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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.
Difference b/w historigram and histogram
The type of longitudinal study in which the subjects receive different treatments on the various occasions. Random allocation is required to determine the order in which the treatm
Machine learning is a term which literally means the ability of a machine to recognize patterns which have occurred repetitively and to improve its performance based on the past
Gllamm is a program which estimates the generalized linear latent and mixed models by the maximum likelihood. The models which can be fitted include structural equation models mul
Multivariate analysis of variance is the procedure for testing equality of the mean vectors of more than two populations for the multivariate response variable. The method is dire
Evaluate the following statistical arguments. Begin by identifying the sample, population, and the property which is being investigated. Do these arguments sound acceptable? Would
Radical statistics group : The national network of the social scientists in United Kingdom committed to the critique of statistics as taken in use in the policy making procedure. T
what is operational gaining
Pascal's triangle is an arrangement of numbers described by Pascal in his Traité du Triangle Arithmétique published in the year 1665 as 'The number in each cell is equal to in the
Median is the value in a set of the ranked observations which divides the data into two parts of equal size. When there are an odd number of observations the median is middle v
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