Statistics Toolbox™ provides algorithms and tools for organizing, analyzing, and modeling data. You can use regression or classification for predictive modeling, generate random numbers for Monte Carlo simulations, use statistical plots for exploratory data analysis, and perform hypothesis tests.
For analyzing multidimensional data, Statistics Toolbox includes algorithms that let you identify key variables that impact your model with sequential feature selection, transform your data with principal component analysis, apply regularization and shrinkage, or use partial least-squares regression.
Statistics Toolbox includes specialized data types for organizing and accessing heterogeneous data. Dataset arrays store numeric data, text, and metadata in a single data container. Built-in methods enable you to merge datasets using a common key (join), calculate summary statistics on grouped data, and convert between tall and wide data representations. Categorical arrays provide a memory-efficient data container for storing information drawn from a finite, discrete set of categories.
Statistical arrays for storing heterogeneous and categorical data
Regression techniques, including linear, nonlinear, robust, and ridge, and nonlinear mixed-effects models
Classification algorithms, including boosted and bagged decision trees, k-Nearest Neighbor, and linear discriminant analysis
Analysis of variance (ANOVA)
Probability distributions, including copulas and Gaussian mixtures
Random number generation
Design of experiments and statistical process control