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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.
A study not involving the passing of time. All information is collected at the same time and subjects are contacted only once. Many surveys are of this type. The temporal sequence
Chernoff's faces : A method or technique for representing the multivariate data graphically. Each observation is represented by the computer-created face, the features of which are
Cohort component method : A broadly used method or technique of forecasting the age- and sex-speci?c population to the upcoming years, in which the initial population is strati?ed
Lancaster models : The means of representing the joint distribution of the set of variables in terms of the marginal distributions, supposing all the interactions higher than a par
Hello, I have a solution for a Survey Design (proposal) assignment and looking for an expert that can look at it and correct it in case if it is wrong. Do you have this kind of ser
Prior distributions : The probability distributions which summarize the information about a random variable or parameter known or supposed at a given time instant, prior to attaini
Ignorability : The missing data mechanism is said to be ignorable for likelihood inference if (1) the joint likelihood for the responses of the interest and missing data indicators
distinguish the historigram and histogram
The Null Hypothesis - H0: There is no heteroscedasticity i.e. β 1 = 0 The Alternative Hypothesis - H1: There is heteroscedasticity i.e. β 1 0 Reject H0 if nR2 > MTB >
meaning,uses,shortcomings and drawbacks of vital statistics
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