A regression line is identified by the smallest sum of squared distances from the data.
A residual is the difference between the data predicted from a model and the actual data.
Potential pitfalls in fitting a linear regression model are influential data points and nonlinear associations.
In a multiple linear regression model each coefficient represents the independent association of the covariate with the outcome variable, holding all other variables constant.
The null hypothesis for a coefficient in a simple linear regression model is that the coefficient is equal to 0.
Confounding can be detected by a substantial change in the coefficient of interest after including the potential confounding variable in the multiple regression model.