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Principal components analysis is a process for analysing multivariate data which transforms original variables into the new ones which are uncorrelated and account for decreasing the proportions of variance in the data. The goal of the method is to decrease the dimensionality of the data. The principal components, new variables, are defined as the linear functions of the usual variables. If the first few principal components account for the large percentage of the variance of the observations (say it above 70%) they can be used both to simplify subsequent analyses and to display and summarize the data in a parsimonious way.
Maternal mortality : The maternal death is the death of a woman while pregnant, delivering a baby or within 42 days of the termination of pregnancy, from any reason related to or a
Biplots: It is the multivariate analogue of the scatter plots, which estimates the multivariate distribution of the sample in a few dimensions, typically two and superimpose on th
Formal graphical representation of the "causal diagrams" or the "path diagrams" where the relationships are directed but acyclic (that is no feedback relations allowed). Plays an
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
Hot deck is a method broadly used in surveys for imputing the missing values. In its easiest form the method includes sampling with replacement m values from the sample respondent
Write a c++ program to find the sum of 0.123 ? 10 3 and 0.456 ? 10 2 and write the result inthree significant digits.
The growth in bad debt expense for Johnston office supply Company over this time period.If this rate continues,estimate the percentage increase in bad debts for 1997,relative to 19
A name sometimes given to the type of diagram generally used in meta-analysis, in which point estimates and confidence intervals are displayed for all the studies included in the a
Profile plots is a technique of representing the multivariate data graphically. Each of the observation is represented by a diagram comprising of a sequence of equispaced vertical
Bimodal distribution : The probability distribution, or we can simply say the frequency distribution, with two modes. Figure 15 shows the example of each of them
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