Editing Openai/68255622-0ae4-8008-ba6e-24365969d8a5 (section)

=== Assistant: Great follow-up, Master Kenobi. ===
Great follow-up, Master Kenobi.

===== Residual plots are essential diagnostic tools in linear regression. They show the difference between the actual data points and the predicted values from the regression line (these differences are called residuals). =====

===== - Check for Linearity: A good linear model should have residuals randomly scattered around zero. This indicates the relationship is truly linear. =====
* Reveal Patterns: If there’s a clear pattern (e.g. curves or clustering), it suggests that a linear model may not be appropriate, and another type of model (like quadratic or exponential) may be better.
* Spot Outliers or Influential Points: Residual plots can expose unusual data points that might distort the model’s accuracy.

===== Yes — especially when you're using linear regression to justify a recommendation. While the R² value shows how well the model fits overall, residual plots validate whether that fit is appropriate and trustworthy. =====

===== - Use regression to identify the relationship and support recommendations. =====
* Use residual plots to verify the assumptions of linear regression and ensure your conclusion is statistically sound.

Both are needed for a thorough and credible analysis.