Models With R — Linear

To check for non-linearity and heteroscedasticity. Normal Q-Q: To ensure residuals are normally distributed.

R’s formula interface is particularly adept at handling complex relationships. One does not need to manually create "dummy variables" for categorical data; R recognizes factors and automatically encodes them. Furthermore, the language allows for seamless integration of: Linear Models with R

To identify influential outliers (Cook’s Distance). To check for non-linearity and heteroscedasticity

A linear model is only as good as the assumptions it satisfies. R excels here by providing built-in diagnostic tools. A simple plot(model) command generates four critical visualizations: Linear Models with R

To verify constant variance across the range of data.