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A term which covers the large number of techniques for the analysis of the multivariate data which have in common the aim to assess whether or not the set of variables distinguish or discriminate between the two or more groups of the individuals. In medicine, for instance, this type of methods are generally applied to the problem of using optimally the results from the various tests or the observations of various symptoms to make the diagnosis which can only be confirmed perhaps by the post-mortem examination. In the two group case the mainly used method is Fisher's linear discriminant function, in which a linear function of variables giving the maximal separation between the groups is then determined. This results in the classification rule which may be used to assign the new patient to one of the two groups. The derivation of the linear function supposes that the variance-covariance matrices of the two groups are the same. If they are not then a quadratic discriminant function might be essential to distinguish between the groups. Such a function comprises of powers and cross-products of variables. The sample of the observations from which the discriminant function is derived is commonly known as the training set. When more than two groups are involved then it is possible to determine the several linear functions of the variables for separating them. In common the number of such functions which can be derived is the smaller of q and g-1 where q is the number of variables and g is the number of groups. The collection of the linear functions for discrimination is called as canonical discriminant functions or simply as canonical variates.
Coincidences : Astonishing concurrence of the events, perceived as meaningfully related, with no apparent causal connection. Such type of events abounds in everyday life and is oft
Mean squarederror is the expected value of square of the difference between an estimator and the true value of the parameter. If the estimator is unbiased then the mean of the squ
Uncertainty analysis is the process for assessing the variability in the outcome variable that is due to the uncertainty in estimating the values of input parameters. A sensitivit
1) Question on the first day questionnaire asked students to rate their response to the question Are you deeply moved by the arts or music? Assume the population that is sampled
Hazard regression is the procedure for modeling the hazard function which does not depend on the suppositions made in Cox's proportional hazards model, namely that the log-hazard
Matching is the method of making a study group and a comparison group comparable with respect to the extraneous factors. Generally used in the retrospective studies when selecting
The measure of the degree to which the particular model differs from the saturated model for the data set. Explicitly in terms of the likelihoods of the two models can be defined a
Weighted least squares is the method of estimation in which the estimates arise from minimizing the weighted sum of squares of the differences between response variable and its pr
Quantile regression is an extension of the classical least squares from estimation of the conditional mean models to the estimation of the variety of models for many conditional q
Conditional probability : The probability that an event occurs given the outcome of other event. Generally written, Pr(A|B). For instance, the probability of a person being color b
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