Why Can You Have More Than One Dependent Variable. Even when you fit a general linear model with multiple independent variables the model only considers one dependent variable. If you have multiple dependent variables youll need to fit separate models.
Testing the combined effects of vaccination vaccinated or not vaccinated and health status healthy or pre-existing condition on the rate of flu infection in a population. For example if you are interested in the effect of a diet on health you can use multiple measures of health. When you have more than one independent variable in your analysis this is referred to as multiple linear regression.
Regular ANOVA tests can assess only one dependent variable at a time in your model.
You may want to simultaneously optimize many different responses. In these experiments there may be more than one set of measurements with different variables. Multiple regression model is one that attempts to predict a dependent variable which is based on the value of two or more independent variables. Each of these is its own dependent variable with its own research question.
