Statistics Toolbox

Exploratory Data Analysis

Statistics Toolbox provides multiple ways to explore data: statistical plotting with interactive graphics, algorithms for cluster analysis, and descriptive statistics for large data sets.

Statistical Plotting and Interactive Graphics

Statistics Toolbox includes graphs and charts to visually explore your data. The toolbox augments MATLAB® plot types with probability plots, box plots, histograms, scatter histograms, 3D histograms, control charts, and quantile-quantile plots. The toolbox also includes specialized plots for multivariate analysis, including dendrograms, biplots, parallel coordinate charts, and Andrews plots.

Group scatter plot matrix showing interactions between five variables.
Group scatter plot matrix showing interactions between five variables.

Visualizing Multivariate Data (Example)
How to visualize multivariate data using various statistical plots.

Compact box plot with whiskers for response grouped by year to look for potential year-specific fixed effects.
Compact box plot with whiskers for response grouped by year to look for potential year-specific fixed effects.
Scatter histogram using a combination of scatter plots and histograms to describe the relationship between variables.
Scatter histogram using a combination of scatter plots and histograms to describe the relationship between variables.
Plot comparing the empirical CDF for a sample from an extreme value distribution with a plot of the CDF for the sampling distribution.
Plot comparing the empirical CDF for a sample from an extreme value distribution with a plot of the CDF for the sampling distribution.

Modelling Data with the Generalized Extreme Value Distribution (Example)
How to fit the generalized extreme value distribution using maximum likelihood estimation.

Descriptive Statistics

Descriptive statistics enable you to understand and describe potentially large sets of data quickly. Statistics Toolbox includes functions for calculating:

These functions help you summarize values in a data sample using a few highly relevant numbers.

Resampling Techniques

In some cases, estimating summary statistics using parametric methods is not possible. To deal with these cases, Statistics Toolbox provides resampling techniques, including:

  • Random sampling from a dataset with or without replacement
  • Generalized bootstrap function for estimating sample statistics using resampling
  • jackknife function for estimating sample statistics using subsets of the data
  • bootci function for estimating confidence intervals
Next: Regression and ANOVA

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