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Nearest-neighbour methods are the methods of discriminant analysis are based on studying the training set subjects much similar to the subject to be classified. Classification might then be determined according to a simple majority verdict among those much similar or 'nearest' training set subjects, that is a subject would be assigned to the group to which the majority of the 'neighbours' belonged. Simple nearest neighbour techniques just consider the most similar neighbour. More common methods consider the k nearby neighbours, where k>1.
Bootstrap : The data-based simulation method/technique for the statistical inference which can be used to study the variability of the estimated characteristics of the probability
what is goal post mentality?
The risk of being able to recognize the respondent's confidential information in the data set. Number of approaches has been proposed to measure the disclosure risk some of which c
Paired samples are the two samples of the observations with the characteristic feature with each of the observation in one sample have only one matching observation in the other s
Bartlett's test for variances : A test for equality of the variances of the number (k)of the populations. The test statistic can be given as follows where s square is an
re-reference all these indexes
what are tests for residual with nonconstant variance in regression diagnostic checking?
#how to analyse data
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
Individual differences scaling is a form of multidimensional scaling applicable to the data comprising of a number of proximity matrices from the different sources that is differe
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