Lines Matching defs:mean

13 mean                Arithmetic mean (average) of data.
14 fmean               Fast, floating point arithmetic mean.
15 geometric_mean      Geometric mean of data.
16 harmonic_mean       Harmonic mean of data.
26 Calculate the arithmetic mean ("the average") of data:
28 >>> mean([-1.0, 2.5, 3.25, 5.75])
67 If you have previously calculated the mean, you can pass it as the optional
71 >>> mu = mean(data)
116     'mean',
209     """Return the exact mean and sum of square deviations of sequence data.
213     If given *c* is used the mean; otherwise, it is calculated from the data.
414 def mean(data):
415     """Return the sample arithmetic mean of data.
417     >>> mean([1, 2, 3, 4, 4])
421     >>> mean([F(3, 7), F(1, 21), F(5, 3), F(1, 3)])
425     >>> mean([D("0.5"), D("0.75"), D("0.625"), D("0.375")])
432         raise StatisticsError('mean requires at least one data point')
437     """Convert data to floats and compute the arithmetic mean.
439     This runs faster than the mean() function and it always returns a float.
475     """Convert data to floats and compute the geometric mean.
489         raise StatisticsError('geometric mean requires a non-empty dataset '
494     """Return the harmonic mean of data.
496     The harmonic mean is the reciprocal of the arithmetic mean of the
518     errmsg = 'harmonic mean does not support negative values'
756 # mean=0.300.  Only the latter (which corresponds with R6) gives the
763 # choice.  Instead of the mean, it uses the mode of the beta
826     values. The optional argument xbar, if given, should be the mean of
827     the data. If it is missing or None, the mean is automatically calculated.
838     If you have already calculated the mean of your data, you can pass it as
841     >>> m = mean(data)
845     This function does not check that ``xbar`` is actually the mean of
870     value. The optional argument mu, if given, should be the mean of
871     the data. If it is missing or None, the mean is automatically calculated.
883     If you have already calculated the mean of the data, you can pass it as
886     >>> mu = mean(data)
944     """In one pass, compute the mean and sample standard deviation as floats."""
1176         '_mu': 'Arithmetic mean of a normal distribution',
1181         "NormalDist where mu is the mean and sigma is the standard deviation."
1193         "Generate *n* samples for a given mean and standard deviation."
1274         """Compute the Standard Score.  (x - mean) / stdev
1277         above or below the mean of the normal distribution.
1285     def mean(self):
1286         "Arithmetic mean of the normal distribution."