Applied Stats & Vis for Analytics

Introduces multivariate regression and random forests for modeling data. Addresses data access, variable selection and model diagnostics. Introduces foundations for visual thinking. Reviews common statistical graphics such as dot plots, box plots, q-q plots. Addresses more advanced methods such as scatterplot matrices enhanced by smoothed or density contours, and search tools for finding graphics with suggestive patterns. Course will introduce R software for analysis. A final project will involve visualization of a real data set. Prerequisite: Undergraduate statistics. (3)

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