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Correspondence Analysis (CA) is a generalization of PCA to contingency tables. The factors of correspondence analysis give an orthogonal decomposi:ion of the Chi- square associated to the table. In correspondence aria!ysis, rows and columns of the table play a symmetric role and can be represznteci in tli~ sarne plot. When several nominal variables are analyzed, correspondence analysis is generalized &'Multiplc Correspondence Analysis (MCA). The ct;rrespondcnce analysis is also .known as dual or optimal scaling or reciprocal averaging.
In New Jersey, banks have been charged with withdrawing from counties having a high percentage of minorities. To substantiate this charge, data is presented in the table below conc
(1) What values can the response variable Y take in logistic regression, and hence what statistical distribution does Y follow? The response variable can take the value of either
Significance of Correlation The study of correlation is of immense use in practical life. Correlation analysis contributes to the understanding of economic behavior, aids in lo
Systematic Random Sampling This method is generally used in such cases where a complete list of the population is available from which sample has to be selected. Under this
PCA is a linear transformation that transforms the data to a new coordinate system such that the greatest variance by any projection of the data comes to lie on the first coordinat
CALCULATE THE PERCENTAGE OF REFUNDS EXPECTED TO EXCEED $1000 UNDER THE CURRENT WITHHOLDING GUIDELINES
Analysis of covariance (ANCOVA) It is initially used for an expansion of the analysis of variance which permits to the possible effects of continuous concomitant variables (suc
Consider a Cournot duopoly with two firms (fi rm 1 and fi rm 2) operating in a market with linear inverse Demand P(Q) = x Q where Q is the sum of the quantities produced by both
BCBSRI was able to reduce MSD related Workers Compensation cases with lost workdays by implementing a New Ergonomic Program in March 2000 and increasing workstation evaluations. Ex
This probability rule determined by the research of the two mathematicians Bienayme' and Chebyshev, explains the variability of data about its mean when the distribution of the dat
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