Relation Between Crime, Poverty and Education in Usa

- Statistical analysis of the relation amongst hatred Rate, genteelness and indigence regular army, 2009 Sonarika Mahajan 100076 Research Question In this inquiry paper, analysis is done to break up whether the take of developing and mendi tummycy influence the descend detestation pasture in the limit together States of the States. Using descriptive statistics such a slopped, model deviation, variance, histograms, scatter diagrams and simple unidimensional arrested development analysis performed upon both autonomous versatiles sepa footsteply, it can be analysed till what limit do these two supreme uncertains, i. . teaching and impoverishment cause fluctuations upon the certified inconsistent, in what proportion (direct or inverse) and of the two independent versatiles, which is a better forecaster for ascertain discourtesy charge per unit in USA. selective information description The grounds selected for this demand be highlighted with discolou r in the above map The Data that is use to define our dependent variable admit both, violent offence (murder and non- negligent manslaughter, flavorous rape, robbery, and aggravated assault) as well as property crime (burglary, larceny-theft, motor fomite theft, and arson). crime statistics used in this study ar published by FBI (Federal Bureau of Intelligence) fate as a governmental manner to the United States Department of Justice. The independent variable that comments upon the information take aims in the United States of America is carried out by analysing the total turn of common high school graduates per state. This info includes students of all the ethnicities for the school year 2008-2009. The statement universe in this study is uniform to the total population of the state.This selective information has been hive away by National Centre for development Statistics (NCES), which is the primary federal official entity that collects education relate entropy in the U. S. and opposite countries and analyses it. The poorness status for an individual is measured by comparing his/her income to a preset measuring rod of dollars known as the threshold apprise. The mendicancy universe excludes children on a put down floor the age of 15, mass living in military barracks, institutional group quarters and college dormitories. This entropy is calm by the U. S. Census Bureau, serving as the most reliable source active Americas pack and economy. wholly the data collected is cross-sectional, since it was taken during the same period period (year 2009) across different parameters. Also, the home base of measurement for these variables is the ratio scale, since the ratio between two values is meaningful and the observations are comparable to a zero value. compendium Mean It is the representative of a underlying value for a given data set, i. e. average. The mean value for crime variable suggests that in the year 2009, the parcel of crim es universe account in some(prenominal) state of USA was 3. 26%.The mean value for education variable suggests that the percentage of public high school graduates being report in any state of USA was 1% for the same time period. Similarly, the mean value for the scantiness variable suggests that the percentage of individuals living below the poverty television channel being reported in any state of USA was 13. 54%. streamer deviation & Variance The higher the value of the cadence deviation, greater is the spreading 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 take is more astray dispersed over the states as compared to the other two variables. Variance is the average of the come of squared deviation scores. It is used to cipher the standard diversity since its a better means for ascertain the dispersion of data. It is measured as the square of standard devia tion for any data set. skewness The symmetry of the variable distribution is measured by the help of this statistic. villainy rate has a skewness of 0. 083, making it a symmetrical distributed variable since the value is enveloping(prenominal) 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 by rights skewness of the histogram. Simple additive regress model a. Crime and Education Y = hooklike variable, Crime X = supreme variable, Education. The retroflexion model is the compare that describes how y is related to x. This regression equation is From fudge 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 stop and ? as the dip Here the sky ? 1 is negative. Interpreta tion of the slope For all 1% increase in the rate of students being graduated from high school, in that location is a decrease of 2. 