order 390, econometrics.docx - Defined Variables Table 1 Variables Label Crime Gini Variable Description Crime Rate Gini coefficient Death Death due to

order 390, econometrics.docx - Defined Variables Table 1...

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Defined Variables Table 1: Variables Label Variable Description Type Type Crime Crime Rate The number of total robberies and property crimes per hundred thousand people in economies in the United States in 2014 dependent Gini Gini coefficient Inequality in Income across the counties of United States in 2014 independent Death Death due to Drugs The number of death due to drug overdose per county in 2014 independent Unem Rate of Unemployment Percentage of labor force who are unemployed and are seeking for a paid job in 2014 independent Police Employed Police Officers Total number of police officers employed per county in 2014 independent Pov Level of Poverty Percentage of person in the county whose annual income between 2013 and 2014 was under the poverty line independent Table 2: Descriptive Statistics Variable Observations Mean Standard Minimum Maximum Deviation crime 214 789.2 698.14 68.38 3778.15 gini 214 0.5562 0.0653 0.3291 0.5262 drug 196 12.99 4.4 5.15 19.15
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Unemp rate 211 6.78 2.56 2.18 23.9 Police Officer 180 215.97 334.31 12.0 2667.0 IV. Initial Model Description: Our first model estimates the relationship between the Gini coefficient for 212 economies in the United States vs. rate of crime per 100,000 people. Our primary hypothesis was that boost in the rate of crime would match up with an increase in the Gini coefficient. Primary Model Assumptions of Gauss Markov: 1. Primary model can be written as Crime = β0 +β1 ( Gini ) + u , It is supposed that model should be linear in terms of parameters and co efficient 2. Each county in the United States that had accurate and absolute data for crime rates, population and Gini coefficients may include in the data set. 3. The estimates of Gini co efficient are unequal. 4. To verify either the error term has an expected zero value and constant variance for independent variable, Errors were plotted against the parallel Gini values. Based on the plot (see appendix), although the mean value revolve around zero means that the variance is not constant.
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