AUTO FATALITY FACTS 2007 ECON 240 A GROUP 5 Yao Wang Brooks Allen Morgan Hansen Yuli Yan Ting Zheng 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 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 Descriptive Statistics Percentage of vehicle fatalities by gender 19752007 taken from Fatality Facts 2007 The age distribution in car accident Table One Histogram of age distribution in car accident 6000 Series AGE Sample 1 65535 Observations 65535 5000 4000 3000 2000 1000 0 0 0 12 5 25 0 37 5 50 0 62 5 75 0 87 5 100 0 Mean Median Maximum Minimum Std Dev Skewness Kurtosis 38 13513 34 00000 99 00000 0 000000 21 82044 0 789594 3 170004 Jarque Bera Probability 6888 654 0 000000 Analysis Description We gathered our data from the U S Department of Transportation s 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 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 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 Variable Coefficient Std Error t Statistic Prob MALE 0 096006 0 004052 23 69246 0 0000 C 0 369374 0 003278 112 6726 0 0000 R squared 0 008493 Mean dependent var 0 432212 Adjusted R squared 0 008478 S D dependent var 0 495387 S E of regression 0 493283 Akaike info criterion 1 424563 Sum squared resid 15946 01 Schwarz criterion 1 424840 F statistic 561 3325 Prob F statistic 0 000000 Log likelihood Durbin Watson stat 46677 35 2 170404 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 Fatal vs Age Dependent Variable FATAL Method Least Squares Sample adjusted 1 65535 Included observations 65535 after adjusting endpoints Variable Coefficient Std Error t Statistic Prob AGE 0 002345 8 82E 05 26 57916 0 0000 C 0 342801 0 003876 88 44925 0 0000 R squared 0 010665 Mean dependent var 0 432212 Adjusted R squared 0 010650 S D dependent var 0 495387 S E of regression 0 492742 Akaike info criterion 1 422369 Sum squared resid 15911 08 Schwarz criterion 1 422647 F statistic 706 4517 Prob F statistic 0 000000 Log likelihood Durbin Watson stat 46605 49 2 161306 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 Fatal vs Alcohol Dependent Variable FATAL As expected alcohol plays a part in motor vehicle fatalities Method Least Squares Sample adjusted 1 65535 Included observations 65535 after adjusting endpoints Variable Coefficient Std Error t Statistic Prob ALCOHOL 0 010260185 0 000481 21 288930 3 1471935e 100 C 0 39117213 0 002726 143 45659 0 R squared 0 00686833 Mean dependent var 0 432211724 Adjusted R squared 0 00685322 S D dependent var 0 495387226 0 4936868 Akaike info criterion 1 42619961 15972 139452 Schwarz criterion 1 42647709 F statistic 453 218549 S E of regression Sum squared resid Log likelihood 46730 9955 Although the coefficient is small it is still a positive factor in fatalities Total Regression Dependent Variable FATAL Method Least Squares Sample adjusted 1 65535 Included observations 65535 after adjusting endpoints Variable Coefficient Std Error t Statistic Prob MALE 0 110633 0 004028 27 46802 0 0000 AGE 0 002614 8 77E 05 29 81126 0 0000 ALCOHOL 0 011945 0 000478 24 97006 0 0000 C 0 212337 0 005301 40 05712 0 0000 R squared 0 029674 Mean dependent var 0 432212 Adjusted R squared 0 029629 S D dependent var 0 495387 S E of regression 0 487993 Akaike info criterion 1 403030 Sum squared resid 15605 37 Schwarz criterion 1 403585 F statistic 668 0061 Prob F statistic 0 000000 Log likelihood Durbin Watson stat 45969 78 2 153951 It is apparent that all 3 factors male age and alcohol all have a positive effect in automobile fatalities Age Alcohol vs Fatal 1 0 This graph shows that old drinkers are more dangerous drivers than young drinkers higher probability of a fatal crash FATAL 0 8 0 6 0 4 0 2 0 0 0 200 400 600 AGE ALCOHOL 800 1000 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 Variable Coefficient Std Error t Statistic Prob YOUNG PEOPLE 0 453953 0 002388 190 0875 0 0000 RETIRED PEOPLE 0 392729 0 007240 54 24392 0 0000 MIDAGE 0 328425 0 003968 82 76246 0 0000 R squared 0 015119 Mean dependent var 0 432212 Adjusted R squared 0 015089 S D dependent var 0 495387 S E of regression 0 491636 Akaike info criterion 1 417888 Sum squared resid 15839 45 Schwarz criterion 1 418304 F statistic 502 9973 Prob F statistic 0 000000 Log likelihood Durbin Watson stat 46457 63 2 171381 Young people 1 age 20 0 age 20 Retired people 1 age 65 0 age 6 5 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 Fatal vs Drunk men Dependent Variable FATAL Method Least Squares Date 12 04 08 Time 03 43 Sample adjusted 1 65535 Included observations 65535 after adjusting endpoints Variable Coefficient Std Error t Statistic Prob MALEALCOHOL 0 016395 0 000520 31 51293 0 0000 C 0 391478 0 002315 169 0980 0 0000 R squared 0 014927 Mean dependent var 0 432212 Adjusted R
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