9% in crime activities in the USA. Interpretation of the break charge if thither is no edition in the education take, the estimated crime rate would be 6. 17%. The coefficient of use or r2 It determines the proportion of variation in the dependent variable by the independent variable. From skirt 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 occlude to 1, it doesnt seem to be a appropriate forecaster to determine the crime rate in USA. dead reckoning scrutiny Ho ? 1 = 0 (education is not a recyclable predictor of crime) Ha ? 1 ? 0 (education is a useful predictor of crime) Significance level ? = 0. 05 tally to the standion rule, the fruitless hypothesis will be rejected if p-value ? ?. From table 2. 4, p-value = 0. 019 Since 0. 019 ? 0. 05, we reject the null hypothesis. At 95% federal agency level, there is enough evidence to conclude that education is a useful predictor for crime in USA since the slope of the regression depict is not zero. b. Crime and pauperismY = Dependent variable, Crime X = Independent variable, Poverty. The regression equation is as follows Plugging in the values to from display board 3. 4, get Crime = 1. 819 + 0. 107 (Poverty) This regression equation can be graphed as follows assuming ? 0 as the tapdance 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 finding or r2From give in 3. 2, r2 = . 191 This states that 19. 1% of the variation in crime rate is explained by regression of poverty on crime. Hypo thesis 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 in return 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 Amer ica. Poverty, with the logical implication level of 19. 1% is known to be a better predictor in this case as compared to the 18. % significance level shown by the independent variable, education. This fact was progress proved when the p-value for poverty stood at a lower amount as compared to its counterpart. Even though it can be conclude 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 employment rate could be a few options to get amongst. Appendix Table 1. 1 Statistics for crimes reported in 30 states of USA.State population unpeaceful Crime Property Crime core Crime luck of tally 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 atomic number 20 3,69,61,664 1,74,459 10,09,614 11,84,073 3. 20 atomic number 27 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 geographical mile 99,69,727 49,547 2,82,918 3,32,465 3. 33 atomic number 25 52,66,214 12,842 1,39,083 1,51,925 2. 88 disseminated multiple sclerosis 29,51,996 8,304 87,181 95,485 3. 23 atomic number 42 59,87,580 29,444 2,02,698 2,32,142 3. 88 tonne 9,74,989 2,473 24,024 26,497 2. 72 Nebraska 17,96,619 5,059 49,614 54,673 3. 04 Nevada 26,43,085 18,559 80,763 99,322 3. 76 raw(a) Jersey 87,07,739 27,121 1,81,097 2,08,218 2. 39 hot Mexico 20,09,671 12,440 75,078 87,518 4. 35 virgin York 1,95,41,453 75,176 3,78,315 4,53,491 2. 2 due north Carolina 93,80,884 37,929 3,44,098 3,82,027 4. 07 northwestern 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 pappa 1,26,04,767 47,965 2,77,512 3,25,477 2. 58 in the south 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 Virginia 78,82,590 17,879 1,91,453 2,09,332 2. 66 upper-case letter 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 offset 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 School 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 how-do-you-do 12,95,178 11,508 0. 89 Iowa 30,07,856 33,926 1. 13 Kansas 28,18,747 30,368 1. 8 kale 99,69,727 1,12, 742 1. 13 Minnesota 52,66,214 59,729 1. 13 manuscript 29,51,996 24,505 0. 83 Missouri 59,87,580 62,969 1. 05 t 9,74,989 10,077 1. 03 Nebraska 17,96,619 19,501 1. 09 Nevada 26,43,085 19,904 0. 75 peeled 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 northwestern 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 papa 1,26,04,767 1,30,658 1. 04 South 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/ca ts/income_expenditures_poverty_wealth/income_and_povertystate_and_local_data. html Regression (Independent variable Education)Table 2. 1 Variables Entered/ take awayb example Variables Entered Variables Removed Method 1 Educationa . Enter a. All requested variables entered. b. Dependent Variable Crime Table 2. 2 Model compendium Model R R form Adjusted R second power Std. Error of the aim 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 symmetricalness 12. 595 28 . 450 Total 15. 379 29 a. Predictors (Constant), Education . Dependent Variable Crime Table 2. 4 Coefficientsa Model Unstandardized Coefficients regulate Coefficients t Sig. B Std. Error important 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 V ariables 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 Adjusted 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