Purpose

This section documents the data cleaning and preparation pipeline for the price elasticity analysis. Perfect retention (100%) indicates no rows were removed during preprocessing, meaning all 80 observations passed quality checks and were available for the log-log OLS regression model that estimates demand elasticity across the 8 products.

Key Findings

Retention Rate: 100% (80/80 rows retained) - No observations were excluded due to missing prices or quantity values, ensuring the full dataset contributed to elasticity estimation
Rows Removed: 0 - The absence of data loss suggests either high initial data quality or lenient filtering criteria
Train/Test Split: N/A - The analysis uses all 80 observations for a single cross-sectional regression rather than temporal validation

Interpretation

The perfect retention rate supports model reliability by maximizing sample size for the elasticity coefficient estimation. However, with only 8 products and an R² of 0.015, the small sample and weak explanatory power suggest preprocessing quality alone cannot overcome fundamental data limitations. The cross-product design means each product contributes one observation to the regression, making the analysis vulnerable to product-specific confounds (brand strength, quality differences) that preprocessing cannot address.

Context

The analysis explicitly notes this is cross-product elasticity rather than within-product own-price elasticity, a critical limitation that preprocessing decisions