Multiple (linear) regression is a regression technique aimed at finding a linear relationship between the dependent variable and multiple independent variables. (See regression analysis.)
The multiple regression model is as follows:
where Yi are values of the dependent variable, X1i, X2i, … , Xmi are values of m independent variables, Ei – random errors, N > m+1 is the sample size.
Multiple regression finds the set of parameters B0, B1, … , Bmi that provides the best fit between the model and the given data (which are a set of N vectors – {(Yi, X1i, … , Xmi), i=1,…,N}).
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