ALLBEST Procedure on page 85—Selects the best multiple linear regression models.

ANCOVAR Function on page 208—Analyzes a one-way classification model with covariates.

ANOVA1 Function on page 202—Analyzes one-way classification model.

ANOVABALANCED Function on page 240—Balanced fixed, random, or mixed model.

ANOVAFACT Function on page 224—Analyzes a balanced factorial design with fixed effects.

ANOVANESTED Function on page 232—Nested random mode.

ARMA Function on page 489—Computes method-of-moments or least-squares estimates of parameters for a nonseasonal ARMA model.

AUTO_ARIMA Function on page 534—Automatically identifies time series outliers, determines parameters of a multiplicative seasonal ARIMA model and produces forecasts that incorporate the effects of outliers whose effects persist beyond the end of the series.

AUTOCORRELATION Function on page 573—Sample autocorrelation function.

AUTO_UNI_AR Function on page 509—Automatic selection and fitting of a univariate autoregressive time series model.

BETA Function on page 1116—Evaluate the complete beta function.

BETACDF Function on page 738—Evaluates the beta probability distribution function.

BETAI Function on page 1118—Evaluate the real incomplete beta function.

BINOMIALCDF Function on page 728—Evaluates the binomial distribution function.

BINOMIALCOEF Function on page 1115—Evaluate binomial coefficient.

BINOMIALPDF Function on page 730—Evaluates the binomial probability function.

BINORMALCDF Function on page 740—Evaluates the bivariate normal distribution function.

BOXCOXTRANS Function on page 563—Perform Box-Cox transformation

CAT_GLM Function on page 396—Generalized linear models.

CLUSTER_HIERARCHICAL Procedure on page 622—Performs a hierarchical cluster analysis given a distance matrix.

CLUSTER_NUMBER Function on page 627—Computes cluster membership for a hierarchical cluster tree.

CHI_SQUARED_NORMALITY_TEST Function on page 458—Performs a chi-squared test for normality.

CHISQCDF Function on page 742—Evaluates the chi-squared distribution function. Using a keyword, the inverse of the chi-squared distribution can be evaluated.

CHISQTEST Function on page 448—Performs a chi-squared goodness-of-fit test.

CMAST_ERR_PRINT Function on page 1127—Set option for error printing.

CMAST_ERR_STOP Function on page 1126—Set option for error recovery.

CMAST_ERR_TRANS Function on page 1125—Determines if an Informational Error has occurred.

COCHRANQ Function on page 440—Cochran's Q test.

COMPLEMENTARY_F_CDF Function on page 751—Evaluates the complement of the F distribution function.

COMPLEMENTARY_T_CDF Function on page 776—Evaluates the complement of the Student’s t distribution.

CONT_TABLE Procedure on page 829—Sets up a table to generate pseudorandom numbers from a general continuous distribution.

CONTINGENCY Function on page 377—Performs a chi-squared analysis of a two-way contingency table.

COVARIANCES Function on page 166—Computes the sample variance-covariance or correlation matrix.

CRD_FACTORIAL Function on page 253—Analyzes data from balanced and unbalanced completely randomized experiments.

CROSSCORRELATION Function on page 567—Computes the sample cross-correlation function of two stationary time series.

CSTRENDS Function on page 425—Cox and Stuarts’ sign test for trends in location and dispersion.

DIFFERENCE Function on page 550—Differences a seasonal or nonseasonal time series.

DISCR_ANALYSIS Procedure on page 649—Perform discriminant function analysis.

DISCR_TABLE Function on page 832—Sets up a table to generate pseudorandom numbers from a general discrete distribution.

DISSIMILARITIES Function on page 618—Computes a matrix of dissimilarities (or similarities) between the columns (or rows) of a matrix.

EMPIRICAL_QUANTILES Function on page 12—Computes empirical quantiles.

ESTIMATE_MISSING Function on page 593—Estimates missing values in a time series.

EXACT_ENUM Function on page 390—Exact probabilities in a table; total enumeration.

EXACT_NETWORK Function on page 392—Exact probabilities in a table.

FACTOR_ANALYSIS Function on page 640—Extracts initial factor-loading estimates in factor analysis.

FAURE_INIT Function on page 843—Initializes the structure used for computing a shuffled Faure sequence.

