Work through this example on R and then do the following.

(a)	Using your personalized crash project that you created previously, fit a regression equation relating the natural log of the head injury criterion (lnhic) to the weight of the vehicle (wt). Then enter the values of the least squares estimates of 0 and 1 into the answer box, separated by commas.

(b)	Find the p-value for testing whether there is a significant correlation between the head injury criterion and the weight of the vehicle.

(c)	Based on the results of the regression, do head injuries tend to be more or less severe for heavier vehicles (compared with lighter vehicles) in the sampled population?

(A) More severe for heavier vehicles because ₀ is negative. (B) More severe for lighter vehicles because ₁ is positive. (C) More severe for lighter vehicles because ₀ is positive. (D) More severe for lighter vehicles because ₁ is negative. (E) More severe for heavier vehicles because ₁ is negative. (F) More severe for lighter vehicles because ₀ is negative. (G) More severe for heavier vehicles because ₀ is positive. (H) More severe for heavier vehicles because ₁ is positive.

Residuals: Min 10 -1.64627 -0.34446 Median 0.00597 30 0.34009 Max 1.35732 Coefficients: Estimate Std. Error t value Pr>tl) (Intercept) 6.319e+00 1.378e-01 45.838 anova (reg) Analysis of Variance Table Response: inhic DE Sum Sq Mean Sq F value Pr(>F) wt 1 1.728 1.72759 7.3625 0.007063 ** Residuals 287 67.344 0.23465 Signif. codes: 0 0.001 "**' 0.01 1*' 0.05.' 0.1'' 1 R Graphics: Device 2 (ACTIVE) X Normal Q-Q 3 19 2 1 T Standardized residuals 0 -1 7- 1 -3 3 266 -3 -2 -1 0 1 2 3 Theoretical Quantiles Im(Inhic -- wt) R Graphics: Device 2 (ACTIVE) x Residuals vs Fitted 1.5 19 1.0 . 0.5 0.0 Residuals -0.5 -1.0 -1.5 3 266 -2.0 6.5 6.6 6.7 6.8 6.9 7.0 Fitted values Im(Inhic - wt)