The data below includes the individuals (who are identified by ID numbers i = 1,...,20) and their monthly wages (in thousands), gender, education levels (years of education), experience (years of work), and the regions where they live. Table 1: Observations ID Wage Education Experience Gender Region 1 17.18 8 1 Female Aegean 2 32.73 16 5 Male Aegean 3 18.81 16 11 Female East Anatolia 4 33.27 12 6 Male Black Sea 5 35.50 16 18 Male 6 32.24 16 15 Male Aegean Aegean 7 19.69 16 4 Female Black Sea 8 17.41 12 15 Female East Anatolia 9 17.69 8 5 Female Black Sea 10 25.90 16 19 Female Marmara 11 24.54 12 5 Male Black Sea 12 21.27 16 0 Female Black Sea 13 28.66 12 11 Male East Anatolia 14 24.23 8 8 Male East Anatolia 15 18.40 16 4 Female Aegean 16 30.48 16 16 17 31.41 16 67 Male East Anatolia 17 Male 18 38.67 16 12 Male Aegean Marmara 19 33 20 16.12. 26 12 13 Male Aegean 16 0 Female Aegean 1 (a) Interpret the data. Do you think eyeballing helps you to make an inference that regional and gender differences are projected in wages? Are there any outliers? (15 Points) (b) Your manager asks you to study the impact of being a college graduate (16 years) and/or high school graduate (12 Years) on earnings relative to having a secondary school diploma. What would be your strategy to estimate this model? Write down the regression model. Construct the dummy variables if it is necessary. (15 Points) (c) Create dummy variables to measure the regional effects and write the first five observations of those dummy variables. How many dummy variables you should create to avoid dummy variable trap? (20 Points) (d) Do you think R2 will be higher if we include more independent variable to the wage model? (15 Points) (e) You are given below the correlation matrix for variables Education, Experience, and Female. Suppose that we are interested in finding the impact of education on earnings. Based on the correlation matrix, should we care about omitted variable bias? If we should, what would be your next action? (15 Points) Table 2: Correlation Matrix Education Experience Female Education 1 0.27 -0.01 Experience 0.27 1 -0.40 Female -0.01 -0.40 1