@@ -501,9 +501,9 @@ However, for reading convenience, most of the examples show sorted sequences.
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variance indicates that the data is spread out; a small variance indicates
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it is clustered closely around the mean.
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- If the optional second argument *mu * is given, it is typically the mean of
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- the *data *. It can also be used to compute the second moment around a
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- point that is not the mean. If it is missing or ``None `` (the default),
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+ If the optional second argument *mu * is given, it should be the * population *
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+ mean of the *data *. It can also be used to compute the second moment around
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+ a point that is not the mean. If it is missing or ``None `` (the default),
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the arithmetic mean is automatically calculated.
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Use this function to calculate the variance from the entire population. To
@@ -573,8 +573,8 @@ However, for reading convenience, most of the examples show sorted sequences.
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the data is spread out; a small variance indicates it is clustered closely
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around the mean.
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- If the optional second argument *xbar * is given, it should be the mean of
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- *data *. If it is missing or ``None `` (the default), the mean is
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+ If the optional second argument *xbar * is given, it should be the * sample *
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+ mean of *data *. If it is missing or ``None `` (the default), the mean is
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automatically calculated.
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Use this function when your data is a sample from a population. To calculate
@@ -590,8 +590,8 @@ However, for reading convenience, most of the examples show sorted sequences.
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>>> variance(data)
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1.3720238095238095
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- If you have already calculated the mean of your data, you can pass it as the
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- optional second argument *xbar * to avoid recalculation:
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+ If you have already calculated the sample mean of your data, you can pass it
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+ as the optional second argument *xbar * to avoid recalculation:
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.. doctest ::
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