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
wisdom-11 question Traffic stops often reveal important patterns about interactions between law enforcement and the public, especially regarding characteristics like race and arrest outcomes. This analysis uses a dataset of nearly 400,000 traffic stops, which includes detailed information on age, race, sex, and arrest status, to explore potential disparities in arrest rates across different groups.
justice-1 question creating the population table and verifying assumptions
justice-2 question correspondence between columns of data and preceptor and that they mean the same thing
justice-3 question One reason the assumption of validity might not hold is if the arrested column contains errors or inconsistencies, such as misreported arrests or missing data, which could bias the analysis. Similarly, if columns like race or age are inaccurately recorded or incomplete, the results may not reliably reflect true patterns.
justice-4 question rows and columns of variables across time. data and preceptor are drawn from it.
justice-5 question stops at all intersections across time and other covariates
justice-6 question relationship between columns of data and population and preceptor are the same
justice-7 question One reason the assumption of stability might not hold is that the patterns in the arrested column or other covariate columns, like race or age, could change over time due to shifts in policing policies or social behavior during the data collection period. This means the relationships observed in the dataset might not be consistent across different time frames.
justice-8 question rows in data and preceptor are randomly drawn.
justice-9 question One reason the assumption of representativeness might not hold is if the stops recorded in the data disproportionately reflect certain zones, times, or demographic groups, meaning the sample columns do not accurately capture the diversity of the overall population of all traffic stops. This could lead to biased conclusions that don’t generalize well beyond the observed data.
justice-10 question One reason the assumption of representativeness might not hold is if the Preceptor Table does not accurately reflect the population because it includes data from a limited time period, specific locations, or particular groups, causing its columns to capture only a subset of the broader population’s characteristics. This mismatch can lead to biased or incomplete inferences when generalizing from the population to the Preceptor Table.
justice-11 question assignment to treatment is independent of outcome and other covarites
justice-12 question done
justice-13 question done
justice-14 question x
justice-15 question x
courage-1 question creating the data generating mech
courage-2 exercise linear_reg(engine = "lm")
courage-3 exercise linear_reg(engine = "lm") |> fit(arrested ~ sex, data = x) x
courage-4 exercise linear_reg(engine = "lm") |> fit(arrested ~ sex, data = x)|>tidy(conf.int = TRUE)
courage-5 exercise linear_reg(engine = "lm") |> fit(arrested ~ race, data = x)|>tidy(conf.int = TRUE)
courage-6 exercise linear_reg(engine = "lm") |> fit(arrested ~ race, data = x)|>tidy(conf.int = TRUE)
courage-7 exercise linear_reg(engine = "lm") |> fit(arrested ~ sex + race, data = x)|>tidy(conf.int = TRUE)
courage-8 exercise linear_reg(engine = "lm") |> fit(arrested ~ sex + race*zone, data = x)|>tidy(conf.int = TRUE) table(x$race)
courage-9 exercise fit_stops
courage-10 question x
courage-11 question x
courage-12 question x
courage-13 question x
courage-14 question x
courage-15 question x
courage-16 exercise tidy(fit_stops, conf.int = TRUE)
courage-17 question x
courage-18 question x
minutes question 35