diffrrence between linear and non linear regression ststistics
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A model is linear when each term is either a constant or the product of a parameter and a predictor variable. A linear equation is constructed by adding the results for each term. This constrains the equation to just one basic form:
Response = constant + parameter * predictor + ... + parameter * predictor
Y = b o + b1X1 + b2X2 + ... + bkXk
NON LINEAR=That covers many different forms, which is why nonlinear regression provides the most flexible curve-fitting functionality. Here are several examples from Minitab’s nonlinear function catalog. Thetas represent the parameters and X represents the predictor in the nonlinear functions. Unlike linear regression, these functions can have more than one parameter per predictor variable.
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