Using poly() to fit non-linear shapes within a linear framework.
To verify constant variance across the range of data.
To check for non-linearity and heteroscedasticity. Normal Q-Q: To ensure residuals are normally distributed.
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:
Using poly() to fit non-linear shapes within a linear framework.
To verify constant variance across the range of data. Linear Models with R
To check for non-linearity and heteroscedasticity. Normal Q-Q: To ensure residuals are normally distributed. Using poly() to fit non-linear shapes within a
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