Explain confounding with an Uber example

## Question In the context of analyzing Uber/Uber Eats data, explain what a **confounding effect** is. 1. Define **confounder** and why it can bias an observed relationship. 2. Give a concrete **Uber-related** example (avoid generic examples like age/sex/demographics). 3. Describe **at least two** practical ways you would detect and/or mitigate confounding in an analysis. ## Expectations - Your example should clearly identify: - the **treatment/exposure** (X) - the **outcome** (Y) - the **confounder** (Z) that affects both X and Y - Explain the direction of the bias intuitively (how it could create a false effect or hide a real one).