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

Probability Distributions

Statistics Toolbox includes an extensive library of probability distribution functions that let you fit probability distributions to your data, compute functions from them, and generate random samples. From the command line you have access to more than 150 different functions for:

• Calculating the probability density function (pdf)
• Calculating the cumulative distribution function (cdf) and its inverse
• Computing mean and variance
• Estimating distribution parameters
• Generating random numbers

Statistics Toolbox includes three interactive graphical user interfaces (GUIs) that simplify common analysis tasks. The Distribution Fitting Tool GUI lets you fit data using 21 predefined probability distributions, a nonparametric (kernel-smoothing) estimator, or a custom distribution that you define yourself. It supports both complete and censored (reliability) data and lets you exclude data, save and load sessions, and generate M-code.

Fitting univariate distributions to data. The Distribution Fitting Tool lets you easily import, analyze, and plot your data. Click on image to see enlarged view.

The Distribution Tool GUI lets you learn about a variety of probability distributions and explore how parameters affect their position and shape.

The Random Number Tool GUI provides a random number generator to simulate behavior associated with particular distributions. You can use this random data to test hypotheses or models under different conditions, as well as perform Monte-Carlo simulations.

Statistics Toolbox also includes functions for generating random samples from multivariate distributions, such as t, normal, copulas, and Wishart; sampling from finite populations; performing Latin hypercube sampling; and generating samples from Pearson and Johnson systems of distributions.

Analysis of Variance

Analysis of variance (ANOVA) lets you determine whether data sets from different groups have different characteristics. You can classify groups using discrete predictor variables. A follow-up multiple comparisons analysis can pinpoint which pairs of groups differ from each other. Statistics Toolbox includes algorithms for ANOVA and related techniques, including:

• One-way ANOVA with graphics
• Two-way ANOVA for balanced data
• Multiway ANOVA for balanced and unbalanced data (both fixed and random effects)
• Analysis of covariance (ANOCOVA)
• One-way multivariate ANOVA
• Nonparametric one- and two-way ANOVA (Kruskal-Wallis, Friedman)
• Multiple comparison of group means, slopes, and intercepts

Analysis of covariance (ANOCOVA) tool, which plots data to assess group-to-group differences and the impact of a predictor variable on a response variable. Click on image to see enlarged view.