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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
Bivariate survival data : The data in which the two related survival times are of interest. For instance, in familial studies of disease incidence, data might be available on the a
moving and semi average method graphical reprsentation
Model is the description of the supposed structure of a set of observations which can range from a fairly imprecise verbal account to, more commonly, a formalized mathematical exp
The theory of measurement which recognizes that in any measurement situation there are multiple (actually infinite) sources of variation (known as facets in the theory), and that a
Prospective study : The studies in which individuals are followed-up over the period of time. A general example of this type of investigation is where the samples of individuals ar
Reasons for screening data Garbage in-garbage out Missing data a. Amount of missing data is less crucial than the pattern of it. If randomly
Captures recapture sampling : Another approach to a census for estimating the size of population, which operates by sampling the population number of times, identifying the individ
The Null Hypothesis - H0: There is no autocorrelation The Alternative Hypothesis - H1: There is at least first order autocorrelation Rejection Criteria: Reject H0 if LBQ1 >
Confounding: A procedure observed in some factorial designs in which it is impossible to differentiate between some main effects or interactions, on the basis of the particular d
Median absolute deviation (MAD) : It is the very robust estimator of the scale given by the following equation or, in other words we can say that, the median of the absolute
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