 Probit model

Binary probit regression sets $p_i=\Phi ({\beta }_0+x_i^T\beta )$, where $\Phi$ is the standard normal distribution function. Its intercept score is a weighted sum of residuals, so an intercept does not generally force ${\sum }_i{\hat{p}}_i={\sum }_i{y}_i$.