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. If your data contains
NaN) values, MATLAB® arithmetic operation functions return
However, specialized functions available in Statistics and Machine Learning Toolbox™ ignore these missing values and return a numerical value
calculated using the remaining values. For more information, see Data with Missing Values.
|Linear or rank correlation|
|Robust multivariate covariance and mean estimate|
|Cholesky-like covariance decomposition|
|Convert covariance matrix to correlation matrix|
|Linear or rank partial correlation coefficients|
|Partial correlation coefficients adjusted for internal variables|
|Covariance ignoring |
|Compute nearest correlation matrix by minimizing Frobenius distance|
Explore the distribution of data using descriptive statistics.
Compute descriptive statistics while ignoring missing values.
Locate a distribution of data along an appropriate scale.
Find out how spread out the data values are on the number line.
Learn how the Statistics and Machine Learning Toolbox computes quantiles and percentiles.
Grouping variables are utility variables used to group or categorize observations.