FAURE_NEXT_PT Function on page 845—Generates shuffled Faure sequence.

FCDF Function on page 749—Evaluates the F distribution function. Using a keyword, the inverse of the F distribution function can be evaluated.

FREQTABLE Function on page 24—Tallies observations into a one-way frequency table.

FRIEDMANS_TEST Function on page 436—Friedman’s test.

GA_CHROMOSOME Function on page 870—Creates a data structure containing unencoded and encoded phenotype information.

GA_DECODE Function on page 885—Decodes an individual’s chromosome into its binary, nominal, integer and real phenotypes.

GA_ENCODE Function on page 886—Encodes an individual’s binary, nominal, integer and real phenotypes into its chromosome.

GA_GROW_POPULATION Function on page 907—Adds the individuals in the array individual to an existing population.

GA_INDIVIDUAL Function on page 878—Creates a data structure from user supplied phenotypes.

GA_MERGE_POPULATION Function on page 908—Creates a new population by merging two populations with identical chromosome structures.

GA_MUTATE Function on page 883—Performs the mutation operation on an individual’s chromosome.

GA_POPULATION Function on page 887—Creates a population data structure from user supplied individuals.

GA_RANDOM_POPULATION Function on page 896—Creates a population data structure from randomly generated individuals.

GAMMA_ADV Function on page 1120—Evaluate the real gamma function.

GAMMACDF Function on page 760—Evaluates the gamma distribution function.

GAMMAI Function on page 1122—Evaluate incomplete gamma function.

GARCH Function on page 601—Compute estimates of the parameters of a GARCH(p,q) model

GENETIC_ALGORITHM Function on page 909—Optimizes a user-defined fitness function using a tailored genetic algorithm.

HOMOGENEITY Function on page 356—Conducts Bartlett’s and Levene’s tests of the homogeneity of variance assumption in analysis of variance.

HYPERGEOCDF Function on page 732—Evaluates the hypergeometric distribution function.

HYPERGEOPDF Function on page 734—Evaluates the hypergeometric probability function.

HYPOTH_PARTIAL Function on page 123—Constructs an equivalent completely testable multivariate general linear hypothesis HβU = G from a partially testable hypothesis HpβU = Gp.

HYPOTH_SCPH Function on page 129—Computes the matrix of sums of squares and crossproducts for the multivariate general linear hypothesis HβU = G given the regression fit.

HYPOTH_TEST Function on page 133—Performs tests for a multivariate general linear hypothesis HβU = G given the hypothesis sums of squares and crossproducts matrix SH.

K_MEANS Function on page 631—Performs a K-means (centroid) cluster analysis.

KALMAN Procedure on page 605—Performs Kalman filtering and evaluates the likelihood function or the state-space model.

KAPLAN_MEIER_ESTIMATES Function on page 663—Computes Kaplan-Meier estimates of survival probabilities in stratified samples.

KOLMOGOROV1 Function on page 463—One-sample continuos data Kolmogorov-Smirnov.

KOLMOGOROV2 Function on page 466—Two-sample continuos data Kolmogorov-Smirnov.

KTRENDS Function on page 442—K-sample trends test.

KW_TEST Function on page 433—Kruskal-Wallis test.

LACK_OF_FIT Function on page 590—Lack-of-fit test based on the corrleation function

LATIN_SQUARE Function on page 271—Analyzes data from latin-square experiments.

LATTICE_DESIGN Function on page 280— Analyzes balanced and partially-balanced lattice experiments.

LIFE_TABLES Function on page 718—Produces population and cohort life tables.

LILLIEFORS_NORMALITY_TEST Function on page 455—Performs a Lilliefors test for normality.

LNBETA Function on page 1119—Evaluate the log of the real beta function.

LNGAMMA Function on page 1123—Evaluate the logarithm of the absolute value of the gamma function.

LNORMREGRESS Function on page 148—Fits a multiple linear regression model using criteria other than least squares. Namely, LNORMREGRESS allows the user to choose Least Absolute Value (L1), Least Lp norm (Lp), or Least Maximum Value (Minimax or L∞) method of multiple linear regression.

MACHINE Function on page 1107—Returns information describing the computer’s arithmetic.

MAX_ARMA Function on page 504—Exact maximum likelihood estimation of the parameters in a univariate ARMA (autoregressive, moving average) time series model.

