# Statistics and Estimation theory          Estimation theory is a branch of statistics that deals with estimating the values of parameters based on measured empirical data that has a random component.

- Estimation theory

These inferences may take the form of answering yes/no questions about the data (hypothesis testing), estimating numerical characteristics of the data (estimation), describing associations within the data (correlation), and modeling relationships within the data (for example, using regression analysis).

- Statistics 2 related topics ## Estimator   In statistics, an estimator is a rule for calculating an estimate of a given quantity based on observed data: thus the rule (the estimator), the quantity of interest (the estimand) and its result (the estimate) are distinguished.

Estimation theory is concerned with the properties of estimators; that is, with defining properties that can be used to compare different estimators (different rules for creating estimates) for the same quantity, based on the same data. ## Expected value

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{{Redirect|E(X)|the For a different example, in statistics, where one seeks estimates for unknown parameters based on available data, the estimate itself is a random variable.

To empirically estimate the expected value of a random variable, one repeatedly measures observations of the variable and computes the arithmetic mean of the results.