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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.
importance of mathamatical expection in business
Kendall's tau statistics : The measures of the correlation between the two sets of rankings. Kendall's tau itself (τ) is the rank correlation coefficient based on number of inversi
Prognostic scoring system is a technique of combining the prognostic information contained in the number of threat factors, in a manner which best predicts each patient's risk of
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
Clustered data : The term applied to both the data in which the sampling units are grouped into the clusters sharing some common feature, for instance families or geographical reg
Discuss the use of dummy variables in both multiple linear regression and non-linear regression. Give examples if possible
A radically different approach of dealing with the uncertainty than the traditional probabilistic and the statistical methods. The necessary feature of the fuzzy set is a membershi
The special cases of the probability distributions in which the random variable's distribution is concentrated at one point only. For instance, a discrete uniform distribution when
This graph for Cross Correlation Function for RES1, RES1 shows that there is possibly negative autocorrelation as there are alternating spikes; also the first spike is negative whi
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
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