|Gauss' arithmetic geometric mean|
|Correlation between data samples|
|Compute the correlation matrix associated with a covariance matrix|
|Covariance of data samples|
|Clamp (winsorize) extremal values|
|Tally numerical data into classes and count frequencies|
|Geometric mean of a data sample|
|Harmonic mean of a data sample|
|Kurtosis (excess) of a data sample|
|Arithmetic mean of a data sample|
|Mean deviation of a data sample|
|Median value of a data sample|
|Modal (most frequent) value(s) in a data sample|
|The K-th moment of a data sample|
|Obliquity (skewness) of a data sample|
|Quadratic mean of a data sample|
|Standard deviation of a data sample|
|Variance of a data sample|
MuPAD® offers various data containers, such as lists, arrays, tables, and so on, to store and organize data.
Measures of central tendency locate a distribution of data along an appropriate scale.
The measures of dispersion summarize how spread out (or scattered) the data values are on the number line.
The measures of shape indicate the symmetry and flatness of the distribution of a data sample.
If you have two or more data samples with an equal number of elements, you can estimate how similar these data samples are.
The outliers are data points located far outside the range of the majority of the data.