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Multi dimensional unfolding is the form of multidimensional scaling applicable to both the rectangular proximity matrices where the rows and columns refer to the different sets of stimuli, for instance, judges and soft drinks, and asymmetric proximity matrices, for instance, citations of journal A by journal B and vice versa. Unfolding was introduced as a manner of representing judges and stimuli on a single straight line so that the rank-order of the stimuli as determined by each of the judge is reflected by the rank order of the distance of stimuli to that judge.
Persson Rootze ´n estimator is an estimator for the parameters in the normal distribution when the sample is truncated so that all the observations under some fixed value C are re
Chains of infection : The description of the course of infection among the group of individuals. The susceptibles infected by the direct contact with the introductory cases are sai
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
The method of summarizing the large amounts of data by forming the frequency distributions, scatter diagrams, histograms, etc., and calculating statistics like means variances and
Log-linear models is the models for count data in which the logarithm of expected value of a count variable is modelled as the linear function of parameters; the latter represent
Group visible design is an arrangement of the v mn treatments in b blocks such that: * Each block comprises k distinct treatments k5v; * Each treatment is replicated r number
Hypergeometric distribution is t he probability distribution related with the sampling without replacement from the population of finite size. If the population comprises of r ele
MEANING ,IMPORTANCE AND RELEAVANCE OF SCATTER DIAGRAM
How large would the sample need to be if we are to pick a 95% confidence level sample: (i) From a population of 70; (ii) From a population of 450; (iii) From a population of 1000;
Kleiner Hartigan trees is a technique for displaying the multivariate data graphically as the 'trees' in which the values of the variables are coded into length of the terminal br
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