Employment in different areas. A set of data was retrieved from the Occupational Employment Statistics (OES)

Survey conducted by the Department of Labor in May 2019 (https://www.bls.gov/oes/). The data contains the mean annual wage ($), the total number of employments rounded to the nearest 10 with excluded self-employment and the number of jobs for given occupation from three different areas—Alabama, Alaska, and the District of Columbia. A regression model to predict the mean annual wage from the total number of employments and the number of jobs in the Area (0 = Alabama, 1 = Alaska, and 0 = District of Columbia) gives the following result:

Dependent variable is: the mean annual wage ($)

R-squared = 0.1228, Adjusted R-squared: 0.0803 SE = 29332.1314 with 66 observations Variable Coefficient SE(Coeff) t-ratio P-value Intercept 66272.7934 6517.3825 10.1686 0.0000 Total Employment -0.3061 0.1053 -2.9062 0.0051 Jobs 361.9165 148.6489 2.4347 0.0178 Area -16001.2093 9113.9666 -1.7557 0.0841

a) What is the interpretation of the coefficient of Area in this regression? According to these results, which area would you expect to have higher mean annual wage?

b) What displays would you like to see to check assumptions and conditions for this model?