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Mean

Mean[list]
gives the statistical mean of the elements in list.
Mean[dist]
gives the mean of the symbolic distribution dist.
  • Mean handles both numerical and symbolic data.
  • Mean[{{x1, y1, ...}, {x2, y2, ...}, ...}] gives {Mean[{x1, x2, ...}], Mean[{y1, y2, ...}]}.
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Basic Examples   (4)
Mean of numeric values:
Mean of symbolic values:
Means of elements in each column:
Mean of a symbolic lognormal distribution:
Mean of numeric values:
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Mean of symbolic values:
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Means of elements in each column:
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Mean of a symbolic lognormal distribution:
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Scope   (4)
Lists of integers yield rational numbers as means:
Numerical approximation:
Obtain results at any precision:
Compute results for a large vector or matrix:
Applications   (3)
Means of random reals approach a Gaussian distribution:
Theoretical curve:
Successive averages (Cesàro summation):
Mean values of cells in a sequence of steps of 2D cellular automaton evolution:
Properties & Relations   (13)
Mean is Total divided by Length:
Mean is equivalent to a 1-norm divided by Length for positive values:
For nearly symmetric samples, Mean and Median are nearly the same:
The Mean of absolute deviations from the Mean is MeanDeviation:
Mean is logarithmically related to GeometricMean for positive values:
Mean is the inverse of HarmonicMean of the inverse of the data:
The square root of Mean of the data squared is RootMeanSquare:
The n^(th) CentralMoment is the Mean of deviations raised to the n^(th) power:
Variance is a scaled Mean of squared deviations from the Mean:
ExpectedValue for a list is a Mean:
MovingAverage is a sequence of means:
A 0% TrimmedMean is the same as Mean:
The ExpectedValue of a random variable in a distribution is the Mean:
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