Documentation

# Descriptive Statistics

Measures of central tendency, dispersion, shape, and correlation

### Note

MuPAD® notebooks will be removed in a future release. Use MATLAB® live scripts instead.

To convert a MuPAD notebook file to a MATLAB live script file, see `convertMuPADNotebook`. MATLAB live scripts support most MuPAD functionality, although there are some differences. For more information, see Convert MuPAD Notebooks to MATLAB Live Scripts.

## MuPAD Functions

 `numeric::gaussAGM` Gauss' arithmetic geometric mean `stats::correlation` Correlation between data samples `stats::correlationMatrix` Compute the correlation matrix associated with a covariance matrix `stats::covariance` Covariance of data samples `stats::cutoff` Discard outliers `stats::winsorize` Clamp (winsorize) extremal values `stats::frequency` Tally numerical data into classes and count frequencies `stats::geometricMean` Geometric mean of a data sample `stats::harmonicMean` Harmonic mean of a data sample `stats::kurtosis` Kurtosis (excess) of a data sample `stats::mean` Arithmetic mean of a data sample `stats::meandev` Mean deviation of a data sample `stats::median` Median value of a data sample `stats::modal` Modal (most frequent) value(s) in a data sample `stats::moment` The K-th moment of a data sample `stats::obliquity` Obliquity (skewness) of a data sample `stats::quadraticMean` Quadratic mean of a data sample `stats::stdev` Standard deviation of a data sample `stats::variance` Variance of a data sample

## Topics

Store Statistical Data

MuPAD offers various data containers, such as lists, arrays, tables, and so on, to store and organize data.

Compute Measures of Central Tendency

Measures of central tendency locate a distribution of data along an appropriate scale.

Compute Measures of Dispersion

The measures of dispersion summarize how spread out (or scattered) the data values are on the number line.

Compute Measures of Shape

The measures of shape indicate the symmetry and flatness of the distribution of a data sample.

Compute Covariance and Correlation

If you have two or more data samples with an equal number of elements, you can estimate how similar these data samples are.

Handle Outliers

The outliers are data points located far outside the range of the majority of the data.

Bin Data

The `stats::frequency` function categorizes the numerical data into a number of bins given by semiopen intervals (ai, bi].

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