Q8.)

Below is a multiple regression model that explores the circumstances where a fatality is more likely to be a young person aged 17-25.

Dependent Variable: Y=1 if the fatality was a young person aged 17-25, = 0 otherwise.

Independent Variable: t is Years, t=0 in 1988

Male = 1 if the person who died is a male, = 0 otherwise

Night = 1 if the accident took place at night, = 0 otherwise

Weekend = 1 if the accident took place on the weekend, = 0 otherwise

Single Vehicle = 1 if the accident involved only one vehicle, = 0 otherwise

SUMMARY OUTPUT Regression Statistics Multiple R 0.22534 R Square 0.050778 Adjusted R 0.050685 Standard E 0.462363 Observatic 51202 ANOVA df SS MS F ignificance F Regression 5 585.4778 117.0956 547.7388 0 Residual 51196 10944.68 0.21378 Total 51201 11530.15 Coefficientsandard Em t Stat P-value Lower 95%Upper 95% Intercept 0.316466 0.005413 58.46549 0 0.305856 0.327075 t -0.00619 0.000229 -27.0458 5.7E-160 -0.00664 -0.00574 Male 0.002152 0.004582 0.469647 0.638609 -0.00683 0.011132 Night 0.11653 0.004338 26.86406 7.2E-158 0.108028 0.125032 Weekend 0.058326 0.004311 13.52821 1.26E-41 0.049876 0.066777 Single Vehi 0.091135 0.004244 21.47355 7.8E-102 0.082816 0.099453Below is a regression model that explores the trend in proportion of young person fatalities over time. Dependent Variable: Y=1 if the fatality was a young person Aged 17-25, = 0 otherwise. Independent Variable: t is Years, t=0 in 1988 SUMMARY OUTPUT Regression Statistics Multiple R 0.119079 R Square 0.01418 Adjusted R 0.014161 Standard E 0.471174 Observatic 51202 ANOVA df SS MS F ignificance F Regression 1 163.4963 163.4963 736.4532 4.9E-161 Residual 51200 11366.66 0.222005 Total 51201 11530.15 Coefficientsandard Em t Stat P-value Lower 95%Upper 95% Intercept 0.431778 0.003894 110.8873 0 0.424146 0.43941 -0.00631 0.000232 -27.1377 4.9E-161 -0.00676 -0.00585