# Labor Market Hypothesis Regression > lm_labor_interaction summary(lm_labor_interaction) Call: lm(formula = h1bvis.supp ~ group + age + female * impl.prejud, data = immig) Residuals: Min

# Labor Market Hypothesis Regression > lm_labor_interaction <- lm(h1bvis.supp ~ group + age + female * impl.prejud, data = immig) > summary(lm_labor_interaction) Call: lm(formula = h1bvis.supp ~ group + age + female * impl.prejud, data = immig) Residuals: Min 1Q Median 3Q Max -0.51587 -0.28921 -0.03937 0.18575 0.74520 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.6718755 0.0602513 11.151 < 2e-16 *** groupTech -0.0894341 0.0437475 -2.044 0.041183 * groupUnemployed 0.0137135 0.0198390 0.691 0.489576 groupWhitecollar 0.0720444 0.0436103 1.652 0.098847 . age -0.0018014 0.0006311 -2.855 0.004398 ** female -0.2170039 0.0695961 -3.118 0.001873 ** impl.prejud -0.3393396 0.0932492 -3.639 0.000288 *** female:impl.prejud 0.2366881 0.1173852 2.016 0.044032 * --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.2937 on 1001 degrees of freedom (124 observations deleted due to missingness) Multiple R-squared: 0.04552, Adjusted R-squared: 0.03884 F-statistic: 6.819 on 7 and 1001 DF, p-value: 6.251e-08