Relationship Between Capita And College Graduation Rate

I have chosen to compare the relationship between average life expectancy, per capita personal income, and college graduation rate by state in 2010. I intend to prove that average life expectancy by state, the dependent variable, will either positively or negatively correlate with income and college graduation rate, the independent variables. The null hypothesis (H0) for my independent variables is that there will be absolutely no relationship between income or college graduation rate and average life expectancy. On the other hand, the alternative hypothesis (Ha) for each independent variable will be that income and college graduation rate do relate to average life expectancy. As for the background of my topic, I chose to test these†¦show more content†¦Kaiser Family Foundation website in the State Health Facts section on Life Expectancy at Birth (in years). This information was given for the 2010 time frame for all states in the United States. For per capita personal income by state, I gathered my data from a table from the Bureau of Business Economic Research website looking only at the 2010 column. For college graduation rate by state, I utilized the U.S. Department of Education website article New State-by-State College Attainment Numbers Show Progress Toward 2020 Goal. The information I used came from percentages in the table inside the article under the column â€Å"Graduates as of 2010†. Based on the variables that I chose to test, I expect that per capita personal income will be more closely related to my dependent variable, average life expectancy, than college graduation rate for the year 2010. Higher income tends to allow for people to purchase the higher-end health or medical products necessary for longevity and better health. In my opinion, college graduation rate influences a person’s personal income that then influences life expectancy – a secondary piece of the correlation. The Excel Regression tool built a linear model using my data and variables to produce a summary output of my regression analysis. My equation provided by my Excel regression model can be written as Y=74.7255954+(-0.0000309729)X1+(12.95779303)X2+ÃŽ µ. This may seem difficult to understand so let me break it down. The regular