Descriptive Statistics and Insights

Summarize data using descriptive statistics and regression models

Descriptive statistics quantitatively describe features of a data set. You can interpret and summarize your data using descriptive statistics such as mean, median, standard deviation, and percentiles. You can also assess the relationships between variables by analyzing correlations and fitting regression models.

Functions

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Basic Statistics

Central Tendency

`mean`	Average or mean value of array
`median`	Median value of array
`mode`	Most frequent values in array
`rms`	Root mean square value

Extremes

`min`	Minimum elements of array
`mink`	Find `k` smallest elements of array
`max`	Maximum elements of array
`maxk`	Find `k` largest elements of array
`bounds`	Minimum and maximum values of an array

Dispersion and Spread

`std`	Standard deviation
`var`	Variance
`prctile`	Percentiles of data set
`quantile`	Quantiles of data set
`iqr`	Interquartile range of data set

Moving and Cumulative Statistics

Moving Statistics

`movmin`	Moving minimum
`movmax`	Moving maximum
`movmean`	Moving mean
`movmedian`	Moving median
`movmad`	Moving median absolute deviation
`movstd`	Moving standard deviation
`movvar`	Moving variance
`movsum`	Moving sum
`movprod`	Moving product

Cumulative Statistics

`cummax`	Cumulative maximum
`cummin`	Cumulative minimum
`cumsum`	Cumulative sum
`cumprod`	Cumulative product

Data Summaries

`summary`	Data summary
`pivot`	Summarize tabular data in pivoted table (Since R2023a)
`groupsummary`	Compute summary statistics by group

Regression and Prediction

Covariance and Correlation

`cov`	Covariance
`corrcoef`	Correlation coefficients
`xcov`	Cross-covariance
`xcorr`	Cross-correlation

Data Fitting

`mldivide`	Solve systems of linear equations Ax = B for x
`mrdivide`	Solve systems of linear equations xA = B for x
`polyfit`	Polynomial curve fitting
`polyval`	Polynomial evaluation

Forecasting Metrics

`rmse`	Root mean squared error between arrays (Since R2022b)
`mape`	Mean absolute percentage error between arrays (Since R2022b)

Data Distributions

`kde`	Kernel density estimate for univariate data (Since R2023b)
`histogram`	Histogram plot
`histcounts`	Histogram bin counts

Topics

Statistics

Compute Mean, Median, and Other Descriptive Statistics
Summarize data by computing one or more simple descriptive statistics.
Compare Global, Moving, and Cumulative Statistics
Get data insights by computing and visualizing global, moving, and cumulative statistics.
Explore Basic Statistics on 2-D Plot
Interactively summarize data by computing, visualizing, and saving descriptive statistics for 2-D plotted data using the Data Statistics tool.

Regression and Prediction

Linear Correlation
Covariance and correlation coefficients help to describe the linear relationship between variables.
Linear Regression with One Predictor Variable
Fit and evaluate a first-order and a second-order linear regression model for one predictor variable and one response variable using polyfit and polyval.
Linear Regression with Nonpolynomial Terms
Fit a nonpolynomial linear regression model for one predictor variable and one response variable by constructing a design matrix and using the backslash operator (\).
Linear Regression with Multiple Predictor Variables
Fit a polynomial linear regression model for multiple predictor variables and one response variable by constructing a design matrix and using the backslash operator (\).
Interactively Fit Data and Visualize Model
Interactively fit and validate polynomial regression models for 2-D plotted data by using the Basic Fitting tool. Then, use the models to interpolate and extrapolate the data.

Featured Examples

Identify and Visualize Correlated Variables

Calculate, filter, rank, and visualize correlations between variables.

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