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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.
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Test for Equality of Proportions For example, we may want to test whether the percentage of smokers (p 1 ) among the males equals the percentage of female smokers (p 2 ). W
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Cause and Effect Even a highly significant correlation does not necessarily mean that a cause and effect relationship exists between the two variables. Thus, correlation does
Question: The weights of 60 children born to mothers in a small rural hospital were recorded. 3.63 3.54 3.15 3.90 4.29 4.06 2.91 3.36 3.3
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Chi-square analysis can be used with both Goodness-of-Fit Tests and with Tests for Independence. There are specific instances when each test should be used based on the information
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
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