6.14 Testing for lack-of-fi (Data fie: MinnLand) Refer to the Minnesota farm sales data introduced in Problem 5.4. 6.14.1 Fit the regression model log(acrePrice) ~ year via ols, where year is not a factor, but treated as a continuous predictor. What does this model say about the change in price per acre over time? Call this model A. 154 chapter 6 testing and analysis of variance 6.14.2 Fit the regression model via log(acrePrice) ~ 1 + fyear via ols, where fyear is a factor with as many levels are there are years in the data, including the intercept in the model. What does this model say about the change in price per acre over time? Call this model B. (Hint: fyear is not included in the data fie. You need to create it from the variable year.) 6.14.3 Show that model A is a special case of model B, and so a hypothesis test of NH : model A versus AH : model B is reasonable. 6.14.4 A question of interest is whether or not model A provides an adequate description of the change in loglacrePrice) over time. The hypothesis test of NH : model A versus AH : model B addresses this question. and it can be called a lack-of test for model A