Using Principal Components Analysis (PCA) to Analyze Latino Stress by Agricultural Season and Occupation

Principal Components Analysis (PCA) is a commonly used unsupervised machine learning technique. In this presentation, I describe the PCA method with a general description and geometric interpretation using simulated two and three-dimensional data. The description of the PCA methodology is followed by an application of PCA to analyze Latino stress by agricultural season and occupation in a majority-minority agricultural area of eastern Washington State.

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Created by Wade K. Copeland | Privacy Policy | This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.