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 based upon a normal distribution. Other resampling methods may be defined by specifying an appropriate routine by means of the method input.

Utilizes the Box-Muller transformation approach to generate the requested number of random samples from a normal distribution of the specified mean and standard deviation.

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

Generates a central composite design in coded variables.

Computes the size of a central composite design.

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.

Converts coded variable values (-1, +1) to natural scale.

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 model diagnostics and goodness-of-fit metrics.

Computes predictions with confidence and prediction intervals.

Computes residual analysis data for model diagnostics.

Converts natural variable values to coded (-1, +1) scale.

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.

Generates a 2-level fractional factorial design.

Computes the size of a fractional factorial design.

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.

Generates a Latin hypercube design for factor space exploration.

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 based upon a normal distribution.

Random resampling with replacement from the supplied sample.

Computes the regularized beta function.

Uses rejection sampling to randomly sample a target distribution.

Samples a multivariate normal distribution such that $\vec{x} = \vec{mu} + L \vec{u}$, where $L$ is the lower form of the Cholesky factorization of the covariance matrix, and $\vec{u}$ is a randomly generated vector that exists on the set $[-1 1]$

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

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.