Inference for Linear Regression in R

You should be comfortable with dplyr, ggplot2, hypothesis testing, and intermediate regression in R. This is an advanced course that builds on both statistical inference and linear modeling foundations.

Introduction to Regression focuses on fitting models, while this course focuses on making inferences from those models, including confidence intervals, prediction intervals, and significance testing for slope parameters.

You will learn both. The course covers simulation-based inference for sampling distributions and t-based inference for constructing confidence intervals and testing slope significance.

The broom package is used to tidy model output into clean data frames, making it easier to work with regression results alongside dplyr and ggplot2 in your analysis pipeline.

It covers both. The final chapter extends inference to multiple regression models and addresses multicollinearity, building on the simple linear regression concepts from earlier chapters.