Tips Dataset Analysis

Python NumPy Pandas Seaborn Matplotlib Scikit-learn

02/2023

Overview

Exploratory data analysis on a tips dataset comparing tips received by male and female servers. The project employs bootstrap resampling techniques to generate statistically robust confidence intervals and uses kernel density estimation for accurate distribution modeling.

Initial distribution comparison of tips by server gender.

Bootstrap resampling distribution of mean tips.

Smoothed bootstrap with kernel density estimation.

Confidence intervals for mean tips comparison.

Methodology

Statistical Methods:

• Bootstrap Resampling: Generated thousands of resampled datasets to estimate the sampling distribution of the mean tip amount for each server gender

• Smoothed Bootstrap: Applied kernel density estimation to the bootstrap distribution for a continuous representation

• Confidence Intervals: Computed 95% confidence intervals from bootstrap distributions to assess whether the difference in mean tips is statistically significant

Key Findings: The analysis examines whether there is a statistically significant difference in tipping behavior based on server gender, using non-parametric methods that make no assumptions about the underlying distribution.

Technologies

Category	Tools
Data Processing	NumPy, Pandas
Visualization	Seaborn, Matplotlib
Statistical Analysis	Scikit-learn, Bootstrap methods

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