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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.
1. If you are calculating a correlation coefficient testing the relationship between height and weight, state the null and alternative hypotheses. 2. What kind of relationship d
Systematic Sampling In Systematic Sampling each element has an equal chance of being selected, but each sample does not have the same chance of being selected. Here,
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Consider a Cournot duopoly with two firms (firm 1 and firm 2) operating in a market with linear inverse Demand P(Q) = x Q where Q is the sum of the quantities produced by both
data:59,59,65,70,74 176,179,195,210,200
Disadvantages The value of mode cannot always be determined. In some cases we may have a bimodal series. It is not capable of algebraic manipulations. For example, from t
Arithmetic Average or Mean The arithmetic mean is the most widely and the most generally understandable of all the averages. This is clear from the reason that when the term
#question HOW TO TEST
I have 5 observations that i must plug into spss. I need an example of 1. I do not know if you are familiar with SPSS but I am going to ask anyway. Subject 1 is a Hispanic male who
A. Do the correlation matrix table. B. Which variable (s) has the largest correlation coeffieient which is not a perfect correlation? C. Which variable (s) has the s
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