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Multidimensional scaling (MDS) is a generic term for a class of techniques or methods which attempt to construct a low-dimensional geometrical representation of the proximity matrix for a set of stimuli, with the goal of making any structure in the data as transparent as possible. The goal of all such techniques or method is to find a low-dimensional space in which points in the space represent stimuli, one point representing one stimulus, such that the distances between points in the space match as well as possible in some sense the original dissimilarities or the similarities. In a very common sense this simply means that the larger the observed dissimilarity value (or smaller the similarity value) amongs two stimuli, the further apart should be the points representing them in derived spatial solution. A common approach to finding the required coordinate values is to select them so as to minimize some least squares type fit criterion such as follows
ACC – A pioneer in the Indian cement industry Associated Cement Companies Ltd. (ACC) came into existence in 1936, after the merger of 10 companies belonging to four important bus
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The term used when the aggregated data (for instance, aggregated over different areas) are analysed and the results supposed to apply to the relationships at the individual level.
High-dimensional data : This term used for data sets which are characterized by the very large number of variables and a much more modest number of the observations. In the 21 st
The graphical process most frequently used in the analysis of data from a two-by-two crossover design. For each of the subject the difference between the response variable values o
The special cases of the probability distributions in which the random variable's distribution is concentrated at one point only. For instance, a discrete uniform distribution when
The method or technique for displaying the relationships between categorical variables in a type of the scatter plot diagram. For two this type of variables displayed in the form o
Genetic algorithms: The optimization events motivated by the biological analogies. The prime idea is to try to mimic the 'survival of the fittest' rule of the genetic mutation in
Regression through the origin : In some of the situations a relationship between the two variables estimated by the regression analysis is expected to pass by the origin because th
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
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