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Linear models, as the name implies, relate an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist's toolkit. This course covers regression analysis, least squares, and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will also be covered. Residual analysis and variability will be investigated. The course will cover modern thinking about model selection and new uses of regression models, including scatterplot smoothing.
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Professor of biostatistics
Bloomberg School of Public Health
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