In this StatsClinic R Lab taught by Daniel Olofin, we introduce correlation and simple linear regression using real clinical and public health datasets. Participants learn how to visualize relationships between variables, compute Pearson correlation, and build linear models using R functions like lm() and glm(). The session covers interpreting regression coefficients, p-values, R-squared, and residual diagnostics to assess model fit. You will also see how binary, categorical, and multi-variable predictors are handled in R, along with model comparison using AIC. This lab marks the beginning of the Linear Regression module in the StatsClinic November course series.
