Final answer:
The statement is false because the roles of variables in simple linear regression are specific: the independent variable (X) predicts the dependent variable (Y), and they are not interchangeable.
Step-by-step explanation:
The statement, 'With simple linear regression it does not matter whether variable X or Y is specified as the explanatory variable,' is false. In simple linear regression, the choice of the independent variable (X) and the dependent variable (Y) is crucial because the goal is to predict the value of the dependent variable based on the independent variable. The independent variable is the one that is presumed to cause, influence, or predict the outcome of the dependent variable.
For example, if we consider the relationship between hours studied (X) and exam score (Y), we would typically set hours studied as the independent variable because we want to see how changes in study time affect exam scores, not the other way around. The least-squares regression line, given by the equation ŷ = a + bx, identifies the best-fitting line through the data points where 'b' represents the slope and 'a' the y-intercept. The variables are not interchangeable because the slope and intercept have different interpretations depending on which variable is considered the predictor.