Using logistic regression to investigate the relationships between rating scores on TireRatings and respondent purchasing likelihood.

SJSU Bus2 194a - Class Exercise 15: Chapter 15.9 - Basic Logistic Regression Model

This document provides an analysis of logistic regression for predicting the probability of tire purchase based on performance ratings (Wet and Noise). The steps include data import, cleaning, model creation, and probability estimation

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homework coding for class exercise 15 dealing with a logistic regression model.

This analysis explores the relationship between Wet and Noise ratings and the likelihood of customers purchasing tires again. Using a logistic regression model, we assess the significance of predictors, evaluate model performance through metrics like McFadden R-squared and AUC, and predict purchase probabilities for different scenarios. The results demonstrate how tire performance impacts customer decisions.

Predicting tire repurchases for Tire Rack.

TireRatings