Suppose we want to predict sales for Johnson & Johnson. Using observations from the first quarter of 1985 to the fourth quarter of 2001, we estimate an AR(1) model using ordinary least squares on the first-differenced data. We estimate the following equation: (In Sales, - In Sales,-1) = bo + b1(In Sales,-1 - In Sales,-2) + Er. The following table shows the results of the regression Log Differenced Sales: AR(1) Model Johnson & Johnson Quarterly Observations, January 1985-December 2001 Regression Statistics R-squared 0.1957 Standard error 0.0408 Observations 68 Durbin-Watson 2.1226 Coefficient Standard Error t-Statistic Intercept 0.0297 0.0058 5.1334 Lag 1 -0.1956 0.1207 -1.6210 Autocorrelations of the Residual Lag Autocorrelation Standard Error t-Statistic -0.0616 0.1213 -0.5083 WNH -0.3634 0.1213 -2.9969 -0.1355 0.1213 -1.1170 0.6030 0.1213 4.9728 A. Using the regression output in the above table, determine whether the estimates for bo and b1 are valid. B. If this model is mis-specified, describe the steps we should take to determine the appropriate autoregressive time-series model for these data