In your regression output table, there are four race variables: Black, White, Asian, and race_other. The "race_other" variable has been dropped from the analysis.

When you interpret the coefficient, the removed variable serves as the reference group. So, for all your race variables, the reference group is "race_other".

How would you interpret the coefficient for "Asian"?

A. There is about a 1.50 unit difference between Asian employees' view of the performance system's fairness and all other employees' views.

B. Because the "race-other" variable has been dropped from the analysis, we cannot accurately interpret the coefficients for any of the other race variables.

C. Asian employees' view of the performance system's fairness is predicted to be 0.042 units, compared to Black employees at 0.153 and White employees at 0.089 units.

D. On average, holding all other variables constant, Asian employees' view of the performance system's fairness is predicted to be 0.042 units more than employees who identified as "Other Race".

Regression Statistics Multiple R 0.186582386 R Square 0.034812987 Adjusted R Square 0.031952195 Standard Error 1.427514884 Observations 4400 10 ANOVA 11 MS F Significance F 12 Regression 13 322.373795 24.79798423 12.16900555 1.52551E-26 13 Residual 4386 8937.785296 2.037798745 14 Total 4399 9260.159091 15 16 Coefficients Standard Error t Stat P-value Lower 9596 Upper 95%% Lower 95.0% Upper 95.0% 17 Intercept 2.199582691 0.346491303 6.348161339 2.39987E-10 1.520284758 2.878880624 1.520284758 2.878880624 18 goal_clarity 0.153412887 0.019715766 7.781228765 8.89499E-15 0.114760029 0.192065745 0.114760029 0.192065745 19 black -0.15386469 0.109870502 -1.4004186 0.16145874 -0.36926636 0.061536974 -0.36926636 0.061536974 20 white -0.08992526 0.093419967 -0.96259145 0.335805654 -0.27307558 0.093225051 -0.27307558 0.093225051 21 asian 0.042307254 0.136316136 0.310361309 0.75630096 -0.22494121 0.30955572 -0.22494121 0.30955572 22 hispanic -0.26822024 0.083869119 -3.19808108 0.001393293 -0.43264607 -0.10379442 -0.43264607 -0.10379442 23 supervisor -0.41067632 0.04711906 -8.71571546 4.0338E-18 -0.50305347 -0.31829917 -0.50305347 -0.31829917 24 experience -0.01204165 0.031661166 -0.38032855 0.703719962 -0.07411352 0.050030229 -0.07411352 0.050030229 25 age40up+ 0.174164089 0.061475511 2.833064506 0.004631455 0.0536410420 0.294687137 0.053641042 0.294687137 26 female -0.0480151 0.047428482 -1.0123685 0.311417735 -0.14099888 0.044968675 -0.14099888 0.044968675 27 military 0.000995777 0.054684203 0.018209597 0.985472475 -0.10621288 0.108204431 -0.10621288 0.108204431 26 agency_defense -0.02579112 0.322114196 -0.08006826 0.936186618 -0.65729762 0.605715369 -0.65729762 0.605715369 23 agency_domestic 0.123621919 0.321759184 0.384206341 0.700844144 -0.50718857 0.754432408 -0.50718857 0.754432408 30 agency_humanserv 0.106358836 0.327472968 0.324786611 0.745358068 -0.53565356 0.748371228 -0.53565356 0.748371228