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Lancaster models: The means of representing the joint distribution of the set of variables in terms of the marginal distributions, supposing all the interactions higher than a particular order disappear. Such models give a manner to capture the dependencies between variables without making the sometimes unrealisitic supposition of total independence on the one hand, yet having a model which does not need an unrealistic number of the observations to give precise parameter estimates.
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
A test for equality of the variances of the two populations having normal distributions, based on the ratio of the variances of the sample of observations taken from each. Most fre
how to calculate the semi average method when 8 observations are given?
A two-step distillation and mixing process is shown in the figure. The system operates at steady-state conditions and there are no chemical reactions. The known flow rates and comp
The contingency tables in which the row and column both the categories follow a natural order. An instance for this might be, drug toxicity ranging from mild to severe, against the
Negative binomial distribution is the probability distribution of number of failures, X, before the kth success in the sequence of Bernoulli trials where the probability of succes
An approach to investigations designed to recognize a particular medical condition in the large population, usually by means of a blood test, which might result in the considerable
Pattern recognition is a term for a technology that recognizes and analyses patterns automatically by machine and which has been used successfully in many areas of application inc
Literature controls : The patients with the disease of interest who have received, in the past, one of two treatments under the investigation, and for whom the results have been pu
Projection pursuit is a procedure for attaning a low-dimensional (usually two-dimensional) representation of the multivariate data, which will be particularly useful in revealing
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