You have weekly store-level unit sales data as well as binary promotion information about if the product was on display or not. You run a linear regression model [yumtsges = .31] + lmdgsplay) where the dependent variable is unit sales and the independent variable is the display [Yes=1 and No=0) and get the following output. Parameter standard t p_- Estimate error statistic value Intercept 158.69 20.29 7.82.001 Display 238.33 33.62 7.09 .001 Using the Display variable, imagine that you made a new independent variable NoDisplay, such that NoDisplay=O when Display=1 and NoDisplay=1 when Display=O. You run a second linear regression using the same data and the NoDisplay variable (yummales = 160 + 13117;\") displayl- Regarding these two models, which if the following statements is FALSE? Parameter standard t p_- Estimate error statistic value Intercept ? ? ? ? NoDisplay ? ? ? ? O The estimate of the NoDisplay parameter will be the same as the Display parameter in the rst model, only the sign will change 0 The estimate for the intercept will be the same 0 The t statistic for the NoDisplay parameter will be the same as the Display parameter in the rst model. only the sign will change 0 The pvalue for the NoDisplay parameter will be the same as the Display parameter in the rst model