In statistics, specifically regression analysis, a binary regression estimates a relationship between one or more explanatory variables and a single output binary variable.
Generally the probability of the two alternatives is modeled, instead of simply outputting a single value, as in linear regression.
The latent variable interpretation has traditionally been used in bioassay, yielding the probit model, where normal variance and a cutoff are assumed.
The latent variable interpretation is also used in item response theory (IRT).
Then the manager will invest only when she expects the net discounted cash flow to be positive.
is assumed to follow a normal distribution conditional on the explanatory variables x.
The logit model is "simplest" in the sense of generalized linear models (GLIM): the log-odds are the natural parameter for the exponential family of the Bernoulli distribution, and thus it is the simplest to use for computations.