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# Probability presentation

## on Oct 09, 2010

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## Probability presentationPresentation 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