ABSTRACT
IN CHAPTER 8 WE examined methods of assessing the statistical relationship between two variables. The correlation coefficient is one such measure of association but, useful as this measure might be, it does not allow us to predict the numerical value of one variable based on the other. Neither does correlation make any assumptions on causation, for example that it is one of the variables that controls the behaviour of the other. The importance of regression analysis is that it goes much further than correlation, and it enables us to make a numerical prediction of one variable by reference to another. In order, however, to embark on this procedure we must decide on the direction of causation, and which is the dependent variable and which is the independent, i.e. which variable controls the other. Statistical convention dictates that the dependent variable is termed Y, and the independent variable X. For example, mean annual UK rainfall is known to increase with altitude. In this situation rainfall (Y) could be argued to depend on altitude (X). The reverse would not make scientific sense, although in some situations the dependency relationships are far less easy to distinguish, especially in areas of human geography.
