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.