1. Summarize the findings and evaluate the models.

	model1	model2	model3
Observations	199	199	193
R2	0.301	0.596	0.907
Adjusted R2	0.298	0.594	0.902
Residual Std. Error	8.483 (df = 197)	6.448 (df = 197)	3.194 (df = 181)
F Statistic	84.975*** (df = 1; 197)	291.087*** (df = 1; 197)	160.917*** (df = 11; 181)

 #Run a simple bi-variate regression of Female Life expectancy (lifeExpF) on the GDP per capita variable (ppGDP) model1 <- lm(lifeExpF ~ ppgdp, data = UN) summary(model1) #Now, run a regression with female life expectancy and log(GDP) model2 <- lm(lifeExpF ~ log(ppgdp), data = UN) summary(model2) #Now run a multi regression using all of the variables and analyze the findings. model3 <- lm(lifeExpF ~ ppgdp + fertility + lifeExpF + pctUrban + infantMortality + region + group , data = UN) summary(model3) library(stargazer) stargazer(model1, model2, model3, type = "text")