The chapter talks about regression models, which are used to understand relationships between different variables in a straightforward way. They mainly focus on the normal linear regression model, where they link one variable to another assuming they follow a typical distribution pattern. The main types discussed are simple linear regression, involving just two variables with a linear connection, and multiple regression, which looks at more than two variables. They also touch on model formulas in R, a type of statistical software, generalized linear models, issues like collinearity between variables, and the benefits of logarithmic transformations.