U.S Statistics Based on Poverty rate, Unemployment rate, Transfer Payment Spending per Capita, Inflation rate (CPI rate), and percentage of college graduation completion rate
U.S Statistics Based on Poverty rate, Unemployment rate, Transfer Payment Spending per Capita, Inflation rate (CPI rate), and percentage of college graduation completion rate
Qn. A
Start with the data set you tabulated for the previous assignment – for US states cross-sectional data on poverty rate, state unemployment rate, and transfer payment. Raise the number of states at least to 35 for different issues. Add two variables state of inflation rate, state CPI or cost of living index is fine, and the percentage of college graduation completion rate in the state.
Answer:
| Name of the State | Poverty rate, % | Transfer Payment Spending per Capita in dollars | Unemployment rate | Inflation rate – Cost of living Index | % of college graduation completion rate, Bachelor’s degree |
| Alabama | 19.2 | 36538 | 3.8 | 89.5 | 24.5 |
| Alaska | 11.4 | 67411 | 7.3 | 70.4 | 29.0 |
| Arizona | 18.2 | 38427 | 4.9 | 60.7 | 28.4 |
| Arkansas | 18.7 | 36249 | 3.8 | 72.9 | 22.0 |
| California | 16.4 | 55,374 | 4.3 | 71.6 | 32.6 |
| Colorado | 12.1 | 52019 | 3.0 | 68.8 | 39.4 |
| Connecticut | 10.8 | 62236 | 4.5 | 79.1 | 38.4 |
| Delaware | 13.0 | 63271 | 4.3 | 75.2 | 31.0 |
| Florida | 16.6 | 38398 | 3.9 | 58.2 | 28.5 |
| Georgia | 18.4 | 43313 | 4.4 | 66.5 | 29.9 |
| Hawaii | 11.5 | 49497 | 2.1 | 63.2 | 32.0 |
| Idaho | 14.8 | 35099 | 2.9 | 67.9 | 26.8 |
| Illinois | 14.3 | 52795 | 4.6 | 61.9 | 33.4 |
| Indiana | 15.2 | 44577 | 3.2 | 82.6 | 25.3 |
| Lowa | 12.3 | 49218 | 2.8 | 70.9 | 27.7 |
| Kansas | 13.5 | 46003 | 3.4 | 78.9 | 32.3 |
| Kentucky | 19.0 | 38298 | 4.0 | 67.5 | 23.2 |
| Louisiana | 19.9 | 44254 | 4.4 | 73.9 | 23.4 |
| Maine | 14.0 | 38014 | 2.7 | 78.7 | 30.3 |
| Maryland | 10.4 | 54003 | 4.3 | 75.8 | 39.0 |
| Massachusetts | 11.7 | 62510 | 3.5 | 80.5 | 42.1 |
| Michigan | 16.2 | 41514 | 4.7 | 69.2 | 28.1 |
| Minnesota | 11.4 | 53005 | 3.2 | 80.1 | 34.8 |
| Mississippi | 21.9 | 31522 | 4.5 | 87.5 | 21.3 |
| Missouri | 15.5 | 42442 | 3.6 | 69.0 | 28.2 |
| Montana | 15.2 | 39214 | 4.1 | 77.0 | 30.7 |
| Nevada | 15.4 | 42947 | 4.9 | 62.4 | 23.7 |
| Ohio | 15.8 | 46385 | 4.4 | 77.3 | 27.2 |
| Oregon | 16.4 | 48342 | 4.1 | 63.7 | 32.3 |
| Oklahoma | 16.6 | 45007 | 4.0 | 85.8 | 24.8 |
| South Dakota | 14.1 | 34839 | 3.4 | 77.3 | 27.8 |
| Rhode Island | 14.8 | 40293 | 4.5 | 68.0 | 33.0 |
| Wisconsin | 13.2 | 33712 | 2.9 | 77.9 | 29.0 |
| Vermont | 12.2 | 33159 | 2.8 | 63.8 | 36.8 |
| Pennsylvania | 13.6 | 41472 | 4.8 | 73.2 | 30.1 |
- Set expectations and hypothesis testing – again the poverty rate being the dependent variable and four independent variables, including both new and the previously used variables.
Answer: From the collected data, it is expected all independent variables to be proportional to the dependent variable, poverty rate, regardless of how big values one possesses. After plotting, the expectations which was also the hypothesis was found to be incorrect putting suggestions that the data is non-linear and had some multicollinearity.
- Set hypothesis, run two regressions: one with all the variables – poverty rate being the dependent variable and another without the state inflation rate, now you have three regressions including the one from the previous assignment. Properly report results based on convention.
Answer: The two regression have no strong correlation. The reason behind is because they have not proportional to the dependent variable.
After plotting the regression, the hypothesis was found to be true as seen in the values of R squared in the above graph.
- Interpret the result of the full equation based on expectation based on a theoretical/literature backing and on statistical grounds (robustness of individual estimated coefficients of the parameters and overall fitness also see if there is possible of multicollinearity).
Answer: From the regression curve for the four variable, there is multicollinearity of two variables which did not appear in the curve that is the inflation rate – cost of living index together with the unemployment rate. Besides, the coefficients of the two linear regression curves, the appearing one are have no strong correlation because their regression values, R, are less than 0.9. They are 0.5661 and 0.7493 for Transfer Payment Spending per Capita in dollars and % of college graduation completion rate, Bachelor’s degree respectively.
- If the base for comparison is the equation with three dependent variable, establish if there is a reason to believe your original equation is understated – with (an) omitted variable(s). Also, establish if the equation with four independent variables might have an irrelevant variable.
Answer: From the data and regression curves, there is no reason to believe that the original equation is understated. This is because there are two variables which did not appear in the graph as their values were too small and inconsistent which made them not to appear. Besides, this is a proof the equation with the four independent variables had some irrelevant variable.
- Visually decide with a scatter diagram if one of variable may have a nonlinear relationship with the dependent variable – in reality this is done with all of the independent variables.
Answer: From the scatter diagram, graph, above, the cost of living, unemployment rate and % of college graduation completion rate, Bachelor’s degree are not linear in connection with the poverty rate. Their lines cannot be traced graphically as they are have no single relationship. In this connection on Transfer Payment Spending per Capita in dollars can be linearly interpreted with poverty rate.
References
Goodwin, N., Harris, J. M., Nelson, J. A., Roach, B., & Torras, M. (2015). Macroeconomics in context. Routledge.