\fAdata analyst for the Urban Institute, an independent Washington DC. based research rm, is interested in predicting legal immigration into the US. She estimates two different models, with results from the models provided below. The level of signicance is .05. NOTE: US immigration policy allows foreign nationals to enter the country legally under different regulations. These include lottery, quota based, political/war refugee, marriage to a US citizen, and critical skill groups. 1. Why would the analyst code Entry Status as a dummy variable? 2. Reference Model #1 what is the expected sign on the coefcient for US Unemployment rate and WHY? You must justify your answer. 3. Reference Model #2 what is the expected sign on the coefcient for Country of Origin Average Wage and WHY? You must justify your answer. 4. Propose an additional independent variable that could be included in the model. What is the expected relationship between your proposed independent variable and legal immigration? Why would you include the proposed independent variable? 5. Two consultants, Dr. Black and Dr. Stone review the data analyst models. Dr. Stone concludes that Model #1 is the better model for predicting legal immigration. Dr. Black concludes that Model #2 is best for predicting legal immigration. The data analyst concludes that both models are good models for predicting legal immigration. Who is correct; Dr. Stone, Dr. Black or the data analyst and WHY? This requires some thought and is not as easy as it seems....NO not a trick question it's a thinking question. Model #1 Model #2 R squared .59 R- squared .58 Signicant F .023 Signicant F .015 Independent Variables Independent Variables US . . Country of Origin Unemployment p-value = .011 p-value = .017 unemployment rate rate US GDP growth rate p-value = .045 US GDP Growth rate p-value = .042