A common task in data analysis is model building. Models are used to understand how a set of measured variables explains other variables. These commonly are commonly known as regression problems.

A common type of problem in statistics is predicting the category of an object based on its measurable properties.  For instance, who will live or die from a surgery based on characteristics like age, blood pressure, and so on.  These classification rules are based on cases where the category of each object is known, and their associated quantitative properties measured.  A well-known statistical method called Discriminant Functions makes the prediction of category membership through rules expressed as equations.

It is the nature of explanation that we account for something in terms of something else.  Analytic data methods aimed at explanation utilize a Y-frame that denotes the set of things we are trying to account for, and an X-frame that is the set of things doing the explaining. In traditional practice a regression model is proposed to capture the linkage between X and Y. 

To an analyst, language descriptions can feel slippery and vague. Dictionaries don’t even define the meaning of quantifying adverbs. Numbers can be measured with great precision and their mathematical manipulations are well defined. With this sense of exactitude why switch to language?

The short answer is that language is the medium people use to state conclusions from the findings of analytic efforts. In statistical studies these are generalizations about the patterns discovered in empirical data. And language is the primary way people share the rules-of-thumb we pass around as knowledge.