What is the interpretation of coefficients in probit regression?

The probit coefficient, denoted as b, holds a specific interpretation in statistical analysis. It signifies that for every unit increase in the predictor variable, there is a corresponding increase in the probit score by a magnitude of b standard deviations. This relationship underscores the sensitivity of the probit model to changes in the predictor.

Thinking and communicating in the Z metric, which is the standard normal distribution used in the probit model, can be a challenging task. It requires a shift in mindset from traditional probabilistic thinking to a more mathematical and abstract approach.

Understanding and interpreting the probit coefficient can be challenging, especially for those who are not familiar with the intricacies of statistical modeling. It requires a grasp of both the theoretical underpinnings and the practical implications of the probit model.

As with any new skill, mastering the use of the Z metric takes time and practice. Regular exposure to probit models and their applications can help develop a more intuitive understanding of the Z metric and its implications.

The probit score, as a transformation of the linear predictor, is not directly interpretable in its raw form. It is a latent variable that serves as an intermediate step in the process of estimating probabilities.