Relation Between Crime, Poverty and Education in Usa

- Statistical perfumemary of the relation between hatred Rate, rearing and meagreness regular army, 2009 Sonarika Mahajan 100076 Research brain In this research paper, analysis is done to conclude whether the take of facts of life and mendicancy influence the total abomination vagabond in the unify conveys of America. Using descriptive statistics such a mean, standard deviation, variance, histograms, scatter diagrams and simpleton linear throwback analysis performed upon both self-employed person variable quantitys separately, it can be analysed till what extent do these two independent variables, i. . fosterage and meagerness cause fluctuations upon the dependent variable, in what proportion (direct or inverse) and of the two independent variables, which is a better soothsayer for determining curse rate in USA. Data description The realms selected for this poll are highlighted with yellow in the above map The Data that is used to define our dependent variab le include both, cutthroat criminal offense (murder and non- negligent manslaughter, forcible rape, robbery, and aggravated assault) as well as property discourtesy (burglary, larceny-theft, motor vehicle theft, and arson). offence statistics used in this study are published by FBI (Federal Bureau of Intelligence) serving as a governmental agency to the United States Department of Justice. The independent variable that comments upon the education levels in the United States of America is carried out by analysing the total look of domain high school graduates per state. This data includes students of all the ethnicities for the school social class 2008-2009. The education universe in this study is equivalent to the total population of the state.This data has been collected by National Centre for facts of life Statistics (NCES), which is the primary coil federal entity that collects education related data in the U. S. and other countries and analyses it. The pauperisation sta tus for an individual is measured by comparing his/her income to a planned amount of dollars known as the threshold lever. The beggary universe excludes children at a lower place the age of 15, hoi polloi living in military barracks, institutional multitude quarters and college dormitories. This data is collected by the U. S. Census Bureau, serving as the most reliable source about Americas people and economy.All the data collected is cross-sectional, since it was taken during the same time period ( course of instruction 2009) crossways different parameters. Also, the scale of measurement for these variables is the ratio scale, since the ratio between two abide bys is meaningful and the observations are comparable to a zero value. compendium Mean It is the representative of a central value for a habituated data set, i. e. average. The mean value for crime variable suggests that in the year 2009, the percentage of crimes existence reported in any state of USA was 3. 26%.Th e mean value for education variable suggests that the percentage of public high school graduates beingness reported in any state of USA was 1% for the same time period. Similarly, the mean value for the pauperization variable suggests that the percentage of individuals living below the want line being reported in any state of USA was 13. 54%. prototype deviation & Variance The higher the value of the standard deviation, greater is the distribution of the data set. Out of the three variables, poverty has the highest standard deviation value of 2. 98.Therefore, the percentage of individuals below poverty level is more widely spread over the states as compared to the other two variables. Variance is the average of the sum of squared deviation scores. It is used to compute the standard variation since its a better means for determining the dispersion of data. It is measured as the square of standard deviation for any data set. Skewness The parallelism of the variable distribution is measured by the help of this statistic. shame rate has a skewness of 0. 083, making it a symmetrical distributed variable since the value is closer to zero. The education variable is skewed negatively at -. 67 since the variable has lower values, indicating a left skewed histogram. Whereas, poverty shows a positive skewness value of . 670 since its variables have numerous high values, which justifies the advanced skewness of the histogram. Simple linear degeneration pretense a. curse and teaching Y = Dependent variable, horror X = Independent variable, Education. The regression model is the equation that describes how y is related to x. This regression equation is From tabular array 2. 4 in appendix, the regression equation is, Crime = 6. 17 2. 