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Peer reviewedMcQuitty, Louis L.; Koch, Valerie L. – Educational and Psychological Measurement, 1975
Develops and illustrates a method for clustering hierarchically the interrelationships between many persons, as represented in a matrix of a thousand by a thousand. (RC)
Descriptors: Classification, Cluster Grouping, Matrices, Measurement Techniques
Spearing, Debra; Woehlke, Paula – 1989
To assess the effect on discriminant analysis in terms of correct classification into two groups, the following parameters were systematically altered using Monte Carlo techniques: sample sizes; proportions of one group to the other; number of independent variables; and covariance matrices. The pairing of the off diagonals (or covariances) with…
Descriptors: Classification, Correlation, Discriminant Analysis, Matrices
PDF pending restorationHuberty, Carl J.; Curry, Allen R. – 1975
A linear classification rule (used with equal covariance matrices) was contrasted with a quadratic rule (used with unequal covariance matrices) for accuracy of internal and external classification. The comparisons were made for seven situations which resulted from combining three data conditions (equal and unequal covariance matrices, minimal and…
Descriptors: Analysis of Covariance, Bayesian Statistics, Classification, Comparative Analysis
Statistical Reporting Service (USDA), Washington, DC. – 1974
Intended as a reference for the convenience of students in sampling, this monograph attempts to express relevant, introductory mathematics and probability in the context of sample surveys. Although some proofs are presented, the emphasis is more on exposition of mathematical language and concepts than on the mathematics per se and rigorous proofs.…
Descriptors: Agriculture, Algebra, Classification, Data Collection


