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Motor Vehicle Accidents

Motor Vehicle Accidents. Hunjung Kim Melissa Manfredonia Heidi Braunger Yaming Liu Jo-Yu Mao Grace Lee December 1, 2005. Econ 240A Project. I. Rollover crashes Actual data vs. Condensed ANOVA OLS Regression Results II. Alcohol-related crashes

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Motor Vehicle Accidents

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  1. Motor Vehicle Accidents Hunjung Kim Melissa Manfredonia Heidi Braunger Yaming Liu Jo-Yu Mao Grace Lee December 1, 2005 Econ 240A Project

  2. I. Rollover crashes • Actual data vs. Condensed • ANOVA • OLS Regression • Results II. Alcohol-related crashes • Actual vs. Condensed • Contingency Table • ANOVA • Results III. Conclusion

  3. I. Rollover Crashes

  4. Survival Rate in Rollover Crashes Depends on…

  5. Number of Quarter Turns

  6. Vehicle Types Van SUV Passenger car Pick-Up Truck

  7. Complete Rollover Data

  8. Survivors vs. # of Rollovers & Vehicle Type

  9. ANOVA: two-factor w/o replication

  10. ANOVA: cont…

  11. Condensed Rollover Data

  12. Survivors vs.# of Rollovers &Vehicle Type(condensed data)

  13. ANOVA:two-factor w/o replication

  14. ANOVA:cont…

  15. ANOVA Analysis • Ho: Two variables independent (ie: µp = µs = µv = µt) • Ha: Two variables dependent (ie: at least two means differ) α = 0.05 • Differences between the number of quarter turns taken (ROW) F-statistic = 5.785 > F-critical = 1.859 P-value of 1.041e-6  Therefore, Ho is rejected and we conclude that the number of survivors is dependent on the number of quarter turns. • Differences between the vehicle types (Columns) F-statistic = 3.660 > F-critical = 2.798 P-value = 0.0187 Therefore, Ho is rejected and we conclude that the number of survivors is dependent on the type of vehicle.

  16. OLS Regression

  17. Survivors vs. # of Turns & Vehicle Type

  18. Cont…

  19. Cont…

  20. OLS with Dummy variable

  21. OLS with Dummy variable (cont.)

  22. Summary Output : OLS with Dummy Variables

  23. Results of Wald Coefficient Test Estimation Equation: SURVIVOR = C(1)*DUMMY1_PASSGER CAR + C(2)*DUMMY2_SUV + C(3)*DUMMY3_VAN + C(4)*DUMMY4_TRUCK + C(5)*CON_QUART_TURN Wald Coefficient Test : C(1)=C(2), C(1)=C(3), C(1)=c(4), C(2)=C(3), C(2)=c(4), C(3)=c(4), On the base of outcome from the EView, Only C(1) is different from c(3). Thus, Passenger car is safer than Van. In the other cases, we didn’t have enough evidence that which vehicle is safer than others

  24. Results • Number of survivors in rollover crashes has statistically significant dependence on • Number of quarter turns • Type of vehicle • Passenger Car has the higher survival rate than VAN • Other cases we didn’t have enough evidence which type of vehicle is safer • More variables need to be considered

  25. II. Alcohol-related Crashes

  26. Connection BetweenAlcohol-Related FatalitiesandTime of the DayandDay of the Week Statistical Techniques: Contingency Table ANOVA

  27. Adjusted Data( Source: Minnesota, 2003 )- Divide 4 classes by the time period of crashes (Remember Rule of Five) - Delete unknown data of the raw data for the convenience of analysis

  28. Histogram: Alcohol-Related Fatal Crashes by Day of Week

  29. Pie chart : Alcohol-Related Fatal Crashes by Day of Week

  30. Contingency Table: we are testing the independence between the time of day and the day of week against the alternative hypothesis that these variables are dependent.

  31. 1. Hypotheses Ho: Two variables (time of the day and day of week) are independent Ha: not Ho 2. Test stat: χ2 statistic : 33.0897 3. Critical χ2 statistic : 28.8693 (α = 0.05, df = 3*6 = 18) 4. Computed χ2 statistic > Critical χ2 statistic 5. We can reject Ho, therefore two variables are dependent CONCLUSION: Two variables are dependent.  The observed number of crashes are different from the expected number of crashes. Null Hypothesis Test: for the Contingency Table

  32. ANOVA:Two-Factor without Replication

  33. ANOVA: Two-Factor without Replication

  34. ANOVA Analysis The alcohol-related crashes may be affected by two factors: Factor 1: the time of day Factor 2: the day of week

  35. Factor 1 1. Hypotheses Ho: No difference from time period of day Ha: not Ho 2. Test stat: F-stat = 7.50 3. Critical F-stat: F=3.16 (α = 0.05, df = 3, 18 ) 4. Computed F-stat > Critical F-stat 5. We can reject Ho, therefore there is a difference in the time of day.

  36. Factor 2 1. Hypotheses Ho: No difference from day of week Ha: not Ho 2. Test stat: F-stat=1.94 3. Critical F-stat: F=2.66(α = 0.05, df = 6, 18) 4. Computed F-stat< Critical F-stat 5. We can’t reject Ho, therefore there is no statistical difference among the days of the week.

  37. Results The contingency table only suggested two variables are not independent. The ANOVA table illustrated a statistically significant difference between time of day and fatal alcohol-related crashes, however, there’s no difference between the days of the week and fatal alcohol-related crashes.

  38. III. Conclusion

  39. Rollover & Alcohol-related crashes No significant conclusion can be drawn between the two data sets

  40. Future Application • Rollover crashes • Survival rate on each type of vehicle • Alcohol-related crashes • Survival rate on day of the week

  41. Moral of the Story… • Vehicles are not 100% “DEATH PROOF” • DON’T drink and drive!

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