9 (Education) This regression equation can be graphed as follows assuming ? 0 as the intercept and ? as the cant Here the sky ? 1 is negative. reading of the slope For every 1% increase in the number of students being graduat ed from high school, there is a decrease of 2. 9% in crime activities in the USA. Interpretation of the intercept Even if there is no variation in the education level, the estimated crime rate would be 6. 17%. The coefficient of finis or r2 It determines the proportion of variation in the dependent variable by the independent variable. From tabularize 2. 2, r2 = . 181 This states that 18. 1% of the variation in crime rate is explained by regression of education on crime.Since this value is not close to 1, it doesnt seem to be a appropriate soothsayer to determine the crime rate in USA. Hypothesis testing Ho ? 1 = 0 (education is not a useful predictor of crime) Ha ? 1 ? 0 (education is a useful predictor of crime) Significance level ? = 0. 05 According to the rejection rule, the null supposal will be rejected if p-value ? ?. From circumvent 2. 4, p-value = 0. 019 Since 0. 019 ? 0. 05, we reject the null hypothesis. At 95% confidence level, there is generous evidence to conclud e that education is a useful predictor for crime in USA since the slope of the regression line is not zero. b. Crime and needY = Dependent variable, Crime X = Independent variable, Poverty. The regression equation is as follows Plugging in the values to from mesa 3. 4, get Crime = 1. 819 + 0. 107 (Poverty) This regression equation can be graphed as follows assuming ? 0 as the intercept and ? 1 as the slope Here the slope ? 1 is positive. Interpretation of the slope For every 1% increase in the individuals below poverty line, there is an increase of . 11% in crime activities in the USA. Interpretation of the intercept With the poverty level remaining constant, the estimated crime rate would be 1. 82%. The coefficient of determination or r2From Table 3. 2, r2 = . 191 This states that 19. 1% of the variation in crime rate is explained by regression of poverty on crime. Hypothesis testing Ho ? 1 = 0 (poverty is not a useful predictor of crime) Ha ? 1 ? 0 (poverty is a useful predictor of crime) Significance level ? = 0. 05 According to the rejection rule, the null hypothesis will be rejected if p-value ? ?. From table 3. 4, p-value = 0. 016 Since 0. 016 ? 0. 05, we reject the null hypothesis. At 95% confidence level, there is enough evidence to conclude that poverty is a useful predictor for crime in USA since the slope of the regression line is not zero.Conclusion and recommendations From this study conducted, it is assured that the crime rate in USA is directly proportionate to the people below the poverty line and inversely proportionate to the number of high school students graduating in the year 2009. When simple linear regression was performed to both the independent variables separately, the coefficient of determination (r2) and the p-value aided our study to select the variable that was a better predictor for determining the crime rate in America. Poverty, with the meaning level of 19. 1% is known to be a better predictor in this object lesson as compare d to the 18. % significance level shown by the independent variable, education. This fact was shape up proved when the p-value for poverty stood at a lower amount as compared to its counterpart. Even though it can be concluded that poverty is a better predictor for crime rate in USA, the level of significance still stands at a diminutive 19. 1%. Much stronger predictors could be used for the above study. GDP, income level, provision of federal aid or business rate could be a few options to choose amongst. Appendix Table 1. 1 Statistics for crimes reported in 30 states of USA.State Population Violent Crime Property Crime Total Crime shareage of Total Crime Alabama 47,08,708 21,179 1,77,629 1,98,808 4. 22 Alaska 6,98,473 4,421 20,577 24,998 3. 58 genus Arizona 65,95,778 26,929 2,34,582 2,61,511 3. 96 calcium 3,69,61,664 1,74,459 10,09,614 11,84,073 3. 20 Colorado 50,24,748 16,976 1,33,968 1,50,944 3. 00 Connecticut 35,18,288 10,508 82,181 92,689 2. 63 Florida 1,85,37,969 1,13,541 7,12,010 8,25,551 4. 45 how-do-you-do 12,95,178 3,559 47,419 50,978 3. 94 Iowa 30,07,856 8,397 69,441 77,838 2. 59Kansas 28,18,747 11,278 90,420 1,01,698 3. 61 Michigan 99,69,727 49,547 2,82,918 3,32,465 3. 33 manganese 52,66,214 12,842 1,39,083 1,51,925 2. 88 Mississippi 29,51,996 8,304 87,181 95,485 3. 23 Missouri 59,87,580 29,444 2,02,698 2,32,142 3. 88 Montana 9,74,989 2,473 24,024 26,497 2. 72 northeast 17,96,619 5,059 49,614 54,673 3. 04 Nevada 26,43,085 18,559 80,763 99,322 3. 76 cutting Jersey 87,07,739 27,121 1,81,097 2,08,218 2. 39 New Mexico 20,09,671 12,440 75,078 87,518 4. 