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Correspondence analysis is an exploratory technique used to analyze simple two-way and multi-way tables containing measures of correspondence between the rows and colulnns of any given data. The results provide information almost similar to those produced by Factor Analysis techniques, and they allow us to explore the structure of categorical variables included in the table. Multiple correspondence analysis (MCA) is an extension of simple correspondence analysis to more than two variables. MCA can be used to analyze several contingency tables by generalizing CA.
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
Using log(x1), log(x2) and log(x3) as the predictors, do pair wise scatterplots of all pairs of variables (including the response) and comment (use the pairs function). Do you thin
Analysis of variance allows us to test whether the differences among more than two sample means are significant or not. This technique overcomes the drawback of the method used in
Do people of different age groups differ in their response to e-mail messages? A survey by the Cent of the Digital Future of the University of Southern California reported that 70.
Determine the Effects of Stopping Smoking On Weight Gain As part of a study to determine the effects of stopping smoking on weight gain, nine females were weighed on the day t
JAR 21 SUPPLEMENTAL TYPE CERTIFICATION JAR 21 Part E introduces the need for Supplemental Type Certification when a manufacturer wishes to make major changes to the Type Desig
The 4 assumptions of regression: 1. Variables are normally distributed 2. Linear relationship between the independent and dependent variables 3. Homosced
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In reduced rank regression (RRR), the dependent variables are first submitted to a PCA and the scores of the units are then used as dependent variables in a series of
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