The researcher specifies an empirical model in regression analysis. A very common model is the straight-line model, which is used to test if there is a linear relationship between independent and dependent variables. The variables are said to be correlated if a linear relationship exists. However, correlation does not prove causation, as both variables may be correlated with other, hidden, variables, or the dependent variable may "reverse" cause the independent variables, or the variables may be otherwise spuriously correlated. For example, suppose there is a correlation between deaths by drowning and the volume of ice cream sales at a particular beach. Yet, both the number of people going swimming and the volume of ice cream sales increase as the weather gets hotter, and presumably the number of deaths by drowning is correlated with the number of people going swimming. An increase in swimmers may cause both the other variables to increase.