Some variable, or set of variables, y, are predicted to have a particular relationship with
some predictor variable (or variables) denoted in x.
In the simplest case when both x and y are continuous variables, the analysis is called a regression analysis, if x has more than one predictor variable then it is called a multiple regression, and if y is binary it is a logistic regression.
However, if the predictor variable is categorical the model is called an analysis of variance with many variants depending upon the number and relationship of categorical predictor variables in x. Finally, if predictor variables consist of categorical and continuous variables then it is called an analysis of covariance
Linear probability model (LPM) is one where the probability of an event occuring is tested. the dependent variable is binar.
Linear regression is suitable for measurement var, but not suitable for nominal variables like ratios and proportions either. Cobning measurement vars with nominal vars in a regression test, would produce erroneous results. In the case of nominal vars, logistic regression is more appropriate