Slide 1

ECON 240 A GROUP 5 Yao Wang Brooks Allen Morgan Hansen Yuli Yan Ting Zheng AUTO FATALITY FACTS 2007

Slide 2

Overview More men than women die each year in motor vehicle crashes. Men typically drive more miles than women and more often engage in risky driving practices including not using seat belts, driving while impaired by alcohol, and speeding. Crashes involving male drivers often are more severe than those involving female drivers. More men than women die each year in motor vehicle crashes. Men typically drive more miles than women and more often engage in risky driving practices including not using seat belts, driving while impaired by alcohol, and speeding. Crashes involving male drivers often are more severe than those involving female drivers. We analyze car crash fatality data for 2007, and run several regressions to try to determine the likely causes of fatality. We analyze car crash fatality data for 2007, and run several regressions to try to determine the likely causes of fatality. We find that being male, being young, and alcohol all significantly contribute to the probability of dying during a car crash. We find that being male, being young, and alcohol all significantly contribute to the probability of dying during a car crash.

Slide 3

Descriptive Statistics Percentage of vehicle fatalities by gender, 1975-2007, taken from Fatality Facts 2007

Slide 4

The age distribution in car accident Table One: Histogram of age distribution in car accident

Slide 5

Analysis Description We gathered our data from the U.S. Department of Transportations Fatality Analysis Reporting System, FARS. We gathered our data from the U.S. Department of Transportations Fatality Analysis Reporting System, FARS. We classify drivers by gender, age group, and alcohol consumption for our independent variables, then run linear probability regressions to try to find a relationship with our dependent variable, fatality. We classify drivers by gender, age group, and alcohol consumption for our independent variables, then run linear probability regressions to try to find a relationship with our dependent variable, fatality.

Slide 6

Expectations Based on historical data, we assume that males drive more dangerously In addition, alcohol should play a very significant role in vehicle fatalities We also expect that the very young and very old age groups will have higher fatality rates, due to less experience and poor coordination, respectively

Slide 7

STATISTCAL ANALYSIS Fatal vs. Male Dependent Variable: FATAL Method: Least Squares Date: 12/03/08 Time: 15:17 Sample(adjusted): 1 65535 Included observations: 65535 after adjusting endpoints VariableCoefficientStd. Errort-StatisticProb. MALE0.0960060.00405223.692460.0000 C0.3693740.003278112.67260.0000 R-squared0.008493 Mean dependent var0.432212 Adjusted R-squared0.008478 S.D. dependent var0.495387 S.E. of regression0.493283 Akaike info criterion1.424563 Sum squared resid15946.01 Schwarz criterion1.424840 Log likelihood-46677.35 F-statistic561.3325 Durbin-Watson stat2.170404 Prob(F-statistic)0.000000 *Male in a car accident is a bernoulli variable with 0 (female) and 1(male). *As shown in the table, the t-stat and F-test are both highly significant. * The coefficient shows a 9% increase in the probability of death given that you are male.

Slide 8

Fatal vs. Age Dependent Variable: FATAL Method: Least Squares Sample(adjusted): 1 65535 Included observations: 65535 after adjusting endpoints VariableCoefficientStd. Errort-StatisticProb. AGE0.0023458.82E-0526.579160.0000 C0.3428010.00387688.449250.0000 R-squared0.010665 Mean dependent var0.432212 Adjusted R-squared0.010650 S.D. dependent var0.495387 S.E. of regression0.492742 Akaike info criterion1.422369 Sum squared resid15911.08 Schwarz criterion1.422647 Log likelihood-46605.49 F-statistic706.4517 Durbin-Watson stat2.161306 Prob(F-statistic)0.000000 *Fatal is a Bernoulli variable set up as: 0 (alive) and 1(death). A motorist either lives or was fatally wounded. The t-stat and F-test are both highly significant, with very low probabilities. *Durbin-Watson stat is close to 2, which indicates there is not enough evidence of autocorrelation. *Coefficient of age means the probability of death will increase 0.2345% per age.

Slide 9

Fatal vs. Alcohol Dependent Variable: FATAL Method: Least Squares Sample(adjusted): 1 65535 Included observations: 65535 after adjusting endpoints VariableCoefficientStd. Errort-StatisticProb. ALCOHOL0.0102601850.00048121.2889303.1471935e-100 C0.391172130.002726143.456590 R-squared0.00686833Mean dependent var0.432211724 Adjusted R-squared0.00685322 S.D. dependent var0.495387226 S.E. of regression0.4936868 Akaike info criterion1.42619961 Sum squared resid15972.139452 Schwarz criterion1.42647709 Log likelihood-46730.9955 F-statistic453.218549 As expected, alcohol plays a part in motor vehicle fatalities. Although the coefficient is small, it is still a positive factor in fatalities.

Slide 10

Total Regression Dependent Variable: FATAL Method: Least Squares Sample(adjusted): 1 65535 Included observations: 65535 after adjusting endpoints VariableCoefficientStd. Errort-StatisticProb. MALE0.1106330.00402827.468020.0000 AGE0.0026148.77E-0529.811260.0000 ALCOHOL0.0119450.00047824.970060.0000 C0.2123370.00530140.057120.0000 R-squared0.029674 Mean dependent var0.432212 Adjusted R-squared0.029629 S.D. dependent var0.495387 S.E. of regression0.487993 Akaike info criterion1.403030 Sum squared resid15605.37 Schwarz criterion1.403585 Log likelihood-45969.78 F-statistic668.0061 Durbin-Watson stat2.153951 Prob(F-statistic)0.000000 It is apparent that all 3 factors (male, age and alcohol) all have a positive effect in automobile fatalities

Slide 11

Age*Alcohol vs. Fatal This graph shows that old drinkers are more dangerous drivers than young drinkers (higher probability of a fatal crash)

Slide 12

Dummy variable regression of age Dependent Variable: FATAL Method: Least Squares Date: 12/03/08 Time: 20:33 Sample(adjusted): 1 65535 Included observations: 65535 after adjusting endpoints VariableCoefficientStd. Errort-StatisticProb. YOUNG_PEOPLE0.4539530.002388190.08750.0000 RETIRED_PEOPLE0.3927290.00724054.243920.0000 MIDAGE0.3284250.00396882.762460.0000 R-squared0.015119 Mean dependent var0.432212 Adjusted R-squared0.015089 S.D. dependent var0.495387 S.E. of regression0.491636 Akaike info criterion1.417888 Sum squared resid15839.45 Schwarz criterion1.418304 Log likelihood-46457.63 F-statistic502.9973 Durbin-Watson stat2.171381 Prob(F-statistic)0.000000 We generated 3 dummy variables for young, middle aged and retired people The results indicate that young people are the most dangerous, then retired, and lastly middle aged drivers. Young_people=1*(age 20) Retired_people=1*(age>65)+0*(age