Is probit better than logit?

Logit and probit models share a fundamental similarity in their purpose and application, yet they differ significantly in the underlying distribution they employ. The Logit model utilizes the cumulative standard logistic distribution, denoted as F, to analyze and predict binary outcomes.

On the other hand, the Probit model adopts the cumulative standard normal distribution, represented by Φ, for its analytical framework. Despite this mathematical distinction, both models serve similar functions and often yield comparable results in practical applications.

The primary objective of both Logit and Probit happening models. is

When interpreting the results from Logit and Probit models, the focus is on the combined effect of all variables included in the model. This cumulative effect indicates whether the overall impact of these variables, taken together, is significantly different from zero. A statistically significant result implies that the model is able to explain the variation in the dependent variable beyond chance.

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