1)A model is linear when each term is either a constant or the product of a parameter and a predictorvariable. A linear equation is constructed by adding the results for each term. This constrains theequation to just one basic form:Response = constant + parameter * predictor + ... + parameter * predictorY = b o + b1X1 + b2X2 + ... + bkXkIn statistics, a regression equation (or function) is linear when it is linear in the parameters. While theequation must be linear in the parameters, you can transform the predictor variables in ways thatproduce curvature. For instance, you can include a ...
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