Statistical Analysis

SOC 302: Statistical Analysis Project Directions: Please type your project; you are completing this as if you were a real-world researcher submitting an official research report. Please paste your jamovi output at the end of your project and identify the problem numbers on your output. For each problem, include the following in sentence form: the null and alternative hypotheses the sample means and standard deviations for all groups the identification of the appropriate statistical test that was used and an explanation for why that test was selected your final conclusion with appropriate statistical notation Dataset info: All datasets are available. The dataset titled Philadelphia Police Stops includes data on all pedestrian and vehicular stops by the Philadelphia Police from January 2014 to March 2016. The data come from Stanfords Open Police Data website. The dataset titled Future Years of Incarceration Imposed 2020-2024 includes data on the daily sum number of years of incarceration sentenced for each conviction by the Philadelphia District Attorneys Office between 2020 and 2024. The data are provided by the DATA LAB. The dataset titled Neighborhood Food Retail 2019 includes data on food access for 1336 block groups in Philadelphia in 2019. The data include the number of high- produce supply stores (e.g., supermarkets, produce stores, farmers markets) in relation to low-produce supply stores (like dollar stores, pharmacies, and convenience stores). The data are provided by Open Data Philly. The dataset titled Bias Training is totally fake. 2 Problems: Questions #1 through #6 are each worth 15 points. Question #7 is worth 10 points. 1. Using the dataset titled Police Stops, is the average age for pedestrian stops significantly younger than the average age for vehicular stops? 2. Using the dataset titled Police Stops, is there a significant difference in the average age for people who got arrested and those who didnt get arrested? 3. Using the dataset titled Future Years of Incarceration, is there a significant difference in the mean number of years of incarceration imposed for violent, property, weapons, drug, and other crimes? (Be sure to use the Offense Category variable, not the Offense Type variable) 4. Using the dataset titled Future Years of Incarceration, is there a significant difference in the mean number of years of incarceration imposed for defendants from different racial/ethnic backgrounds? 5. Using the dataset titled Neighborhood Food Retail, is there a significant difference in the mean number of high-produce supply stores for high poverty and non-high poverty block groups? 6. Using the dataset titled Bias Training, are implicit racial bias scores significantly lower after completing a bias training program? (Higher scores = more bias) 7. Using any dataset you would like (from the ones posted on Canvas or one you find yourself online), find something interesting! This can be one of the tests weve learned or even just a descriptive statistic. Tell me what you found and why its interesting.