id submission_type answer
tutorial-id none 131-stops
name question Uma Ravat
email question uma.ravat.ucsb@gmail.com
introduction-1 question wisdom justice courage temperace
introduction-2 question done
introduction-3 question done
introduction-4 question done
introduction-5 question This data is from the Stanford Open Policing Project, which aims to improve police accountability and transparency by providing data on traffic stops across the United States. The New Orleans dataset includes detailed information about traffic stops conducted by the New Orleans Police Department.
introduction-6 question difference in two potential outcomes
introduction-7 question you can only observe one pot outcome
introduction-8 question arrested
introduction-9 question body_camera_on = 0 if police doesn't have body camera on during the traffic stop and 1 if yes
introduction-10 question two - yes or no 1 or 0.
introduction-11 question some one who is masked gets arrested (1) and the same person when unmasked doesn't get arrested(0) so the causal effect when wearing a mask as compared to not wearing is 1-0 = 1
introduction-12 question sex
introduction-13 question black and white may have different number of arrests after stops.
introduction-14 question Is there any difference in number/proportion of arrests amongst different races in the stops dataset?
wisdom-1 question creating the data and preceptor table and verifying assumptions of validity.
wisdom-2 question is the minimal table such that if we had no missing information we could calcualte our quantity of interest.
wisdom-3 question units are people stopped. outcome is whether they were arrested and covariates are their race, age, gender, reason and location or time of day for the stop
wisdom-4 question people stopped
wisdom-5 question arrested
wisdom-6 question race
wisdom-7 question none
wisdom-8 question now
wisdom-9 question above
wisdom-10 question Is there a difference in rate of arrests amongst differnet races
minutes question 35