Multiple regression also allows you to determine the overall fit (variance explained) of the model and the relative contribution of each of the independent variables to the total variance explained. If you have a dichotomous dependent variable you can use a binomial logistic regression. Alternately, you could use multiple regression to determine if income can be predicted based on age, gender and educational level (i.e., the dependent variable would be "income", and the three independent variables would be "age", "gender" and "educational level"). For example, you could use multiple regression to determine if exam anxiety can be predicted based on coursework mark, revision time, lecture attendance and IQ score (i.e., the dependent variable would be "exam anxiety", and the four independent variables would be "coursework mark", "revision time", "lecture attendance" and "IQ score").
Multiple regression (an extension of simple linear regression) is used to predict the value of a dependent variable (also known as an outcome variable) based on the value of two or more independent variables (also known as predictor variables). Multiple Regression Analysis using Stata Introduction
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