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K-means cluster analysis is the method of cluster analysis in which from an initial partition of observations into K clusters, each observation in turn is analysed and reassigned, if suitable, to a different cluster in an attempt to optimize some predefined numerical criterion that measures in some sense the 'quality' of cluster solution. Several such clustering criteria have been suggested, but the most usually used arise from considering the features of the within groups, between groups and whole matrices of sums of squares and the cross products (W, B, T) which can be described for every partition of the observations into the particular number of groups. The two most ordinary of the clustering criteria developing from these matrices are given as follows
minimization of trace W
minimization of determinant W
The first of these has tendency to produce the 'spherical' clusters, the second to produce clusters that all have same shape, though this will not necessarily be spherical in shape.
Least significant difference test is an approach to comparing a set of means which controls the family wise error rate at some specific level, let's assume it to be α. The hypothe
Bonferroni correction : A procedure for guarding against the rise in the probability of a type I error when performing the multiple signi?cance tests. To maintain probability of a
This is the powerful visualization tool for studying how the response relies on an explanatory variable given the values of other explanatory variables. The plot comprises of a num
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Advantages and disadvantages of Integrated Economic Statistics
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The approach of controlling the error rate in an exploratory analysis where number of hypotheses are tested, but where the strict control which is provided by multiple comparison p
The model which arises in the context of estimating the size of the closed population where individuals within the population could be identified only during some of the observatio
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