It is useful at this point to write the linear regression model in a very general form: Let z = z1, z2,..., zL be a set of L independent variables; let /1, f2,..., fK be K linearly independent functions of z; let g(y) be an observable function of y; and retain the usual assumptions about the disturbance. The linear regression model is g(y) = 01 f1(z) + 02 f2(z) + ■ ■ ■+ 0KfK(z) + S

By using logarithms, exponentials, reciprocals, transcendental functions, polynomials, products, ratios, and so on, this "linear" model can be tailored to any number of situations.

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