The Art of Statistics: Learning from Data

by David Spiegelhalter

four main modelling strategies have been adopted by different communities of researchers: Rather simple mathematical representations for associations, such as the linear regression analyses in this chapter, which tend to be favoured by statisticians. Complex deterministic models based on scientific understanding of a physical process, such as those used in weather forecasting, which are intended to realistically represent underlying mechanisms, and which are generally developed by applied mathematicians. Complex algorithms used to make a decision or prediction that have been derived from an analysis of huge numbers of past examples, for example to recommend books you might like to buy from an online retailer, and which come from the world of computer science and machine learning. These will often be ‘black boxes’ in the sense that they may make good predictions, but their internal structure is somewhat inscrutable – see the next chapter. Regression models that claim to reach causal conclusions, as favoured by economists.