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Answer :
Final answer:
The question requires performing factor analysis in R on the provided data which involves functions such as 'read.table', 'scale', 'prcomp', and 'factanal' functions. Always remember to evaluate your model and check residuals.
Explanation:
The data that you have provided seems to be from a multivariate data analysis, specifically factor analysis which is mainly used in psychology, finance, and operation research. To analyze this data in R, you would first load the data into R, standardize it and then perform factor analysis using functions such as 'prcomp' and 'factanal'. You can also use the 'psych' package for various additional functions and easier usage. Here's an illustrative code snippet:
data <- read.table('T9-12.DAT')
scaled_data <- scale(data)
pc_solution <- prcomp(scaled_data)
ml_solution <- factanal(scaled_data, factors=2, rotation='varimax')
You would need to adjust the number of 'factors' parameter to match your requirement of 2 or 3 factors, and repeat the steps accordingly. The 'prcomp' function would provide you with the principal component solution while 'factanal' would give you the maximum likelihood solution. It's important to always evaluate your model and check residuals to ensure its effectiveness.
Learn more about Factor Analysis in R here:
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