Answer the following questions with the data provided

1-Find how the percentage of young people in the population (pop.18.24) in 2004 effects the abortion rate of a state (abort.rate). Evaluate this relationship using the methods taught to you in class (2 points).

Rcode: mod.1 <- lm((pop.18.24) ~(abort.rate), data = states)

Data:

Call:

lm(formula = (pop.18.24) ~ (abort.rate), data = states)

Residuals:

Min 1Q Median 3Q Max

-1.10512 -0.46025 -0.07498 0.383352.02802

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) 11.02241 0.2003255.023 < 2e-16 ***

abort.rate -0.05912 0.01160 -5.096 5.81e-06 ***

---

Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.6643 on 48 degrees of freedom

Multiple R-squared: 0.3511, Adjusted R-squared: 0.3375

F-statistic: 25.97 on 1 and 48 DF, p-value: 5.814e-06

2-What is the marginal effect of the percentage of young people in the state on the abortion rate (1 point).

Answer:

3-Using the regression from above, control for the percentage of women in the state legislature (womleg.2007) (2 points). (a) Evaluate this control variable using the methods you learned in class and lab. (b) Evaluate the model as a whole. Did this control variable add any improvement to it (did it help us understand the relationship in a better way)

Rcode: mod.1 <- lm((pop.18.24) ~(abort.rate)+(womleg.2007), data = states)

summary(mod.1)

Data:

all:

lm(formula = (pop.18.24) ~ (abort.rate) + (womleg.2007), data = states)

Residuals:

Min 1Q Median 3Q Max

-1.15799 -0.46566 -0.09334 0.346942.00301

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) 11.178533 0.32435334.464 < 2e-16 ***

abort.rate -0.056100 0.012669 -4.428 5.64e-05 ***

womleg.2007 -0.008696 0.014151-0.615 0.542

---

Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.6687 on 47 degrees of freedom

Multiple R-squared: 0.3562, Adjusted R-squared: 0.3288

F-statistic: 13 on 2 and 47 DF, p-value: 3.2e-05