Statistics Toolbox™ provides statistical and machine learning 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. The toolbox provides supervised and unsupervised machine learning algorithms, including support vector machines (SVMs), boosted and bagged decision trees, k-means and hierarchical clustering, k-nearest neighbor search, Gaussian mixtures, and hidden Markov models.
Regression techniques, including linear, generalized linear, nonlinear, robust, regularized, ANOVA, and mixed-effects models
Repeated measures modeling for data with multiple measurements per subject
Univariate and multivariate probability distributions, including copulas and Gaussian mixtures
Random and quasi-random number generators and Markov chain samplers
Hypothesis tests for distributions, dispersion, and location, and design of experiments (DOE) techniques for optimal, factorial, and response surface designs
Supervised machine learning algorithms, including support vector machines (SVMs), boosted and bagged decision trees, k-nearest neighbor, Naïve Bayes, and discriminant analysis
Unsupervised machine learning algorithms, including k-means and hierarchical clustering, Gaussian mixtures, and hidden Markov models