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The arithmetic mean of a series of values ,, …, is often denoted by placing an "overbar" over the symbol, e.g. ¯, pronounced "bar". Some commonly used symbols for sample statistics are given below: the sample mean ¯,
A mean is a quantity representing the "center" of a collection of numbers and is intermediate to the extreme values of the set of numbers. [1] There are several kinds of means (or "measures of central tendency") in mathematics, especially in statistics.
¯ = sample mean of differences d 0 {\displaystyle d_{0}} = hypothesized population mean difference s d {\displaystyle s_{d}} = standard deviation of differences
The arithmetic mean is often denoted by a bar (vinculum or macron), as in ¯. [4] Some software (text processors, web browsers) may not display the "x̄" symbol correctly. For example, the HTML symbol "x̄" combines two codes — the base letter "x" plus a code for the line above ( ̄ or ¯). [8]
the population mean or expected value in probability and statistics; a measure in measure theory; micro-, an SI prefix denoting 10 −6 (one millionth) Micrometre or micron (retired in 1967 as a standalone symbol, replaced by "μm" using the standard SI meaning) the coefficient of friction in physics; the service rate in queueing theory
Average of chords. In ordinary language, an average is a single number or value that best represents a set of data. The type of average taken as most typically representative of a list of numbers is the arithmetic mean – the sum of the numbers divided by how many numbers are in the list.
A mathematical symbol is a figure or a combination of figures that is used to represent a mathematical object, an action on mathematical objects, a relation between mathematical objects, or for structuring the other symbols that occur in a formula. As formulas are entirely constituted with symbols of various types, many symbols are needed for ...
The sample mean and sample covariance are not robust statistics, meaning that they are sensitive to outliers. As robustness is often a desired trait, particularly in real-world applications, robust alternatives may prove desirable, notably quantile-based statistics such as the sample median for location, [4] and interquartile range (IQR) for ...