MLFF_CLASSIFICATION_TRAINER Function on page 1032—Trains a multilayered feedforward neural network.

MLFF_INITIALIZE_WEIGHTS Function on page 997—Initializes weights for multilayered feedforward neural networks prior to network training using one of four user selected methods.

MLFF_NETWORK Function on page 991—Links and modifies a multilayered feedforward neural network.

MLFF_NETWORK_FORECAST Function on page 1025—Calculates forecasts for trained multilayered feedforward neural networks.

MLFF_NETWORK_INIT Function on page 990—Creates a multilayered feedforward neural network.

MLFF_NETWORK_TRAINER Function on page 1013—Trains a multilayered feedforward neural network using quasi-Newton backpropagation.

MLFF_PATTERN_CLASSIFICATION Function on page 1063—Trains a multilayered feedforward neural network for classification.

MULTICOMP Function on page 362—Performs Student-Newman-Keuls multiple-comparisons test.

MULTI_CROSS Function on page 577—Computes the multichannel cross-correlation function of two mutually stationary multichannel time series.

MULTIPREDICT Function on page 78—Computes predicted values, confidence intervals, and diagnostics after fitting a regression model.

MULTIREGRESS Function on page 63—Fits a multiple linear regression model using least squares and optionally compute summary statistics for the regression model.

MULTIVARIATE_NORMAL_CDF Function on page 765—Evaluates the cumulative distribution function for the multivariate normal distribution.

MVAR_NORMALITY Function on page 469—Mardia’s test for multivariate normality.

NAIVE_BAYES_CLASSIFICATION Function on page 956—Classifies unknown patterns using a previously trained Naive Bayes classifier.

NAIVE_BAYES_TRAINER Function on page 935—Trains a Naive Bayes classifier.

NCTRENDS Function on page 422—Noehter’s test for cyclical trend.

NON_CENTRAL_F_CDF Function on page 757—Evaluates the noncentral F cumulative distribution function (CDF).

NON_CENTRAL_F_PDF Function on page 753—Evaluates the noncentral F probability density function (PDF).

NON_CENTRAL_CHI_SQ_PDF Function on page 746—Evaluates the noncentral chi-squared probability density function.

NONLINOPT Function on page 140—Fits data to a nonlinear model (possibly with linear constraints) using the successive quadratic programming algorithm (applied to the sum of squared errors, sse = Σ(yi − f(xi; θ))2) and either a finite difference gradient or a user-supplied gradient.

NONLINREGRESS Function on page 115—Fits a nonlinear regression model.

NONPARAM_HAZARD_RATE Function on page 710—Performs nonparametric hazard rate estimation using kernel functions and quasi-likelihoods.

NORM1SAMP Function on page 14—Computes statistics for mean and variance inferences using a sample from a normal population.

NORM2SAMP Function on page 18—Computes statistics for mean and variance inferences using samples from two independently normal populations.

NORMALCDF Function on page 763—Evaluates the standard normal (Gaussian) distribution function. Using a keyword, the inverse of the standard normal (Gaussian) distribution can be evaluated.

NORMALITY Function on page 460—Performs a test for normality.

PARTIAL_AC Function on page 587—Sample partial autocorrelation function

PARTIAL_COV Function on page 170—Partial correlations and covariances.

POISSONCDF Function on page 735—Evaluates the Poisson distribution function.

POISSONPDF Function on page 737—Evaluates the probability function of a Poisson random variable with parameter theta.

POLYPREDICT Function on page 108—Computes predicted values, confidence intervals, and diagnostics after fitting a polynomial regression model.

POLYREGRESS Function on page 102—Performs a polynomial least-squares regression.

POOLED_COV Function on page 175—Pooled covariance matrix.

PRINC_COMP Function on page 635—Computes principal components.

PROP_HAZARDS_GEN_LIN Function on page 669—Analyzes survival and reliability data using Cox’s proportional hazards model.

RAND_GEN_CONT Function on page 830—Generates pseudorandom numbers from a general continuous distribution.

RAND_GEN_DISCR Function on page 836—Generates pseudorandom numbers from a general discrete distribution using an alias method or optionally a table lookup method.

RANDOM Function on page 796—Generates pseudorandom numbers. The default distribution is a uniform (0, 1) distribution, but many different distributions can be specified through the use of keywords.

