Final Crucial Model: A Case Study

There are many models with one variable and one model with many variables, but what do they all mean? Each model showed different levels of significance, different direction of relationships, and different strengths and all contributed to the final multivariate model. This final model will give the best indication about the earlier prediction’s validity. To reiterate, the hypothesis is that foreign born percentage (a.k.a amount of immigrants) of a county will have little to no effect on the percent of families in poverty in the county. This hypothesis is proved correct as there are other factors instead of foreign born that play a larger role in the well-being of the economy. As exemplified by the results of the final model, unemployment rate and percent of high school competition played the largest role of the selected variables in predicting poverty rates. These results make sense in context. If more people have jobs, then more
Instead, it was more likely that other factors would have a much more significant impact on how well families would do economically. This prediction was correct as percent foreign born was not included in the final model as it was not significant in affecting the dependent variable. Beyond proving the hypothesis correct, the conclusions from this analysis have larger implications. Since immigration has been a divisive issue in American Politics and since candidates can easily be identified by their views on immigration, there is much said about the impacts and effects of more immigrants. Some candidates embrace an open policy to let people in so they can find a better life. Those who want a tighter immigration policy cite economic issues as reasons for limiting entrance. Their arguments claim that immigrants will take American jobs and make the work environment more