![]() ![]() Just like in MR we want to create linear combinations of the set of IVs (X1-X3). The figure below gives us an idea of what is going to happen. To begin with, it helps to visualize what we’re about to do. In a sense it can be thought of multivariate regression though multiple regression is actually a special case of canonical correlation. Canonical correlation analyzes the relationship between sets of variables, with one set of variables typically seen as the independent set and another as the dependent set, though the causal arrow is not necessarily specified. The square of that correlation between the linear combination and the dependent variable (DV) is the amount of variance in the dependent variable accounted for by the predictors.Īlthough it is easy to think of the independent variables as a set that one believes has some relation to the dependent variable, many do not as often think of a set of dependent variables that one wishes to predict. A linear combination of the independent variables (IVs) is created that will have the minimum squared errors in prediction. Many in the social sciences often employ multiple regression (MR) to solve the problem of how several variables predict another variable. ![]()
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