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Pizza Tips Regression Modeling
Explore which delivery and weather factors are associated with larger tips.
STAR Summary
- Situation
- Tips varied a lot by neighborhood and housing type. I wanted to see what actually drives them.
- Task
- Test which recorded delivery and weather factors are associated with tip size using regression analysis.
- Action
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- Merged 1,251 delivery tickets with NOAA weather, then cleaned the data in Power Query.
- Ran a multiple regression in Excel: Tip = f(cost, delivery time, rain, max/min temperature).
- Result
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- Order cost was the strongest predictor of tip size in the regression.
- Housing type (apartment vs. house) showed a statistically significant difference in this sample.
- Weather and delivery time didn’t show a meaningful effect on tip size.
Pizza Tip Estimator
Explore how delivery and order details affect the estimated tip.
Open full demo
Adjust an order and estimate its tip directly in your browser.
- Edit the order cost, delivery city, housing type, and order hour to compare scenarios.
- Select “Update estimate” to calculate the tip and uncertainty interval.
- Expand the delivery map to choose a location or compare estimated tips across the area.
- Select “Reset” to return to the computed example.
