With regression, you can model a continuous response variable as a function of one or more predictors. Statistics Toolbox offers a wide variety of regression algorithms, including:
You can evaluate goodness of fit using a variety of metrics, including:
With the toolbox, you can calculate confidence intervals for both regression coefficients and predicted values.
Statistics Toolbox supports more advanced techniques to improve predictive accuracy when the dataset includes large numbers of correlated variables. The toolbox supports:
Statistics Toolbox also supports nonparametric regression techniques for generating an accurate fit without specifying a model that describes the relationship between the predictor and the response. Nonparametric regression techniques include decision trees as well as boosted and bagged regression trees.
Develop a predictive model without specifying a function that describes the relationship between variables.
Additionally, Statistics Toolbox supports nonlinear mixed-effect (NLME) models in which some parameters of a nonlinear function vary across individuals or groups.
Classification algorithms enable you to model a categorical response variable as a function of one or more predictors. Statistics Toolbox offers a wide variety of parametric and nonparametric classification algorithms, such as:
You can evaluate goodness of fit for the resulting classification models using techniques such as:
Analysis of variance (ANOVA) enables you to assign sample variance to different sources and determine whether the variation arises within or among different population groups. Statistics Toolbox includes these ANOVA algorithms and related techniques: