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Principal components regression analysis is a process often taken in use to overcome the problem of multicollinearity in the regression, when simply deleting a number of the explanatory variables is not considered correct. Essentially the response variable is regressed on the small number of principal component scores resulting from the principal components analysis of the explanatory variables.
Completeness : A term applied to a statistic t when there is only one function of that the statistic which can have the given expected value. If, for instance, the one function of
What is the EM?
Population pyramid : The diagram designed to show the comparison of the human population by sex and age at a given instant time, consisting of a pair of the histograms, one for eve
Randomization tests are the procedures for determining the statistical significance directly from the data with- out recourse to some particular sampling distribution. For instanc
Half-normal plot is a plot for diagnosing the model inadequacy or revealing the presence of outliers, in which the absolute values of, for instance, the residuals from the multipl
L'Abbe ´ plot is often used in the meta-analysis of the clinical trials where the result is the binary response of it. The event risk (number of events/number of the patients in a
Basic reproduction number : A term used in the theory of infectious diseases for the number of secondary cases which one case would generate in a completely susceptible population.
Barrett and Marshall Model for conception : A biologically reasonable model for the probability of conception in a particular menstrual cycle, which supposes that the batches of sp
Outliers - Reasons for Screening Data Outliers are due to data entry errors, subject is not a member of the population that the sample is trying to represent, or the subject i
Introduction to Generalized Linear Models (GLM) We introduce the notion of GLM as an extension of the traditional normal-theory-based linear regression models. This will be very
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