The data here represent life expectancies (Life) and per capita income (Income) in 1974 dollars for 101 countries in the early 1970's. The source of these data is: Leinhardt and Wasserman (1979), New York Times (September, 28, 1975, p. E-3). They also appear on Regression Analysis by Ashish Sen and Muni Srivastava. You can access these data in R using:

life <- read.table("http://www.stat.ucla.edu/~nchristo/statistics12/countries_life.txt", header = TRUE)

a. Construct a scatterplot of Life against Income. Note: Income should be on the horizontal axis. How does income appear to affect life expectancy?

b. Construct the boxplot and histogram of Income. Are there any outliers?

c. Split the data set into two parts: One for which the Income is strictly below $1000, and one for which the Income is at least $1000. Come up with your own names for these two objects.

d. Use the data for which the Income is below $1000. Plot Life against Income and compute the correlation coefficient.Hint: use the function cor()