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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.
Type of Variable in Regression Analysis There are two types of variable in regression analysis. These are: a. Dependent variable b. Independent variable
Standard Deviation The concept of standard deviation was first introduced by Karl Pearson in 1893. The standard deviation is the most important and the popular measure of disp
Descriptive Statistics : Carrying out an extensive analysis the data was not a subject to ambiguity and there were no missing values. Below are descriptive statistics that hav
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Cluster Analysis could be also represented more formally as optimization procedure, which tries to minimize the Residual Sum of Squares objective function: where μ(ωk) - is a centr
Cluster Sampling Here the population is divided into clusters or groups and then Random Sampling is done for each cluster. Cluster Sampling differs from Stratified Sampl
Random Sampling Method In this method the units are selected in such a way that every item in the whole universe has an equal chance of being included. In the words of croxton
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While there are p original variables the number of principal components is m such that m
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