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Principal components analysis is a process for analysing multivariate data which transforms original variables into the new ones which are uncorrelated and account for decreasing the proportions of variance in the data. The goal of the method is to decrease the dimensionality of the data. The principal components, new variables, are defined as the linear functions of the usual variables. If the first few principal components account for the large percentage of the variance of the observations (say it above 70%) they can be used both to simplify subsequent analyses and to display and summarize the data in a parsimonious way.
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Back-projection: A term most often applied to the procedure for reconstructing plausible HIV incidence curves from the AIDS incidence data. The method or technique assumes that th
Write a c++ program to find the sum of 0.123 ? 103 and 0.456 ? 102 and write the result in three significant digits
Likelihood is the probability of a set of observations provided the value of some parameter or the set of parameters. For instance, the likelihood of the random sample of n observ
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
Residual plots are the plots of some type of residual which might be helpful in assessing the assumption made by the fitted model. In regression analysis there are various method
Partial least squares is an alternative to the multiple regressions which, in spite of using the original q explanatory variables directly, constructs the new set of k regressor v
Buffon's needle problem : A problem proposed and solved by the scientist Comte de Buffon in 1777 which includes determining the probability, p, which a needle of length l will inte
The distribution over distributions in the sense that each draw from the process is itself the distribution. The name Dirichlet process or procedure is due to the fact that the ?ni
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