  2. Method • Inductive Reasoning • Generalizing from a specific idea/notion/data-point • Start: a statistic is used as a “best point estimate” for a corresponding parameter, when it is computed using a random sample of data. • To generalize, we add and subtract a “margin for error” that creates a range of values for the parameter. This range is called the interval estimate • The width of the interval estimate is the product of 2 things: • The level of confidence, AND • The standard error of the statistic

  3. Assumptions • Data sample must be randomly selected • The size of the sample ought to be as large as possible, and should contain at least 30 observations—according to Central Limit Theorem. • If a sample is either not randomly selected or not large enough, then an interval estimate may fail to yield a valid generalization about a parameter’s value.

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