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 GoalTo 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