UNVEILING UNSEEN INSIGHTS: QUALITY MANAGEMENT AND BUSINESS OPTIMIZATION THROUGH THE ANALYSIS OF PRODUCT SALES PATTERNS WITH CATEGORICAL AND CONTINUOUS PREDICTORS IN A UNIQUE DATASET

Abstract

This investigation explores e-commerce product sales patterns, integrating Quality Management and Business Optimization perspectives. We analyze product listings, ratings, and sales metrics using a unique dataset from data.world.com, sourced from Wish.com. Our predictive model unveils correlations among categorical and continuous predictors, spotlighting their role in predicting unit sales. Employing robust linear regression, we assess predictor significance via t-tests, hypothesis evaluations, and ANOVA. Model selection, guided by AIC, identifies the optimal fit. Outliers, influential points, and assumptions are evaluated. Employing data visualization, we present results comprehensively. This study empowers stakeholders with insights into e-commerce dynamics, Quality Management, and Business Optimization for informed decision-making.