In the regression output below, we are looking at the relationship between abortlaw10 (number of restrictions on abortion in a state), cigarettes (number of packs finished by adults on a bimonthly basis in a state), and womleg_2015 (percentage of female legislators in a state). The intercept in this table represents: (Picture G) Call : lm(formula = womleg_2015 ~ abortlaw10 + cigarettes, data = states) Residuals: Min 10 Median 3Q Max -12.4097 -4. 0188 0.2193 3.6585 13.4114 Coefficients: Estimate Std. Error t value Pr(>It!) (Intercept) 38.2184 2.5034 15.266 < 2e-16 *** abortlaw10 -1.4548 0.2969 -4.899 1. 18e-05 *** cigarettes -0. 6503 0.2450 -2.654 0.0108 * Signif. codes: 0 '* * * ' 0.001 '* * ' 0.01 '*' 0.05 ' . ' 0.1 ' ' 1 Residual standard error: 5.357 on 47 degrees of freedom Multiple R-squared: 0.4393, Adjusted R-squared: 0.4155 F-statistic: 18.41 on 2 and 47 DF, p-value: 1.243e-06 The value of womleg_2015 when cigarettes is 0 and abortlaw is 1 The value of womleg_2015 when cigarettes and abortlaw are 0 The value of cigarettes when abortlaw and womleg_2015 are 0 The value of abortlaw when womleg_2015 is 1 and cigarettes is 0