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Apr 7, 2017 11:55 AM
(588 views)

Hello,

I am wondering how to decide and determine the continuous variables for PCA process.

I am analyizing house sales price with about 80 variables, and there are too many continuous variables with wide rage. So I want to reduce the set of numerical variables for concise model, but I don't know which variables I should apply for PCA.

Please help me to figure out this. The file is attached below.

Thank you.

3 REPLIES

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Apr 10, 2017 2:07 AM
(537 views)

Is PCA the technique you require? The individual distributions of the numbers can heavily influence the outcome of the PCA analysis.

Are you trying to predict future house prices or understand what the important variables are? A simple partition analysis gives similar result to generalised regression. 90 % of the variability in prices can be described using 4 variables, all of which are numeric but only two of them are continuous.

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Apr 10, 2017 6:59 AM
(525 views)

I want to first understand what the important variables are, and then run

regression with those variables to predict future house sales price. Also,

I want to run the regression with all variables, and then compare the

result of regressions to obtain the best mode which should be simpler with

the least error at the end.

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Apr 10, 2017 8:03 AM
(520 views)

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