The Statistical Excel Add-Ins are organized in the form of 'books'. Set 1 - Modelling and Multivariate Methods - includes:
Functionality found in Set 2 of NAG's Statistical Add-Ins for Excel, Nonparametric Statistics, can viewed here.
The following tables list the available Statistical Excel Add-Ins (Set 1) functions, a short description and the reference to the associated NAG routine which contains detailed background information.
||Returns, as a single value, a one or two tail probability for the standard Normal distribution.
||Returns, as a single value, the deviate associated with the given probability of the standard Normal distribution.
||Returns, as a single value, the upper tail, lower tail or central probability associated with a multivariate Normal distribution of up to ten dimensions.
||Returns, as a single value, the lower tail, upper tail or two tail probability for the Student's t-distribution.
||Returns, as a single value, the deviate associated with the given tail probability of Student's t-distribution.
||Returns, as a single value, the lower or upper tail probability for the X² distribution.
||Returns, as a single value, the deviate associated with the given lower probability of the X² distribution.
||Returns, as a single value, the lower or upper tail of the F or variance-ratio distribution.
||Returns, as a single value, the deviate associated with the given lower tail probability, of the F or variance-ratio distribution.
||Returns, as a single value, the lower or upper tail probability of the beta distribution.
||Returns, as a single value, the deviate associated with the given lower tail probability of the beta distribution.
||Returns, as a single value, the lower or upper tail probability of the gamma distribution.
||Returns, as a single value, the deviate associated with the given lower tail probability of the gamma distribution.
||Calculates the mean, standard deviation, coefficients of skewness and kurtosis, and the maximum and minimum values for a set of ungrouped data. Weighting may be used.
||Calculates a five-point summary for a single sample.
||Computes chi-squared statistics for a two-way contingency table. For a 2 × 2 table with 40 or fewer observations an exact probability is computed.
||Calculates the product-moment correlation matrix and the variance-covariance matrix.
||Computes a partial correlation/variance-covariance matrix from a correlation or variance-covariance matrix computed by CORREL_MAT.
||Fits a general multiple linear regression model.
||Computes the model matrix for a general linear model specified by a formula. It is for use with MULT_LIN_REG, where it is called instead of providing the X array.
||Calculates the Durbin-Watson test for serial correlation in linear regression.
||Calculates two types of standardized residuals and two measures of influences for a linear regression.
||Computes orthogonal polynomial or dummy variables for a factor or classification variable.
||Carries out non-seasonal and seasonal differencing on a time series.
||Computes the sample autocorrelation function of a time series along with a test for autocorrelation.
||Calculates the partial autocorrelation function from the autocorrelation function.
||Calculates preliminary estimates of the parameters of an autoregressive integrated moving average(ARIMA) model from the autocorrelation function of the appropriately differenced time series.
||Fits either a (seasonal) ARIMA model or a multi-input (transfer function) model.
||Forecasts from a fitted ARIMA model.
||Produces forecasts of a time series which depends on one or more other series via a previously estimated multi-input model.
||Calculates the smoothed sample spectrum of a univariate time series using spectral smoothing by the trapezium frequency (Daniell) window.
||Computes the analysis of variance for a block design with equal sized blocks or a completely Randomized design.
||Computes the analysis of variance for a general row and column design such as a Latin square.
||Computes sum of squares for a user defined contrast between treatment means.
||Computes simultaneous confidence intervals for the differences between means following an analysis of variance.
||Computes an analysis of variance table and treatment means for a complete factorial design.
||Fits a generalized linear model with normal errors.
||Fits a generalized linear model with binomial errors.
||Fits a generalized linear model with Poisson errors.
||Fits a generalized linear model with gamma errors.
||Computes the model matrix for a linear model specified by a formula. It is for use with NORMAL_GLM, BINOMIAL_GLM, POISSON_GLM and GAMMA_GLM where it is called instead of providing the X array.
||Performs a principal component analysis on a data matrix.
||Computes the maximum likelihood estimates of the parameters of a factor analysis model. Either the data matrix or a correlation/covariance matrix may be input.
||Computes factor score coefficients from the result of fitting a factor analysis model by maximum likelihood as performed by FACTOR.
||Computes orthogonal rotations for a matrix of loadings.
||Computes a distance (dissimilarity) matrix.
||Performs hierarchical cluster analysis.
||Computes a cluster indicator variable from the results of a cluster analysis performed ny CLUSTER.
||Allocates observations to groups according to selected rules. It is intended for use after DISCRIM_TEST.
||Computes Mahalanobis squared distances for group or pooled variance-covariance matrices. It is intended for use after DISCRIM_TEST.
||Computes a test statistic for the equality of within-group covariance matrices and also computes information for use in discriminant analysis.
||Performs non-metric (ordinal) multidimensional scaling.
||Performs a principal coordinate analysis also known as classical metric scaling.