St. Louis Homicide Analysis Jacob Treat, Thuy xa, Charles Gendron

Objectives Use statistical analysis to predict the total number of homicides in St. Louis Locate areas with high homicide trends Determine factors that may disrupt data analysis

Data Overview Collected 7 years of data from SLMPD website City divided into 79 neighborhoods Organized data into Excel to run statistical analyses

2008 – 2013 Model Analysis Downward trend in total homicides Model: Y=-9.057x+18340.886 R² = .761 F-Test = .023 P-Value Intercept = .023 P-Value X Variable = .023 Predictions: 100 Murders in 2014 91 Murders in 2015

Anomalies 2014 actual homicide number 156 Bloody Spring Wells Goodfellow St. Louis Place West End Kingsway East Accounted for +18 change in homicides from 2013 to 2014 Ferguson Effect Extra patrols called to Ferguson leaving other neighborhoods with less officers August - December

2008-2014 Model Analysis Linear Regression Model does not fit when data for 2014 is added Model: Y = -3.036x + 6239.821 R² = .107 F-Test = .473 P-Value Intercept = .464 P-Value X Variable = .473 56 more than the model predicted for 2014 2015 YTD (March) change is 0 Prediction: 2015 Prediction of 123 murders

Top 10 Most Dangerous (Statistical Average of 7 Years) Wells Goodfellow = 8 Jeff Vanderlou = 7.86 Baden = 7.14 Dutchtown = 5.43 Mark Twain = 5.43 O’Fallon = 5.14 Greater Ville = 4.86 College Hill = 4.29 Penrose = 4.14 West End = 3.86