Matrix of scatter plots and histograms comparing automobile performance over three model years (left). Statistics Toolbox makes it easy to plot mul- tiple variables and compare data. Parallel coordinates plot of multivariate data describing throttle performance (right). Statistics Toolbox provides convenient tools for visualizing high-dimensional data.

Linear and Nonlinear Modeling

The toolbox provides nonlinear fitting func-tions for classification and regression trees and for nonlinear least squares. Using non- linear least squares functions, you can:Estimate parameters Interactively visualize and predict multidi-mensional nonlinear fittingSet confidence intervals for parameters and predicted valuesYou can also use the toolbox to work with Hidden Markov models. You can estimate the parameters of a model using the Baum- Welch algorithm, calculate the most likely path through a model using the Viterbi algorithm, and generate random sequences from a given model.

Multivariate Statistics

The linear and nonlinear models provided in Statistics Toolbox let you model a response variable as a function of one or more predic- tor variables. These models make predictions, establish relationships between variables, or simplify a problem. For example, linear and nonlinear regression models help establish which variables have the most impact on a response. Robust regression methods can help you find outliers and reduce their effect on the fitted model.The toolbox provides linear algorithms for:One-way, two-way, and multiway ANOVA Mixed random and fixed-effects ANOVA Polynomial, stepwise, ridge, robust, and multiple linear regressionGeneralized linear models, including multi-nomial (discrete-choice) modelsResponse surface fitting Multivariate statistics methods let you analyze your data by evaluating groups of variables together. You can:Segment data in clusters for further analysis Visualize and assess the group-to-group differences in a data setReduce a large set of variables to a more manageable but still representative setMultivariate statistics tasks supported by Statistics Toolbox include:Factor analysis Principal components analysis (PCA) Factor rotation and biplots Cluster analysis (both hierarchical and k-means)Discriminant analysis Multivariate ANOVA Multidimensional scaling (classical, metric, and nonmetric)Multivariate plotting (parallel coordinates, glyph plots, and Andrews plots) ••• ••••• • ••• •• • • •• •

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