RANDOM_ARMA Function on page 839—Generate pseudorandom ARMA process numbers

RAND_FROM_DATA Function on page 826—Generates pseudorandom numbers from multivariate distribution determined from a given sample.

RANDOM_NPP Function on page 816—Generates pseudorandom numbers from a nonhomogeneous Poisson process.

RANDOM_ORDER Function on page 819—Generates pseudorandom order statistics from a standard normal distribution.

RAND_ORTH_MAT Function on page 822—Generates a pseudorandom orthogonal matrix or a correlation matrix

RANDOM_SAMPLE Function on page 824—Generates a simple pseudorandom sample from a finite population

DISCR_TABLE Function on page 832—Sets or retrieves the current table used in either the shuffled or GFSR random number generator

RAND_TABLE_2WAY Function on page 821—Generates a pseudorandom two-way table.

RANDOM_MT32_INIT Procedure on page 849—Initializes the 32-bit Mersenne Twister generator using an array.

RANDOM_MT64_INIT Procedure on page 851—Initializes the 64-bit Mersenne Twister generator using an array.

RANDOMNESS_TEST Function on page 472—Runs test, Paris-serial test, d2 test or triplets tests.

RANDOMOPT Procedure on page 787—Uses keywords to set or retrieve the random number seed or to select the uniform (0, 1) multiplicative, congruential pseudorandom-number generator.

RANKS Function on page 36—Computes the ranks, normal scores, or exponential scores for a vector of observations.

RCBD_FACTORIAL Function on page 263—Analyzes data from balanced and unbalanced randomized complete-block experiments.

REGRESSORS Function on page 56—Generates regressors for a general linear model.

ROBUST_COV Function on page 178—Robust estimate of covariance matrix.

SCALE_FILTER Function on page 1081—Scales or unscales continuous data prior to its use in neural network training, testing, or forecasting.

SEASONAL_FIT Function on page 555—Estimates the optimum seasonality parameters for a time series using an autoregressive model, AR(p), to represent the time series.

SHAPIRO_WILK_NORMALITY_TEST Function on page 453—Performs the Shapiro-Wilk test for normality.

SIGNTEST Function on page 411—Performs a sign test.

SIMPLESTAT Function on page 7—Computes basic univariate statistics.

SORTDATA Function on page 30—Sorts observations by specified keys, with option to tally cases into a multiway frequency table.

SPLIT_PLOT Function on page 298—Analyzes a wide variety of split-plot experiments with fixed, mixed or random factors.

SPLIT_SPLIT_PLOT Function on page 310—Analyzes data from split-split-plot experiments.

STATDATA Function on page 1112—Retrieves commonly analyzed data sets.

STEPWISE Procedure on page 94—Builds multiple linear regression models using forward, backward, or stepwise selection.

STRIP_PLOT Function on page 326—Analyzes data from strip-plot experiments.

STRIP_SPLIT_PLOT Function on page 337—Analyzes data from strip-split-plot experiments.

SURVIVAL_GLM Function on page 685—Analyzes survival data using a generalized linear model and estimates using various parametric modes.

TCDF Function on page 773—Evaluates the Student’s t distribution function.

TIE_STATS Function on page 431—Tie statistics.

TIME_SERIES_CLASS_FILTER Function on page 1090—Converts time series data sorted within nominal classes in decreasing chronological order to a useful format for processing by a neural network.

TIME_SERIES_FILTER Function on page 1087—Converts time series data to the format required for processing by a neural network.

TS_OUTLIER_FORECAST Function on page 525—Computes forecasts, their associated probability limits and y weights for an outlier contaminated time series whose underlying outlier free series follows a general seasonal or nonseasonal ARMA model.

TS_OUTLIER_IDENTIFICATION Function on page 514—Detects and determines outliers and simultaneously estimates the model parameters in a time series whose underlying outlier free series follows a general seasonal or nonseasonal ARMA model.

UNSUPERVISED_NOMINAL_FILTER Function on page 1094—Converts nominal data into a series of binary encoded columns for input to a neural network.

UNSUPERVISED_ORDINAL_FILTER Procedure on page 1098—Converts ordinal data into proportions.

WILCOXON Function on page 414—Performs a Wilcoxon rank sum test.

YATES Function on page 364—Estimates missing observations in designed experiments using Yate’s method.