Zero-Inflated Models

Zero-inflated models are statistical tools used when analyzing data with more zeros than usual. For example, if counting the number of visits to a doctor, many people might not visit at all, resulting in many zeros. These models assume there are two processes: one that determines whether an observation is always zero (e.g., someone who never visits a doctor), and another that counts the number of events (e.g., visits) for those who might have some. This approach helps better understand and predict data with excess zeros, providing more accurate insights.