Statistics Toolbox

Key Features

  • 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
Next: Exploratory Data Analysis

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