This study explores the relationship between customer interaction channels and purchasing behavior for a New York City-based Ecommerce company specializing in clothing sales and personal styling services. Using linear regression analysis on customer data, we sought to determine whether the company’s mobile app or website more effectively drives customer purchases. Our model achieved a Root Mean Square Error (RMSE) of 1.8, reflecting a reliable predictive capacity, although with some room for further refinement. Findings suggest that customer engagement data can effectively guide strategic channel prioritization. These insights provide the company with a data-driven foundation to enhance customer experience and maximize revenue through targeted digital optimization efforts.

Shivani P - Assignment 3

This document presents an in-depth analysis of the factors influencing tire pressure reduction in NASCAR race cars using R's Wilkinson-Rogers formula notation for statistical modeling. By leveraging the lm() function and creating various formula configurations, this analysis examines relationships between tire pressure and environmental, driver, and vehicle conditions. Key variables such as lap number, ambient and track temperatures, driver aggression level, and pit stops are modeled to determine their impact on tire pressure. The document also explores interactions between variables, higher-order effects, and the use of shorthand notation to streamline model creation. A summary table provides quick reference to different formula notations, offering insights into optimizing performance and safety on the track