The meaning of the linearity assumption of the linear regression model is not that the relationship between y and a is linear, but that the model is linear in parameters. a. Suppose In(yi) = Bo + Bid; + wi. Use the fact that In(exp(a)) = a to write y as a function of a, i.e. an equation with y on the lefthand side rather than In(y). Solution: b. Suppose In(yi ) = Bo + Bi In(ci ) + wi. Use the fact that In(exp(a)) = a, bln(a) = In(a"), exp(a + b) = exp(a) exp(b), and exp(In(a)) = a to show that yi = exp(Bo)x," exp(ui). Solution: C. Explain in your own words how these examples illustrate that the assumption of linearity does not prevent us from applying the LRM to situations where the relationship between y and a is non-linear. Solution: d. Does yi = Bo + Biz + uj violate the linearity assumption