Linear regression is a powerful tool for modeling relationships between variables in a dataset. However, there are limitations to linear regression that may result in biased or inaccurate predictions. Discuss at least two common assumptions of linear regression models and the potential consequences of violating these assumptions. How can Python be used to diagnose and address violations of these assumptions in a linear regression model? Provide at least one example of a linear regression model that violates one of these assumptions and how it can be improved.

Prepare your answer as follows:

Provide a professionally written answer, anchored in scholarly work.$$

Create a diagram, table, or other visual prop to help explain your answer and include $12$ sentences describing the visual.

Include a Jupyter notebook with the relevant Python code you created.

Record a short $2-$ to $3-$minute video $($using tools like Zoom or Loom$)$ in which you explain your answer and code. Use an online video platform such as Loom, YouTube, or Vimeo to upload your completed video. Include the link to the video in your answer.