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