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4. As a class, combine your samples to fill in the observed table. Then, calculate the expected counts. Note: all force levels other than "hands" (including push to wall/ground, handcuffs, draw/point weapon, pepper spray, baton) are all considered "higher level" force, The NYPD did not include police shootings in this dataset. YOWtatal XColUmn tota table total Expected Observed Force Black Hispanic White Total Force Black Hispanic White Total None 300 (86 58 None Hands |57 39 Hands Only Only Higher 28 Level 20 Higher Level Total Total 5. Do the data provide convincing evidence of an association between race of suspects and the levels of force used by police officers? Use a = 0.05. STATE: Hypotheses: Significance level: PLAN: Name of procedure: chi-square test for independence Check conditions: DO: Specific Formula: Work: Picture: Test statistic: P-value: CONCLUDE:

4. As a class, combine your samples to fill in the observed table. Then, calculate the expected counts. Note: all force levels other than "hands" (including push to wall/ground, handcuffs, draw/point weapon, pepper spray, baton) are all considered "higher level" force, The NYPD did not include police shootings in this dataset. YOWtatal XColUmn tota table total Expected Observed Force Black Hispanic White Total Force Black Hispanic White Total None 300 (86 58 None Hands |57 39 Hands Only Only Higher 28 Level 20 Higher Level Total Total 5. Do the data provide convincing evidence of an association between race of suspects and the levels of force used by police officers? Use a = 0.05. STATE: Hypotheses: Significance level: PLAN: Name of procedure: chi-square test for independence Check conditions: DO: Specific Formula: Work: Picture: Test statistic: P-value: CONCLUDE:

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
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4. As a class, combine your samples to fill in the observed table. Then, calculate the expected counts. Note: all force levels other than "hands" (including push to wall/ground, handcuffs, draw/point weapon, pepper spray, baton) are all considered "higher level" force, The NYPD dịd not include police shootings in this dataset. YOWtutal XCölumn tota l table total Expected Observed Force Black Hispanic White Total Force Black Hispanic White Total None 300 |78615P None 57 39 Higher 2Y 20 Hands Hands Only Only Higher Level Level Total Total 5. Do the data provide convincing evidence of an association between race of suspects and the levels of force used by police officers? Use a = 0.05. STATE: Hypotheses: Significance level: PLAN: Name of procedure: chi-square test for independence Check conditions: DO: Specific Formula: Work: Picture: Test statistic: P-value: CONCLUDE: Lesson provided by Stats Medic (statsmedic.com) & Skew The Script (skewthescript.org) Made available under a Creative Commons Atribution-NonCommercial-ShareAlike 4.0 License (https://creativecommons.org/licenses/by-no-sal4.0)
Transcribed Image Text:4. As a class, combine your samples to fill in the observed table. Then, calculate the expected counts. Note: all force levels other than "hands" (including push to wall/ground, handcuffs, draw/point weapon, pepper spray, baton) are all considered "higher level" force, The NYPD dịd not include police shootings in this dataset. YOWtutal XCölumn tota l table total Expected Observed Force Black Hispanic White Total Force Black Hispanic White Total None 300 |78615P None 57 39 Higher 2Y 20 Hands Hands Only Only Higher Level Level Total Total 5. Do the data provide convincing evidence of an association between race of suspects and the levels of force used by police officers? Use a = 0.05. STATE: Hypotheses: Significance level: PLAN: Name of procedure: chi-square test for independence Check conditions: DO: Specific Formula: Work: Picture: Test statistic: P-value: CONCLUDE: Lesson provided by Stats Medic (statsmedic.com) & Skew The Script (skewthescript.org) Made available under a Creative Commons Atribution-NonCommercial-ShareAlike 4.0 License (https://creativecommons.org/licenses/by-no-sal4.0)
Was "Stop and Frisk" Biased? Today, we'll analyze 2011 data from New York's "Stop and Frisk" program. The program allowed police officers to stop people on the street and search them for weapons or contraband. The program was controversial. Critics alleged that it led to heightened police discrimination of people of color. We will explore that claim using a chi-square test for independence. Open the following links: The program data (may take some time to load): tinyurl.com/stop-and-frisk-data Random number generator: random.org/integers NYPD precinct map: tinyurl.com/nypd-precincts The data we're using contains every stop made by NYPD police officers in 2011 (more than half a million stops in total). Each row represents a single stop. The second tab in the spreadsheet contains a data key and further information. Note: These data were reported by the police officers who made the stops. 1. Use the random number generator to obtain 10 random integers between the values 2 and 632722 (the number of dataset rows). Find the dataset row numbers that correspond with your 10 random integers and record the following information for each selected stop: Race Force Level 2. For one of your selected stops, look at all the variables listed: race, gender, and age of suspect along with the suspected crime and whether an arrest was actually made. If you'd like, use the precinct number and precinct map (linked above) to see the general area in which this stop took place. Write a two-sentence description of this stop, as if you're writing a news brief for an article: 3. Often, police interactions are portrayed at an individual level in the news media, with vivid details given about the people involved and the interactions themselves. We are about to conduct a statistical analysis of many interactions, focusing solely on the relationship between race and force level used. What are the strengths and weaknesses of each type of analysis (individual & statistical)? Lesson provided by Stats Medic (statsmedic.com) & Skew The Script (skewthescript.org) Made available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License (https://creativecommons.org/licenses/by-nc-sa/4.0)
Transcribed Image Text:Was "Stop and Frisk" Biased? Today, we'll analyze 2011 data from New York's "Stop and Frisk" program. The program allowed police officers to stop people on the street and search them for weapons or contraband. The program was controversial. Critics alleged that it led to heightened police discrimination of people of color. We will explore that claim using a chi-square test for independence. Open the following links: The program data (may take some time to load): tinyurl.com/stop-and-frisk-data Random number generator: random.org/integers NYPD precinct map: tinyurl.com/nypd-precincts The data we're using contains every stop made by NYPD police officers in 2011 (more than half a million stops in total). Each row represents a single stop. The second tab in the spreadsheet contains a data key and further information. Note: These data were reported by the police officers who made the stops. 1. Use the random number generator to obtain 10 random integers between the values 2 and 632722 (the number of dataset rows). Find the dataset row numbers that correspond with your 10 random integers and record the following information for each selected stop: Race Force Level 2. For one of your selected stops, look at all the variables listed: race, gender, and age of suspect along with the suspected crime and whether an arrest was actually made. If you'd like, use the precinct number and precinct map (linked above) to see the general area in which this stop took place. Write a two-sentence description of this stop, as if you're writing a news brief for an article: 3. Often, police interactions are portrayed at an individual level in the news media, with vivid details given about the people involved and the interactions themselves. We are about to conduct a statistical analysis of many interactions, focusing solely on the relationship between race and force level used. What are the strengths and weaknesses of each type of analysis (individual & statistical)? Lesson provided by Stats Medic (statsmedic.com) & Skew The Script (skewthescript.org) Made available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License (https://creativecommons.org/licenses/by-nc-sa/4.0)
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