4.0

credits

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Introduces statistical techniques used to model, analyze, and interpret public health-related spatial data. Casts analysis of spatially dependent data into a general framework based on regression methodology. Covers the geostatistical techniques of kriging and semivariogram analysis, point process methods for spatial case-control data and area-level analysis. Focuses on statistical modeling (although some time will be spent covering topics related to cluster detection of health outcome events). Provides instruction in the public domain statistical computing environment R/RStudio.