A statistical technique that can be used to analyze the relationship between a single dependent variable and several independent variables. The objective of multiple regression analysis is to use the independent variables whose values are known in the past and future to predict the future values of the single dependent variable. Each predictor variable (Xi) is weighted, the weights (bn) denoting their relative contribution to the overall prediction. In calculating the weights, the regression analysis procedure ensures maximal prediction from the set of independent variables. These weights also facilitate interpretation as to the influence of each variable making the prediction, although correlation among the independent variables can complicate the interpretative process.
General Notation:
Y = b0 + bX1 + bX2 + bX3...bXn + ei
Where:
Y = Dependent variable
b0 = Y intercept or constant
bn = Coefficients or weights
Xi = Independent variables
ei = Residual or prediction error
Example:
Consumer demand for product Y = b0 + bprice + badvertising + bmerchandising + bdistribution + bcompetitive price
More terms such as multiple linear regression in Dictionary M.
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