# Descriptive Statistics

Range, central tendency, standard deviation, variance, correlation

Descriptive statistics quantitatively describe features of a sample of data, such as the basic mean or standard deviation. Cumulative methods report a statistic as you move through the elements of an array. Moving methods report a statistic within a local window of array elements, then move to the next window.

## Functions

expand all

 `min` Minimum elements of an array `mink` Find `k` smallest elements of array `max` Maximum elements of an array `maxk` Find `k` largest elements of array `bounds` Smallest and largest elements `topkrows` Top rows in sorted order `mean` Average or mean value of array `median` Median value of array `mode` Most frequent values in array `std` Standard deviation `var` Variance `corrcoef` Correlation coefficients `cov` Covariance `xcorr` Cross-correlation `xcov` Cross-covariance
 `cummax` Cumulative maximum `cummin` Cumulative minimum
 `movmad` Moving median absolute deviation `movmax` Moving maximum `movmean` Moving mean `movmedian` Moving median `movmin` Moving minimum `movprod` Moving product `movstd` Moving standard deviation `movsum` Moving sum `movvar` Moving variance

## Topics

Computing with Descriptive Statistics

Analyze data with basic statistics.

Inconsistent Data

Identify outliers within data sets.

Linear Correlation

Covariance and correlation coefficients help to describe the linear relationship between variables.

Linear Regression

Least squares fitting is a common type of linear regression that is useful for modeling relationships within data.

Interactive Fitting

The Basic Fitting UI is an interactive data modeling tool.

Programmatic Fitting

There are many functions in MATLAB® that are useful for data fitting.