attractive prop of binary dependent var: produce predictions that can be interpreted as probabilities that Y=1.
- the parameter estimates of the slopes can be
- interpreted as marginal probabilities that y=1. this feature of LPM make it easier to inerpret than other models of binary dep vars.
good when dataset large, few predicted values near bounds.
Tools: regular OLS (LPM), logit MLE (cumulative distribution function), probit MLE