1. Nycpd Stop and Frisk Program
Prepared for James P. O'Neill, NYCPD Commissioner
Prepared by Nick Vail, WenWen Sheng, Amanda Spacaj-Gorham, and Jarrett Sullivan

3. Hypothesis
Ho : There is no association between race and further action.
H1 : There is an association between race and further action.

4. Chi-squared race and frisked

5. Chi-squared race and SEARCHED

6. Chi-squared race and Arrested

7. From 2009-2016 the lowest was .117the highest was .331%
*For guns found we added together: pistol, rifle/shotgun and machinegun categories from the raw data.
From the lowest % of SQF’s which recovered a gun was .13%. The greatest was .238%
From the lowest was .117the highest was .331%
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8. The percentage of SGF which resulted in an arrest was
low 4.18% high was 7.91%
low 5.9% high was 817.6%
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9. 03-08 low 41.9% high 54.3% 09-15 low 55.6% high 67.4%
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10. 03-08 low 6.82% high 9.3% 09-15 low 8.32% high 18.7%
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11. 03-08 low 5.3% high 7.3% 09-15 low 2.6% high 7.1% Sample Footer Text
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12. t-Test: Two-Sample Assuming Equal Variances Variable 1 Variable 2
Mean
Variance
Observations 3 11
Pooled Variance
Hypothesized Mean Difference 0
df 12
t Stat
P(T<=t) one-tail
t Critical one-tail
P(T<=t) two-tail
t Critical two-tail
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13. T-test for Murders Committed

14. t-Test: Two-Sample Assuming Equal Variances Variable 1 Variable 2
Mean
Variance
Observations 3 11
Pooled Variance
Hypothesized Mean Difference 0
df 12
t Stat
P(T<=t) one-tail
t Critical one-tail
P(T<=t) two-tail
t Critical two-tail
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15. t-test For rapes committed

16. t-Test: Two-Sample Assuming Equal Variances Variable 1 Variable 2
Mean
Variance
Observations 3 11
Pooled Variance
Hypothesized Mean Difference 0
df 12
t Stat
P(T<=t) one-tail
t Critical one-tail
P(T<=t) two-tail
t Critical two-tail
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17. T-test for Robberies Committed

18. t-Test: Two-Sample Assuming Equal Variances Variable 1 Variable 2
Mean
Variance
Observations 3 11
Pooled Variance
Hypothesized Mean Difference 0
df 12
t Stat
P(T<=t) one-tail E-05
t Critical one-tail
P(T<=t) two-tail
t Critical two-tail
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19. T-test felony assaults committed

20. t-Test: Two-Sample Assuming Equal Variances Variable 1 Variable 2
Mean
Variance
Observations 3 11
Pooled Variance
Hypothesized Mean Difference 0
df 12
t Stat
P(T<=t) one-tail
t Critical one-tail
P(T<=t) two-tail
t Critical two-tail
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21. T-test: Burglaries committed
t-Test: Two-Sample Assuming Equal Variances
Variable 1 Variable 2
Mean
Variance
Observations 3 11
Pooled Variance
Hypothesized Mean Difference 0
df 12
t Stat
P(T<=t) one-tail
t Critical one-tail
P(T<=t) two-tail
t Critical two-tail

22. t-Test: Two-Sample Assuming Equal Variances Variable 1 Variable 2
Mean
Variance
Observations 3 11
Pooled Variance
Hypothesized Mean Difference 0
df 12
t Stat
P(T<=t) one-tail
t Critical one-tail
P(T<=t) two-tail
t Critical two-tail
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23. T-test Grand Larceny t-Test: Two-Sample Assuming Equal Variances
Variable 1 Variable 2
Mean
Variance
Observations 3 11
Pooled Variance
Hypothesized Mean Difference 0
df 12
t Stat
P(T<=t) one-tail
t Critical one-tail
P(T<=t) two-tail
t Critical two-tail

24. t-Test: Two-Sample Assuming Equal Variances Variable 1 Variable 2
Mean
Variance
Observations 3 11
Pooled Variance
Hypothesized Mean Difference 0
df 12
t Stat
P(T<=t) one-tail
t Critical one-tail
P(T<=t) two-tail
t Critical two-tail
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25. T-test Grand Larceny Motor Vehicle
t-Test: Two-Sample Assuming Equal Variances
Variable 1 Variable 2
Mean
Variance
Observations 3 11
Pooled Variance
Hypothesized Mean Difference 0
df 12
t Stat
P(T<=t) one-tail
t Critical one-tail
P(T<=t) two-tail
t Critical two-tail