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Linear regression is the most basic and commonly used predictive analysis. Regression estimates are used to describe data and to explain the relationship between one dependent variable and one or more independent variables.
For nominal data, if the response is a binary variable (that is only two possible values such as graduate in four years or not), then, use Logistic regression model. If the response has more than two categories, use multinomial logistic regression. For ordinal data, if the response follows Poisson distribution, use Poisson regression model. In general, one can use log-linear models for ordinal data. In many applications, the relationship between response variable and predictors are not linear, but may be linearized. Generalized linear modeling techniques are useful.