mis6356Dimension Reduction(1)

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Arizona State University *

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6356

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Information Systems

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Oct 30, 2023

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Dimension Reduction James Zhang MIS 6356 BA with R
Exploring the data Statistical summary of data: common metrics Average Median Minimum Maximum Standard deviation Counts & percentages
Reducing Categories A single categorical variable with m categories is typically transformed into m or m-1 dummy variables (handled automatically by most R modeling functions Each dummy variable takes the values 0 or 1 0 = “no” for the category 1 = “yes” Problem: Can end up with too many variables Solution: Reduce by combining categories that are close to each other Use pivot tables to assess outcome variable sensitivity to the dummies
Combining Categories Many zoning categories are the same or similar with respect to CATMEDV
Principal Components Analysis Goal: Reduce a set of numerical variables. The idea: Remove the overlap of information between these variable. [“Information” is measured by the sum of the variances of the variables.] Final product: A smaller number of numerical variables that contain most of the information
Principal Components Analysis How does PCA do this? Create new variables that are linear combinations of the original variables (i.e., they are weighted averages of the original variables). These new variables are uncorrelated (no information overlap), and only a few of them contain most of the original information. The new variables are called principal components .
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