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Generalized principal components analysis: The non-linear version of the principal components analysis in which the goal is to determine the non-linear coordinate system which is most in agreement with the data configuration. For instance, for the bivariate data, y1,y2, if the quadratic coordinate system is sought, a variable z is defined as given below: with the coefficients being set up so that the variance of z is a maximum amongst all such quadratic functions of y1 and y2.
Path analysis is a device for evaluating the interrelationships among the variables by analyzing their correlational structure. The relationships between the variables are man
Matching coefficient is a similarity coefficient for data consisting of the number of binary variables which is often used in cluster analysis. It can be given as follows he
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
historigrams and histogram
Individual differences scaling is a form of multidimensional scaling applicable to the data comprising of a number of proximity matrices from the different sources that is differe
Matching distribution is a probability distribution which arises in the following manner. Suppose that the set of n subjects, numbered 1; . . . ; n respectively, are arranged in
Percentile : The set or group of divisions which produce exactly 100 equal parts in the series of continuous values, like blood pressure, height, weight, etc. Hence a person with b
PRINCIPLES OF MODELLING IN OR.
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
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
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