4. Logit

This chapter builds upon the fundamental conceptual framework for modeling choice-making behavior introduced in the previous chapter. It addresses the limitations of the linear probability model and introduces the logit model, one of the most popular models in discrete choice analysis. The logit model is derived from the Extreme Value Type I distribution, which allows for a more realistic representation of choice behavior. The chapter also explores the properties of the logit model, including its ability to handle substitution patterns and its independence from irrelevant alternatives. Additionally, it provides insights into specifying systematic utilities and the impact of attributes on choice probabilities. Throughout the chapter, practical examples and exercises are included to enhance understanding and application of the logit model.