35 New York 1,95,41,453 75,176 3,78,315 4,53,491 2. 2 labor union Carolina 93,80,884 37,929 3,44,098 3,82,027 4. 07 northwards Dakota 6,46,844 1,298 12,502 13,800 2. 13 operating theater 38,25,657 9,744 1,13,511 1,23,255 3. 22 Pennsylvania 1,26,04,767 47,965 2,77,512 3,25,477 2. 58 reciprocal ohm Dakota 8,12,383 1,508 13,968 15,476 1. 91 Texas 2,47,82,302 1,21,668 9,95,145 11,16,813 4. 51 Vir ginia 78,82,590 17,879 1,91,453 2,09,332 2. 66 Washington 66,64,195 22,056 2,44,368 2,66,424 4. 00 Wisconsin 56,54,774 14,533 1,47,486 1,62,019 2. 87 Wyoming 5,44,270 1,242 14,354 15,596 2. 87 author http//www. fbi. ov/about-us/cjis/ucr/crime-in-the-u. s/2011/crime-in-the-u. s. -2011/tables/table-5 Table 1. 2 Statistics for public high school graduates in 30 states of USA. State Population Total Public High aim Graduates Percentage of High School Graduates Alabama 47,08,708 42,082 0. 89 Alaska 6,98,473 8,008 1. 15 Arizona 65,95,778 62,374 0. 95 California 3,69,61,664 3,72,310 1. 01 Colorado 50,24,748 47,459 0. 94 Connecticut 35,18,288 34,968 0. 99 Florida 1,85,37,969 1,53,461 0. 83 Hawaii 12,95,178 11,508 0. 89 Iowa 30,07,856 33,926 1. 13 Kansas 28,18,747 30,368 1. 8 Michigan 99,69,727 1,12,742 1. 13 Minnesota 52,66,214 59,729 1. 13 Mississippi 29,51,996 24,505 0. 83 Missouri 59,87,580 62,969 1. 05 Montana 9,74,989 10,077 1. 03 Nebraska 17,96,619 19,501 1. 09 Nevada 26,43,085 19,9 04 0. 75 New Jersey 87,07,739 95,085 1. 09 New Mexico 20,09,671 17,931 0. 89 New York 1,95,41,453 1,80,917 0. 93 North Carolina 93,80,884 86,712 0. 92 North Dakota 6,46,844 7,232 1. 12 Oregon 38,25,657 35,138 0. 92 Pennsylvania 1,26,04,767 1,30,658 1. 04 southern Dakota 8,12,383 8,123 1. 00 Texas 2,47,82,302 2,64,275 1. 7 Virginia 78,82,590 79,651 1. 01 Washington 66,64,195 62,764 0. 94 Wisconsin 56,54,774 65,410 1. 16 Wyoming 5,44,270 5,493 1. 01 Source http//nces. ed. gov/CCD/tables/ESSIN_Task5_f2. asp Table 1. 3 Statistics for individuals below Poverty line in 30 states of USA. State Population for whom poverty status is determined Individuals in poverty Percent below poverty Alabama 45,88,899 8,04,683 17. 54 Alaska 6,82,412 61,653 9. 03 Arizona 64,75,485 10,69,897 16. 52 California 3,62,02,780 51,28,708 14. 17 Colorado 49,17,061 6,34,387 12. 90Connecticut 34,09,901 3,20,554 9. 40 Florida 1,81,24,789 27,07,925 14. 94 Hawaii 12,64,202 1,31,007 10. 36 Iowa 29,05,436 3,42,934 11. 80 Kansas 27,32,685 3,65,033 13. 36 Michigan 97,35,741 15,76,704 16. 20 Minnesota 51,33,038 5,63,006 10. 97 Mississippi 28,48,335 6,24,360 21. 92 Missouri 58,18,541 8,49,009 14. 59 Montana 9,46,333 1,43,028 15. 11 Nebraska 17,39,311 2,14,765 12. 35 Nevada 26,06,479 3,21,940 12. 35 New Jersey 85,31,160 7,99,099 9. 37 New Mexico 19,68,078 3,53,594 17. 97 New York 1,90,14,215 26,91,757 14. 16North Carolina 90,95,948 14,78,214 16. 25 North Dakota 6,20,821 72,342 11. 65 Oregon 37,48,545 5,34,594 14. 26 Pennsylvania 1,21,65,877 15,16,705 12. 47 South Dakota 7,82,725 1,11,305 14. 22 Texas 2,41,76,222 41,50,242 17. 17 Virginia 76,23,736 8,02,578 10. 53 Washington 65,30,664 8,04,237 12. 31 Wisconsin 54,95,845 6,83,408 12. 43 Wyoming 5,29,982 52,144 9. 84 Source http//www. census. gov/compendia/statab/cats/income_expenditures_poverty_wealth/income_and_povertystate_and_local_data. html regression toward the mean (Independent variable Education)Table 2. 1 Variables get intoed/Removedb det errent example Variables Entered Variables Removed regularity 1 Educationa . Enter a. All requested variables entered. b. Dependent Variable Crime Table 2. 2 Model Summary Model R R Square modify R Square Std. Error of the Estimate 1 . 425a . 181 . 152 . 67068 a. Predictors (Constant), Education Table 2. 3 ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 2. 784 1 2. 784 6. 189 . 019a Residual 12. 595 28 . 450 Total 15. 379 29 a. Predictors (Constant), Education . Dependent Variable Crime Table 2. 4 Coefficientsa Model Unstandardized Coefficients alike(p) Coefficients t Sig. B Std. Error Beta 1 (Constant) 6. 165 1. 173 5. 257 . 000 Education -2. 904 1. 167 -. 425 -2. 488 . 019 Regression (Independent variable Poverty) Table 3. 1 Variables Entered/Removedb Model Variables Entered Variables Removed Method 1 Povertya . Enter a. All requested variables entered. b. Dependent Variable Crime Table 3. 2 Model Summary Model R R Square Adjust ed R Square Std.Error of the Estimate 1 . 437a . 191 . 162 . 66665 a. Predictors (Constant), Poverty Table 3. 3 ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 2. 935 1 2. 935 6. 604 . 016a Residual 12. 444 28 . 444 Total 15. 379 29 a. Predictors (Constant), Poverty b. Dependent Variable Crime Table 3. 4 Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 1. 819 . 575 3. 162 . 004 Poverty . 107 . 042 . 437 2. 570 . 016