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Clinical vs. statistical significance: The distinction among results in terms of their possible clinical importance rather than simply in terms of their statistical importance. With large samples, for instance, very small differences which have little or no clinical importance may turn out to be the statistically signi?cant products. The practical implications of any ?nding in the medical investigation should be judged on the clinical as well as the statistical grounds.
Intervention analysis in time series : The extension of the autoregressive integrated moving average models applied to time series permitting for the study of the magnitude and str
Technically the multivariate analogue of the quasi-likelihood with the same feature that it leads to consistent inferences about the mean responses without needing specific supposi
Correlation matrix : A square, symmetric matrix with the rows and columns corresponding to the variables, in which the non diagonal elements are correlations between the pairs of t
The biggest and smallest variate values among the sample of observations. Significant in various regions, for instance flood levels of the river, speed of wind and snowfall.
A term commonly encountered in the application of the agglomerative hierarchical clustering techniques, where it refers to the 'tree-like' diagram illustrating the series of steps
Interior analysis is the term now and again applied to analysis carried out on the fitted model in regression problem. The basic target of such analyses is the identification of
Geographical analysis machine is the procedure designed to detect the clusters of rare diseases in a particular area. Circles of fixed radii are created at each point of the squar
Uncertainty analysis is the process for assessing the variability in the outcome variable that is due to the uncertainty in estimating the values of input parameters. A sensitivit
Write a c++ program to find the sum of 0.123 ? 10 3 and 0.456 ? 10 2 and write the result inthree significant digits.
Non parametric maximum likelihood (NPML) is a likelihood approach which does not need the specification of the full parametric family for the data. Usually, the non parametric max
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