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High-dimensional data: This term used for data sets which are characterized by the very large number of variables and a much more modest number of the observations. In the 21st century\ such data sets are collected in number of areas, such as, text/web data mining and bioinformatics. The job of extracting meaningful statistical and biological information from such data sets present many challenges for which a number of recent methodological developments, for instance, sure screening methods, lasso, and Dantzig selector, might be quite helpful.
A term commonly encountered in the analysis of the contingency tables. Such type of frequencies are the estimates of the values to be expected under hypothesis of interest. In a tw
This term sometimes is applied to the model for explaining the differences found between naturally happening groups which are greater than those observed on some previous occasion;
Cluster sampling : A method or technique of sampling in which the members of the population are arranged in groups (called as 'clusters'). A number of clusters are selected at the
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
R-squared is regarded as the coefficient of determination and is used to give the proportion of the fluctuation of the variance of one variable to another variable. R-squared also
Profile plots is a technique of representing the multivariate data graphically. Each of the observation is represented by a diagram comprising of a sequence of equispaced vertical
Bonferroni correction : A procedure for guarding against the rise in the probability of a type I error when performing the multiple signi?cance tests. To maintain probability of a
Particlefilters is a simulation method for tracking moving target distributions and for reducing computational burden of the dynamic Bayesian analysis. The method uses a Markov ch
how to resolve sequencing problem if jobs 6 given and 4 machines given. how to apply johnson rule for making to machines under this conditions. please give solution as soon as poss
The Null Hypothesis - H0: There is no autocorrelation The Alternative Hypothesis - H1: There is at least first order autocorrelation Rejection Criteria: Reject H0 if LBQ1 >
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