City of saint louis 2015 Homicides

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Presentation transcript:

City of saint louis 2015 Homicides Stephanie Lenhart Anna O’Shea – Newton Sarah Rosen Emily McCarthy Lijing Yang

101 Total Homicides 2015 HOMICIDE Prediction District 1: 16 Note district numbers do not sum to 101 due to individual rounding

TIME SERIES ANALYSIS Time series method of forecasting measures historical data points. The goal of the time series method is to identify meaningful characteristics in the data that can be used in making statements about future outcomes. Variances in data points measured and compared from year to year can reveal seasonal fluctuation patterns that can serve as the basis for future forecasts. Analysis included homicide data that occurred between 2008-2014 Challenge with Time Series Due to the autocorrelation nature of time series, time series violate the assumption of independence of errors. Type I error rates will increase substantially when autocorrelation is present. http://userwww.sfsu.edu/efc/classes/biol710/timeseries/timeseries1.htm http://www.ehow.com/info_8610535_advantages-time-series-method-forecasting.html

R Squared R-squared is a statistical measure of how close the data are to the fitted regression line. R-squared does not indicate whether a regression model is adequate. Low R-Squared values are not always bad and high R-squared values are not always good. In Some fields, such as social fields, it is expected that your R-squared values will be low. In fields that attempt to predict human behavior, typically R-squared values are lower than 50% because Humans are harder to predict. http://blog.minitab.com/blog/adventures-in-statistics/regression-analysis-how-do-i-interpret-r-squared-and-assess-the- goodness-of-fit

District 1

District 2

District 3

District 4

District 5

District 6

Conclusion Crime rates between districts is highly variable. Analysis conducted by district so police resources can be applied as needed.