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Descriptive Statistics

Measures of central tendency, dispersion, shape, and correlation

MuPAD Functions

numeric::gaussAGMGauss' arithmetic geometric mean
stats::correlationCorrelation between data samples
stats::correlationMatrixCompute the correlation matrix associated with a covariance matrix
stats::covarianceCovariance of data samples
stats::cutoffDiscard outliers
stats::winsorizeClamp (winsorize) extremal values
stats::frequencyTally numerical data into classes and count frequencies
stats::geometricMeanGeometric mean of a data sample
stats::harmonicMeanHarmonic mean of a data sample
stats::kurtosisKurtosis (excess) of a data sample
stats::meanArithmetic mean of a data sample
stats::meandevMean deviation of a data sample
stats::medianMedian value of a data sample
stats::modalModal (most frequent) value(s) in a data sample
stats::momentThe K-th moment of a data sample
stats::obliquityObliquity (skewness) of a data sample
stats::quadraticMeanQuadratic mean of a data sample
stats::stdevStandard deviation of a data sample
stats::varianceVariance 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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