1. Understand Model Assumptions: Learn the importance of verifying assumptions to ensure the reliability and accuracy of regression models in data analysis.

2. Identify Diagnostic Plot Issues: Explore how to use diagnostic plots to detect potential problems such as non-linearity, unequal variance, and outliers.

3. Apply Data Transformations: Discover techniques like square root transformations to address assumption violations and improve model performance.

4. Evaluate Model Improvements: Compare diagnostic plots before and after transformations to assess the effectiveness of adjustments made to the model.