Question 1: Presentation of a data in a table (relegated to an appendix)

 Name of the State Poverty rate, % Transfer Payment Spending per Capita in dollars Unemployment rate Alabama 19.2 36538 3.8 Alaska 11.4 67411 7.3 Arizona 18.2 38427 4.9 Arkansas 18.7 36249 3.8 California 16.4 55,374 4.3 Colorado 12.1 52019 3.0 Connecticut 10.8 62236 4.5 Delaware 13.0 63271 4.3 Florida 16.6 38398 3.9 Georgia 18.4 43313 4.4 Hawaii 11.5 49497 2.1 Idaho 14.8 35099 2.9 Illinois 14.3 52795 4.6 Indiana 15.2 44577 3.2 Lowa 12.3 49218 2.8 Kansas 13.5 46003 3.4 Kentucky 19.0 38298 4.0 Louisiana 19.9 44254 4.4 Maine 14.0 38014 2.7 Maryland 10.4 54003 4.3 Massachusetts 11.7 62510 3.5 Michigan 16.2 41514 4.7 Minnesota 11.4 53005 3.2 Mississippi 21.9 31522 4.5 Missouri 15.5 42442 3.6 Montana 15.2 39214 4.1 Nevada 15.4 42947 4.9 Ohio 15.8 46385 4.4 Oregon 16.4 48342 4.1 Oklahoma 16.6 45007 4.0

Question 2: State expectation and set up hypothesis and speculate on type 1 and II errors of coefficients wherever possible.

Answer: The expectations are that the poverty rate should be higher than the unemployment rate together with transfer money per capita. The two types of errors were assumed to have no effects on the results obtained thereby not put into consideration.

Question 3: Run two regressions:

1. One with poverty line being the dependent variable and state unemployment rate being the independent variable; present the result as it is done conventionally including the supporting information such as statistics, R-square, and the number of observations.

R² = 0.0254, number of observations = 8, they are the crossed point in the above graph.

Show on a graph the estimated line and pick an observation to show the total error, estimated error, and the residual error.

From the graph, the estimated is not accurate because the plots are not near the line. This, in turn, makes the precision to be lower. The same applies to total and residual error.

Residual error = 8-4

= 4 crossed and slightly crossed dots minus the far away placed dots.

1. Another regression with the poverty line is the dependent variable and the other two variables as the independent variables and present the results conventionally.

Question 4: Interpret your results (of the second equation) on its compliance with expectations, tests of robustness, and the parameter statistics.

Answer: Linear (series2) shows that the rate of poverty is higher at the lower Unemployment rate, %, & Transfer payment spending per capita in dollars. Also, from the same, the poverty rate is lower when the Unemployment rate, %, & Transfer payment spending per capita in dollars is higher. This indicates that the poverty rate is inversely proportional to the Unemployment rate, %, & Transfer payment spending per capita in dollars.

Linear (series1) shows that the rate of poverty is higher at the higher Unemployment rate, %, & Transfer payment spending per capita in dollars. Also, from the same, the poverty rate is lower when the Unemployment rate, %, & Transfer payment spending per capita in dollars is even lower. This suggests that the poverty rate is directly proportional to the Unemployment rate, %, & Transfer payment spending per capita in dollars.

Question 5: Speculate on the unexpected results and make recommendations for improving the model and policy within the context of results and theory.

Answer: The unexpected results were that the poverty rate was to inversely proportional so linear (series 2) was not suitable for the summation of the graph. This can be improved by working harder and smart to push the poverty line lower. By so doing, the Unemployment rate & Transfer payment spending per capita in dollars will reduce and rise respectively.

Question 6: Include computer output as an appendix.

Answer: The output of the computer is the same as the data presented in the question. Therefore, refer to question 1 above.

References

Goodwin, N., Harris, J. M., Nelson, J. A., Roach, B., & Torras, M. (2015). Macroeconomics in context. Routledge.