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The non-trivial extraction of implicit, earlier unknown and potentially useful information from data, specifically high-dimensional data, using pattern recognition, artificial intelligence and machine learning, and presentation of the information extracted in a form that is without difficulty understandable to humans. Significant biological discoveries are now frequently made by combining data mining methods with the traditional laboratory techniques; an instance is the discovery of novel regulatory areas for heat shock genes in C. Elegans made by mining vast amounts of the gene expression and sequence data for the significant patterns.
The diagnostic tools or devices used to approach the closeness to the linearity of the non-linear model. They calculate the deviation of so-called expectation surface from the plan
Jonckheere Terpstra test is the test for detecting particular types of departures from the independence in a contingency table in which both the row and column categories contain
Hosmer-Lemeshow test is a goodness-of-fit test taken in use in logistic regression, particularly when there are regular covariates. Units are spitted into deciles based on predict
Quantile regression is an extension of the classical least squares from estimation of the conditional mean models to the estimation of the variety of models for many conditional q
Median is the value in a set of the ranked observations which divides the data into two parts of equal size. When there are an odd number of observations the median is middle v
Protocol is the formal document outlining the proposed process for carrying out the clinical trial. The basic features of the document are to study the objectives, patient selecti
Blinder Oaxaca method: A method or technique used for assessing the effect of the role of income on racial wealth gap. The method or technique is based on the decomposition of the
In an experiment, power is a function of 1. The number of variables being measured and the beta level 2. The effect size, internal validity and the beta level 3. The number of part
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 expla
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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