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Communication researchers often gather quantitative data – for example from surveys, content analyses, or experiments – and then generate mathematical models to represent or summarize those data (→  Survey ; Content Analysis, Quantitative ; Experimental Design ). These models are used in two basic ways: first, to generate predictions about certain variables; and second, to study relationships among some number of variables ( Allison 1999 ). In some cases, the goal is to obtain as accurate a prediction as possible, without much regard to the particular variables used to make the prediction. In most cases, at least in communication research, the focus is on the pattern of relationships among particular variables, and especially on testing hypotheses made in advance about how variables are expected to interrelate (→  Hypothesis ; Statistics, Explanatory ). Models represent the relationship between a dependent or outcome variable and one or more independent or predictor variables, by stipulating the former as some mathematical function of the latter. Models contain parameters that express the function, usually a set of weights applied to independent variables. The vast majority of the models used in communication research are linear models , which represent the functional relationship as a line (in two dimensions) or as a linear surface (in three or more dimensions). For example, ... log in or subscribe to read full text