Statistics Toolbox™ 6.2

Product Description

• Statistics Toolbox Key Features
• Data Management and Descriptive Statistics
• Probability Distributions and Analysis of Variance
• Linear and Nonlinear Modeling and Multivariate Statistics
• Design of Experiments
• Hypothesis Testing and Statistical Process Control

Linear and Nonlinear Modeling

The linear and nonlinear models provided in Statistics Toolbox let you model a response variable as a function of one or more predictor 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 regression
• Generalized linear models, including multinomial (discrete-choice) models
• Response surface fitting

The toolbox provides nonlinear fitting functions for classification and regression trees and for nonlinear least squares. Using nonlinear least squares functions, you can:

• Estimate parameters
• Interactively visualize and predict multidimensional nonlinear fitting
• Set confidence intervals for parameters and predicted values

You 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.

Fitting a decision tree to data. The fitting capabilities in Statistics Toolbox let you visualize a decision tree by drawing a diagram of the decision rule and group assignments. Click on image to see enlarged view.

Multivariate Statistics

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 set
• Reduce a large set of variables to a more manageable but still representative set

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

Multivariate 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)

Plots showing how data can be clustered into groups with similar characteristics. Statistics Toolbox includes functions for multivariate analysis and clustering. Click on image to see enlarged view.