Even in the modern era, countries differ dramatically in terms of the health of their populations. For example, average life expectancy is in the 80's in some countries but remains in the low 40's in other countries. Some researchers claim that the most effective way to improve the health of the worst-off populations is to provide more education for people in these countries. To assess this argument, use the following analysis of data from a random sample of countries DEFINITION OF VARIABLES: Life expectancy at birth: Average age to which people born right now will live if exposed to current death rates in the country. % Literate: Percent of adults in the population that can read and write. % Urban: Percent of the country's population residing in cities. 1. Interpretation of Model 1. a. Write out the regression equation implied in the results for Model 1. b. Predict the life expectancy for a country in which 75% of the population can read and write. c. Interpret the meaning of the constant in Model 1. d. Interpret the slope coefficient for the effect of % Literate in Model 1. e. Is the association between literacy and life expectancy statistically significant? Explain how you reached your conclusion. f. Interpret the coefficient of determination in Model 1. g. Do these results support the idea that population health (as measured by life expectancy) is affected by education levels (as measured by % literate)? Explain your answer. 2. A critic of the education argument suggests that any association between literacy and life expectancy is a spurious byproduct of urbanization; in countries in which the population is concentrated in cities people have access to both schooling and health care, creating an association between literacy between literacy and life expectancy that is not causal. To test this argument, interpret Model 2 of the table above. a. Write out the regression equation implied in the results for Model 2. b. Predict the life expectancy for a country in which 75% of the population can read and write and 50% of the population lives in cities. c. Interpret the meaning of the constant in Model 2. d. Interpret the slope coefficient for the effect of % Literate in Model 2. e. Interpret the slope coefficient for the effect of % Urban in Model 2. f. Interpret the coefficient of multiple determination in Model 2 g. Do these results support the argument that the association between life expectancy and % literate is spurious? Explain your