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Descriptive Statistics

Measures of central tendency, dispersion, shape, and correlation

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


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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