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Writing Assignment Essay Gender Effect on Courts Dealing with Criminals and Factors that Affect Homicide Rate There is always some speculation that courts have soft corners in dealing with female crminals. In adddition, it is thought that the percentage of poverty, unemployment, and college attainment in any city have an effect on homicide rate. In this report, First I test whether courts dealing with criminals differs by gender. Than, I test whether there is relationship between homicide rate and poverty (%), unemployment (%), and college (%) using the city level data set. Finally, I perform an regression analysis test to model homicide rate per 100,000 in a city. The National Opinion on Crime and Justice study from survey 1995 gives information about citizens opinions and attitudes concerning crime and criminal justice related-topics. The city level data summarizes the different types of information about the American cities such as population, crime index, median age, poverty (%), balck (%), unemployment (%),college (%), homicide rate per 100,000 population, etc. The city level data reported here are from the 2002 survey. The respondents were asked ‘How do courts deal with crminals? ’ in the National Opinion on Crime and Justice study survey with possible answers from ‘Too harshly’, ‘Not harshly enough’, and ‘About right’. About 62% male as compared to 38% female replied Too harshly, about 54% male as compared to 46% female replied Not harshly enough, and about 53% male as compared to 47% female replied About right (Table 1). There were no significant differences between males and females response on â€Å"How do courts deal with crminals? †, ? 2(2) = 0. 99, p = . 61 (Table 2). The average homicide rate per 100,000 population of city was 10. 45 (SD = 9. 81) with minimum of 0. 00 and maximum of 57. 65. The average unemployment of city was 4. 56% (SD = 1. 41%) with minimum of 2. 2% and maximum of 9. 1%.. The average poverty of city was 12. 8% (SD = 5. 5%) with minimum of 3. 4% and maximum of 32. 4%. The average college attainment of city was 26. 89% (SD = 11. 14%) with minimum of 6% and maximum of 69%. (Table 3). There was significant strong positive correlation between homicide rate per 100,000 population and poverty (%), r(156) = . 62, p . 001 (Figure 1). There was significant strong positive correlation between homicide rate per 100,000 population and unemployment (%), r(156) = . 59, p . 001 (Figure 2). There was significant weak negative correlation between homicide rate per 100,000 population and college (%), r(156) = -. 26, p = . 001 (Table 4). Poverty (%) significantly predicted homicide rate per 100,000 population, ? = . 62, t(156) = 9. 92, p . 001. Poverty (%) also explained a significant proportion of variance in homicide rate per 100,000 population, R2 = . 39, F(1, 156) = 98. 43, p . 001 (Table 5, 6 and 7). However, about 61% of the variation in homicide rate per 100,000 population was not explained by poverty (%). Therefore, there was moderate strong effect of poverty (%) on homicide rate per 100,000 population. Each additional percentage of poverty increases the homicide rate per 100,000 population by 1. 1. The regression equation was given by ‘(Homicide rate) = -3. 625 + 1. 1(Poverty)’. For a poverty of 20%, the homicide rate per 100,000 population was about 18. 38. In conclusion, courts do not deal differently with criminals by gender. There is weak negative linear relationship between homicide rate per 100,000 population and colleget (%). However, there was strong positive linear relationship between homicide rate per 100,000 population and unemployment (%), and homicide rate per 100,000 population and poverty (%). Poverty (%) was a useful predictor of homicide rate per 100,000 population in a city.