The Multivariate Tolerance Limits procedure creates statistical tolerance limits for data consisting of more than one variable.
More: Multivariate Normality Test.pdf or Watch Video Multivariate Tolerance Limits It includes Royston’s H test and tests based on a chi-square plot of the squared distances of each observation from the sample centroid. This procedure tests whether a set of random variables could reasonably have come from a multivariate normal distribution. More: Canonical Correlations.pdf Multivariate Normality Test Often, a small number of pairs can be used to quantify the relationships that exist between the two sets. The second set of canonical variables is the pair of linear combinations that show the next strongest correlation amongst all combinations that are uncorrelated with the first set.
The pair of linear combinations with the strongest correlation forms the first set of canonical variables. It does so by finding linear combinations of the variables in the two sets that exhibit strong correlations. The Canonical Correlations procedure is designed to help identify associations between two sets of variables. More: Partial Least Squares.pdf Canonical Correlations PLS is widely used by chemical engineers and chemometricians for spectrometric calibration. Unlike other regression procedures, estimates can be derived even in the case where the number of predictor variables outnumbers the observations. The procedure is most helpful when there are many predictors and the primary goal of the analysis is prediction of the response variables. Partial Least Squares is designed to construct a statistical model relating multiple independent variables X to multiple dependent variables Y. More: Neural Network Classifier.pdf Partial Least Squares The estimate is constructed using a Parzen window that weights observations from each group according to their distance from the specified location. Rather than making any assumption about the nature of the distribution of the variables within each group, it constructs a nonparametric estimate of each group’s density function at a desired location based on neighboring observations from that group. The Neural Network Classifier implements a nonparametric method for classifying observations into one of g groups based on p observed quantitative variables. More: Discriminant Analysis.pdf Neural Network Bayesian Classifier to be able to classify new observations as belonging to one or another of the groups. to be able to describe observed cases mathematically in a manner that separates them into groups as well as possible. The objective of such an analysis is usually one or both of the following:ġ. It does so by constructing discriminant functions that are linear combinations of the variables. The Discriminant Analysis procedure is designed to help distinguish between two or more groups of data based on a set of p observed quantitative variables.