> summary(lm(bmi~sex))

Call:

lm(formula = bmi ~ sex)

Residuals:

Min 1Q Median 3Q Max

-11.4563 -2.9997 -0.5004 2.1966 17.8870

Coefficients:

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

(Intercept) 28.7663 0.2219 129.637 < 2e-16 ***

sex -1.2278 0.4085 -3.005 0.00276 **

---

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

Residual standard error: 4.564 on 598 degrees of freedom

Multiple R-squared: 0.01488, Adjusted R-squared: 0.01323

F-statistic: 9.032 on 1 and 598 DF, p-value: 0.002764

Is there a statistically significant relationship between sex and BMI based on the above analysis? State your conclusion, and briefly justify your response using the R output.