Introduction—Introduces PV-WAVE IMSL Statistics and covers some of the basic concepts found in this guide.

Basic Statistics—Discusses univariate summary statistics, frequency tables, and rank and order statistics.

Regression—Discusses stepwise regression, all best regression, multiple linear regression models, polynomial models and nonlinear models.

Correlation and Covariance—Discusses sample variance-covariance,partial correlation and covariances, pooled variance-covariance and robust estimates of a covariance matrix and mean factor.

Analysis of Variance and Designed Experiments—Discusses one-way classification models, a balanced factorial design with fixed effects and the Student-Newman-Keuls multiple comparisons test.

Categorical and Discrete Data Analysis—Discusses chi-squared analysis of a two-way contingency table, exact probabilities in a two-way contingency table and analysis of categorical data using general linear models.

Nonparametric Statistics—Discusses sign tests, Wilcoxon sum tests and Cochran Q test for related observations.

Goodness of Fit—Discusses chi-squared goodness-of-fit tests, Kolmogorov/Smirnov tests and tests for normality.

Time Series and Forecasting—Discusses analysis and forecasting of time series using a nonseasonal ARMA model, GARCH (Generalized Autoregressive Conditional Heteroskedasticity), Kalman filtering, Automatic Model Selection, Bayesian Seasonal Analysis and Prediction, Optimum Controller Design, Spectral Density Estimation, portmanteau lack of fit test and difference of a seasonal or nonseasonal time series.

Multivariate Analysis—Discusses principal components and factor analysis.

Survival Analysis—Discusses Kaplan-Meier estimates of survival probabilities.

Probability Distribution Functions and Inverses—Discusses binomial, hypergeometric, bivariate normal, gamma and many more.

Random Number Generation—Discusses the Mersenne Twister generator and a generator for multivariate normal distributions and pseudorandom numbers from several distributions, including gamma, Poisson, beta, and low discrepancy sequence.

Data Mining—Discusses genetic algorithms, Naive Bayes functions, and forecasting, classification, and statistical pattern recognition using neural networks.

Utilities—Discusses machine, mathematical, physical constants, retrieval of machine constants and customizable error handling.

References—Lists the references used in this document.

Summary of Routines—Lists a summary of the routines referenced in this document.