Procedures

Computes the adjusted R-squared value for a data set.

Computes the Allan variance of a data set.

Performs an analysis of variance (ANOVA) on the supplied data set.

Computes Bartlett's test statistic and associated probability.

Computes the beta function.

Performs a bootstrap calculation on the supplied data set for the given statistic. The default implementation utlizes a random resampling with replacement. Other resampling methods may be defined by specifying an appropriate routine by means of the method input.

Generates random, normally distributed values via the Box-Muller transform.

Computes statistics for the quality of fit for a regression model.

Computes the confidence interval for the specified distribution.

Computes the sample correlation coefficient (an estimate to the population Pearson correlation) as follows.

Computes the sample covariance of two data sets.

Computes the covariance matrix $C$ where $C = \left( X^{T} X \right)^{-1}$ and $X$ is computed by design_matrix.

Computes the design matrix $X$ for the linear least-squares regression problem of $X \beta = y$, where $X$ is the matrix computed here, $\beta$ is the vector of coefficients to be determined, and $y$ is the vector of measured dependent variables.

Computes the difference between elements in an array.

Computes the digamma function.

Fits a Taylor series model to the provided data.

Computes the F-test and returns the probability (two-tailed) that the variances of two data sets are not significantly different.

Computes the factorial of X.

Computes a table with values scaled from 1 to N describing a full-factorial design.

Computes the appropriate size for a full-factorial design table.

Computes the incomplete beta function.

Computes the lower incomplete gamma function.

Computes the upper incomplete gamma function.

Computes the Jacobian matrix for a nonlinear regression problem.

Computes Levene's test statistic and associated probability.

Computes a linear least-squares regression to fit a set of data.

Computes the smoothing of a data set using a robust locally weighted scatterplot smoothing (LOWESS) algorithm. Fitted values are computed at each of the supplied x values.

Computes the mean of the values in an array.

Computes the median of the values in an array.

Performs a nonlinear regression to fit a model using a version of the Levenberg-Marquardt algorithm.

Computes the pooled estimate of variance.

Computes the specified quantile of a data set using the SAS Method 4.

Computes the R-squared value for a data set.

Random resampling, with replacement, based upon a normal distribution.

Computes the regularized beta function.

Uses rejection sampling to randomly sample a target distribution.

Reports an array size error.

Reports an error relating to two arrays not being the same size when they should be the same size.

Reports an iteration count error.

Reports a matrix size error.

Reports a memory allocation related error.

Reports an underdefined problem error.

Estimates the sample size required to achieve an experiment with the desired power and significance levels to ascertain the desired difference in parameter.

A random resampling, scaled by the standard deviation of the original data, but based upon a normal distribution.

Computes the sample standard deviation of the values in an array.

Computes the 2-tailed Student's T-Test for two data sets of assumed equivalent variances.

Computes the 2-tailed Student's T-Test for two paired data sets.

Computes the 2-tailed Student's T-Test for two data sets of assumed non-equivalent variances.

Computes the trimmed mean of a data set.

Computes the sample variance of the values in an array.