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# Descriptive Statistics

Numerical summaries and associated measures

Compute descriptive statistics from sample data, including measures of central tendency, dispersion, shape, correlation, and covariance. Tabulate and cross-tabulate data, and compute summary statistics for grouped data.

## Functions

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 `geomean` Geometric mean `harmmean` Harmonic mean `trimmean` Mean, excluding outliers `kurtosis` Kurtosis `moment` Central moment `skewness` Skewness
 `range` Range of values `iqr` Interquartile range `mad` Mean or median absolute deviation `prctile` Percentiles of a data set `quantile` Quantiles of a data set `zscore` Standardized z-scores
 `corr` Linear or rank correlation `robustcov` Robust multivariate covariance and mean estimate `cholcov` Cholesky-like covariance decomposition `corrcov` Convert covariance matrix to correlation matrix `partialcorr` Linear or rank partial correlation coefficients `partialcorri` Partial correlation coefficients adjusted for internal variables `nearcorr` Compute nearest correlation matrix by minimizing Frobenius distance
 `grpstats` Summary statistics organized by group `tabulate` Frequency table `crosstab` Cross-tabulation `tiedrank` Rank adjusted for ties

## Topics

Exploratory Analysis of Data

Explore the distribution of data using descriptive statistics.

Measures of Central Tendency

Locate a distribution of data along an appropriate scale.

Measures of Dispersion

Find out how spread out the data values are on the number line.

Quantiles and Percentiles

Learn how the Statistics and Machine Learning Toolbox™ computes quantiles and percentiles.

Grouping Variables

Grouping variables are utility variables used to group or categorize observations.

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