Introduction :: Organizing Data (Statistics Toolbox™)

Introduction

MATLAB data is placed into "data containers" in the form of workspace variables. All workspace variables organize data into some form of array. For statistical purposes, arrays are viewed as tables of values.

MATLAB variables use different structures to organize data:

2-D numerical arrays (matrices) organize observations and measured variables by rows and columns, respectively. (See Other Data Structures in the MATLAB documentation.)
Multidimensional arrays organize multidimensional observations or experimental designs. (See Multidimensional Arrays in the MATLAB documentation.)
Cell and structure arrays organize heterogeneous data of different types, sizes, units, etc. (See Cell Arrays and Structures in the MATLAB documentation.)

Data types determine the kind of data variables contain. (See Classes (Data Types) in the MATLAB documentation.)

These basic MATLAB container variables are reviewed, in a statistical context, in the section on MATLAB Arrays.

These variables are not specifically designed for statistical data, however. Statistical data generally involves observations of multiple variables, with measurements of heterogeneous type and size. Data may be numerical (of type single or double), categorical, or in the form of descriptive metadata. Fitting statistical data into basic MATLAB variables, and accessing it efficiently, can be cumbersome.

Statistics Toolbox software offers two additional types of container variables specifically designed for statistical data:

Categorical Arrays accommodate data in the form of discrete levels, together with its descriptive metadata.
Dataset Arrays encapsulate heterogeneous data and metadata, including categorical data, which is accessed and manipulated using familiar methods analogous to those for numerical matrices.

These statistical container variables are discussed in the section on Statistical Arrays.

Organizing Data

MATLAB Arrays

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