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Probability presentation
 

Probability presentation

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    Probability presentation Probability presentation Presentation Transcript

    • MBA 512 – Business Research & Design Jason Giomboni and John Mullisky
    • Introduction
      • Scenario
        • A body was found in a bag.
        • The detectives on scene were not able to immediately determine the gender, race and age of the victim.
      • Our Goal
        • To determine the victim’s probable gender and race.
        • To determine the probable gender and race of the offender.
      • Hypothesis Statement
        • We are going to analyze available data and use probability to determine a preliminary gender and race profile of the parties involved.
    • Source Data
      • Available from FBI crime statistics for the year 2003
      • Race
      • Gender
    • Victim Profile
      • Based on the table of data, we have used probability to determine the most likely profile for our victim.
      • Classical probability = # of ways to get outcome
      • total # of outcomes
      • Our classical probability calculations suggest:
        • White – probability of 52% (3,562/6,911)
        • Black - probability of 45% (3,098/6,911)
        • Other/Unknown – probability of 3% (251/6,911)
      • Our classical probability calculations suggest:
        • Male – probability of 71 % (4,987/7,024)
        • Female – probability of 28% (1,962/7,024)
        • Unknown – probability of 1% (75/7,024)
      • Our hypothesis is that the victim is white and male.
    • Offender Profile
      • Based on the table of data, we have used probability to determine the most likely profile for our offender.
      • Our classical probability calculations suggest:
        • White – probability of 48% (3,323/6,911)
        • Black - probability of 49% (3,412/6,911)
        • Other/Unknown – probability of 3% (176/6,911)
      • Our classical probability calculations suggest:
        • Male – probability of 89 % (6,220/7,024)
        • Female – probability of 10% (691/7,024)
        • Unknown – probability of 1% (113/7,024)
      • Our hypothesis is that the offender is male.
        • Our data +/- 1% suggests the offender could be white or black.
    • Joint Profile
      • Based on the table of data, we have used probability to determine that our victim and offender of the same race and gender.
      • P(A&B) = P(A) * P(B)
      • White on White crime:
        • P(A) – Victim -Probability of 85% (3,017/3,562)
        • P(B) – Offender – Probability of 91% (3,017/3,323)
        • P(A&B) = 77% (.85 x .91)
      • Black on Black crime:
        • P(A) – Victim -Probability of 92% (2,864/3,098)
        • P(B) – Offender – Probability of 84% (2,864/3,412)
        • P(A&B) = 77% (.84 x .92)
      • Unknown/Other on Unknown/Other crime:
        • P(A) – Victim -Probability of 49% (124/251)
        • P(B) – Offender – Probability of 70% (124/176)
        • P(A&B) = 34% (.49 x .70)
    • Joint Profile - Continued
      • Based on the table of data, we have used probability to determine that our victim and offender of the same race and gender.
      • P(A&B) = P(A) * P(B)
      • Male on Male crime:
        • P(A) – Victim -Probability of 89% (4,417/4,987)
        • P(B) – Offender – Probability of 71% (4,417/6,220)
        • P(A&B) = 63% (.89 x .71)
      • Female on Female crime:
        • P(A) – Victim -Probability of 4% (185/4,987)
        • P(B) – Offender – Probability of 27% (185/691)
        • P(A&B) = 1% (.04 x .27)
      • Unknown/Other on Unknown/Other crime:
        • P(A) – Victim -Probability of 25% (19/75)
        • P(B) – Offender – Probability of 17% (19/113)
        • P(A&B) = 4% (.25 x .17)
    • Profile Comparison
      • Our initial prediction is that the victim is a white (52%) and male (72%).
      • Our initial prediction is that the offender is white (48%) or black (49%) and male (89%).
      • Our joint profile is that the victim and offender’s race is equally likely that it w/w or b/b (77%) and the gender is male (63%).
      • The data suggests that our gender analysis is correct and the race profile is equally likely to be black or white.
    • Forensic Results
      • The forensic results determined that the victim is a white female with blonde hair in mid thirties.
      • Our preliminary hypothesis was incorrect.
      • Classical Probability
      • The offender gender is:
        • Male -Probability of 89% (1,754/1,962)
        • Female – Probability of 10% (185/1,962)
        • Unknown – Probability of 1% (23/1,962)
      • The offender race is:
        • White -Probability of 85% (3,017/3,562)
        • Black – Probability of 14% (501/3,562)
        • Other/Unknown – Probability of 1% (44/3,562)
    • Conclusion
      • In addition to the victim information, our alert on this case will include a preliminary profile of the offender as a white male
      • Decisions using analysis results was difficult to predict based on very close probability results
      • Having data other profile categories to analyze would help to narrow the scope for identifying profile of the victim and offender. Ex. Age or relationships
      • THE END