Use the cps09mar . csy dataset to estimate the effect of education on earnings. Data description and variable definitions can be found in the document cps09mar_description. pdf. For all questions below, use the sub-sample of non-Hispanic women at least 23 years old. Research Questions 1. (20 points) Load this dataset in R (2 points). Create a new variable wage = earnings/ (hours x week). Obtain summary statistics (mean, standard deviation, 25, 50 (median), and 75 percentiles) for wage and education (5 points). Plot histograms for these two variables to explore their distributions. Make your histograms reader-friendly; that is, give informative ti- tles and variable names instead of just using the default titles and variable names ( points) . For example, you could use Years of Schooling in place of education. Create a new variable In (wage) - and draw a scatter plot of In( wage) versus education (5 points). Comment on the correlation between these two variables (2 points). 2. (25 points) Estimate the simple linear regression model: In(wage, ) = Bo + Bjeducation; + e;. where e, is the error and Bo and B, are the unknown population coefficients. ' In R, the function log() computes logarithms, by default natural logarithms