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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.
The Null Hypothesis - H0: β 1 = 0 i.e. there is homoscedasticity errors and no heteroscedasticity exists The Alternative Hypothesis - H1: β 1 ≠ 0 i.e. there is no homoscedasti
Kappa coefficient : The chance corrected index of the agreement between, for instance, judgements and diagnoses made by the two raters. Calculated as the ratio of the noticed exces
Paul Jordan has just been hired as a management analyst at Digital Cell Phone Inc. Digital Cell manufactures a broad line of phones for the consumer market. Paul's boss, John Smith
This is an alternative to the Newton-Raphson technique for optimization (finding out the minimum or the maximum) of some function, which includes replacing the matrix of second der
O. J. Simpson paradox is a term coming from the claim made by the defence lawyer in murder trial of O. J. Simpson. The lawyer acknowledged that the statistics demonstrate that onl
Different approaches to the study of early indian history
Hypergeometric distribution is t he probability distribution related with the sampling without replacement from the population of finite size. If the population comprises of r ele
Negative binomial distribution is the probability distribution of number of failures, X, before the kth success in the sequence of Bernoulli trials where the probability of succes
Pattern recognition is a term for a technology that recognizes and analyses patterns automatically by machine and which has been used successfully in many areas of application inc
Principal factor analysis is the method of factor analysis which is basically equivalent to a principal components analysis performed on reduced covariance matrix attained by repl
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