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

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