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Hill-climbing algorithm is an algorithm which is made in use in those techniques of cluster analysis which seek to find the partition of n individuals into g clusters by optimizing some numerical index of the clustering. Since it is not possible to consider every partition of n individuals into g groups (because of the enormous number of the partitions), the algorithm starts with some given initial partition and considers individuals in turn for moving into the other clusters, creating the move if it causes an improvement in the value of the clustering index. The procedure is continued until no move of the single individual causes an improvement.
Randomization tests are the procedures for determining the statistical significance directly from the data with- out recourse to some particular sampling distribution. For instanc
Protopathic bias is the type of bias (also called as reverse-causality) that is a consequence of differential misclassification of the exposure related to timing of occurrence. It
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
Surveys which use lists related with the vital statistics to sample individuals for the further information. For instance, the 1988 National Mortality Follow back Survey sampled de
Genomics is the study of the structure, function and the evolution of deoxyribonucleic acid (DNA) or ribonucleic acid (RNA) sequences which comprise the genome of living organisms
We are installing a router for our network. We believe that the time between the arrival of packets will be exponentially distributed with parameter R = 2 packets/second, and th
The estimator of the group by the time period interaction in a study in which the subjects in two different groups are observed in two different time periods. Normally one of th
What is statistical inference? Statistical inference can be defined as the method of drawing conclusions from data which are subject to random variations. This is based o
The nonparametric Bayesian inference approach to using the finite mixture distributions for modelling data suspected of the containing distinct groups of observations; this approac
how to constuct design matrix
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