Statistics Toolbox

Data Organization and Management

Statistics Toolbox provides two specialized arrays for statistical data: dataset arrays and categorical arrays.

An Introduction to Dataset Arrays 5:31
Improve productivity with statistical arrays

Dataset Arrays

Dataset arrays enable convenient organization and analysis of heterogeneous statistical data and metadata. Dataset arrays have columns that represent measured variables and rows that represent observations. With dataset arrays, you can:

• Collect variables of different data types and sizes in a single array
• Use metadata to describe variables and observations and to access them by name
• View summary statistics and display data in an intuitive tabular format
• Create, manage, and operate on dataset arrays using a variety of supporting methods

An Introduction to Joins 5:01
Combine fields from two dataset arrays using a variable that is present in both

Categorical Arrays

Categorical arrays let you organize and process categorical data that takes on values from a finite set of discrete levels or categories. With categorical arrays, you can:

• Store nominal data using descriptive labels, such as "red," "green," and "blue" for an unordered set of colors
• Store ordinal data using descriptive labels, such as "cold," warm," and "hot" for an ordered set of temperature measurements
• Manipulate categorical data using familiar array operations and indexing methods
• Index into other variables or create subsets of data based on the category of observation
• Group observations of the same category for computing statistics and creating visualizations

Descriptive Statistics

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

• Measures of central tendency (measures of location), including average, median, and various means
• Measures of dispersion (measures of spread), including range, variance, standard deviation, and mean or median absolute deviation
• Linear and rank correlation (partial and full)
• Results based on data with missing values
• Percentile and quartile estimates
• Density estimates (using a kernel-smoothing function)

These functions help you summarize the values in a data sample with a few highly relevant numbers. Although descriptive statistics are typically generated using parametric techniques, you can also use bootstrap methods to derive descriptive statistics.

Statistical Plotting and Interactive Graphics

Statistics Toolbox includes numerous functions that help you represent your data graphically. In addition to the standard set of MATLAB plot types, Statistics Toolbox includes box plots, probability plots, histograms and 3-D histograms, control charts, quantile-quantile plots, and several multivariate plots. It also provides interactive graphics that enhance analysis in areas such as:

• Nonlinear and polynomial fitting and prediction
• Exploration of distribution functions and distribution fitting and analysis
• Interactive random number generation
• Response surface modeling
• Interactive process experimentation and analysis
• Stepwise regression analysis

Regression analysis to determine the most important ingredients for cement-mixture curing. Stepwise regression capabilities in Statistics Toolbox provide automated procedures for identifying models from several potential explanatory variables.