Analyze the Distribution of Trip Distance

# Analyze the Distribution of Trip Distance Using the loaded green-taxi trip data, plot a histogram of `Trip Distance`. Describe the visible structure and propose testable hypotheses for zero, unusually small, multimodal, or extreme values without presenting speculation as fact. ### Constraints & Assumptions - Preserve and report how many missing, zero, negative, and extreme values exist before filtering. - Explain binning, axis scale, and any clipping or transformation. - The chart and narrative must distinguish observed patterns from possible causes. ### Clarifying Questions to Ask - Is the audience interested in operational data quality, typical passenger trips, or both? - Should distance be analyzed on the raw scale, a log scale, or complementary views? ```hint Let the tails influence the display choice A single linear histogram can hide the center when a few records are extremely large. ``` ### What a Strong Answer Covers - Data-quality checks before visualization. - Defensible binning and at least one view that reveals the main mass. - Quantitative annotations such as quantiles and excluded counts. - Plausible hypotheses paired with ways to test them. ### Follow-up Questions - How would you tell data-entry errors from legitimate long trips? - Which plot would better compare distance distributions across time of day?