Signature Assignment Report

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Need help creating report for my signature Assignment.

Criminals and arrested citizens By: Erica Murillo Mr. Oliver Math 215  The chosen topic entails criminals that got arrested for the criminal acts such as property crime, violence and drug crimes.  The analysis went to the depth of determining the portion that got arrested once more in possession of drugs in 2015  The topic got chosen out of curiosity of the outcome, crime being a field of concern Arrest of Peddlers 315,000 310,000 305,000 300,000 295,000 290,000 285,000 280,000 275,000 2009 2010 2011 2012 2013 2014 2015 2016 Chart Title 1600000 1400000 1200000 1000000 800000 600000 400000 200000 0 1 2 Year 3 Arrest of Users 4  From the two graphs it is noticeable that more arrests of peddlers resulted in the reduction in the number of drug users  More arrests indicated a reduction in the number of users in the localities  The arrested people got rehabilitated and peddlers charged  Bergman, P., & Berman-Barrett, S. J. (2015). The criminal law handbook: Know your rights, survive the system.  Singer, R. G., & La, F. J. Q. (2012). Criminal law: Examples and explanations. Gaithersburg: Aspen Law & Business.  Train, A. (1926). Courts and criminals. New York: Scribner.
Quantitative Reasoning I Security and Criminal Justice Scenario Topic 2 Predicting the Number of People Arrested for Drug Possession Scenario Review the data involving the number of people arrested for drug possession from 2 2 Predict the number of people who will be arrested for drug possession in 2018. Arrest in the United States, by sex, age group, and race, 2010 Age Group Junvenile Adult Sex Male Arrested Americans Violent 2010 Property Crime Murder and Non Negligent manslaugh ter Forcible Rape Robbery Aggravated Assault Simple Assault Burglary LarcenyTheft Motor vehicle Theft Arson Forgery and Counterfeit ing Fraud 9,792,190 Female Under age 18 18+ 3,329,920 1,642,650 11,479,470 9,980 1,230 1,010 10,190 19,860 98,600 230 13,700 2,870 27,190 17,220 85,110 316,460 92,030 44,820 363,670 944,970 245,770 347,480 44,000 210,240 65,200 1,082,200 224,570 717,770 553,640 281,060 990,350 58,980 9,350 12,500 1,950 15,760 4,560 55,730 6,740 48,780 109,740 29,320 78,150 1,690 5,770 76,410 182,120 Drug Sale/Manu facturing 249,050 53,260 23,800 278,510 Posession/ 1,075,810 Use 260,720 146,770 1,189,760 Arrested Americans Violent 2011 Property Drug Crime Murder and Non Negligent manslaugh ter Forcible Rape Robbery Aggravated Assault Simple Assault Burglary LarcenyTheft Motor vehicle Theft Arson Forgery and Counterfeit ing Fraud 9,890,112 3,363,219 1,659,077 11,594,265 10,180 1,218 1,040 10,292 20,257 100,572 228 13,563 2,956 28,006 17,392 85,961 322,789 91,110 46,165 367,307 963,869 250,685 344,005 43,560 216,547 67,156 1,093,022 226,816 732,125 548,104 289,492 1,000,254 60,160 9,537 12,375 1,931 16,233 4,697 56,287 6,807 49,756 111,935 29,027 77,369 1,741 5,943 77,174 183,941 254,031 Posession/ 1,097,326 Use 52,727 24,514 281,295 258,113 151,173 1,201,658 Sale/Manu facturing Arrested Americans Crime 9,939,563 3,380,035 1,667,372 11,652,236 Violent 2012 Property Drug Murder and Non Negligent manslaugh ter Forcible Rape Robbery Aggravated Assault Simple Assault Burglary LarcenyTheft Motor vehicle Theft Arson Forgery and Counterfeit ing Fraud 10,689 1,279 1,092 10,807 20,460 101,075 230 13,631 2,986 28,146 17,566 86,391 316,333 89,288 45,242 359,961 983,146 258,206 350,885 44,867 220,878 69,171 1,114,882 233,620 644,270 482,332 254,753 880,224 63,168 9,060 12,994 1,834 17,045 4,462 59,101 6,467 52,741 110,816 30,769 76,595 1,845 5,884 81,804 182,102 274,353 56,945 26,475 303,799 Posession/ 1,207,059 Use 283,924 166,290 1,321,824 Sale/Manu facturing Year People Arrested for Drug Possession 2006 2007 1,519,760 1,361,658 2008 2009 2010 2011 2012 2013 1,321,824 1,387,915 1,179,728 1,143,931 1,237,708 1,203,323 2014 982,169 2015 801,560 ce Scenario on r drug possession from 2006-2015. possession in 2018. Race White American Indian or Alaska Native Black Asian, Native Hawaiian or Other Pacific Islander 9,122,010 3,655,620 186,120 158,370 5,540 5,430 120 110 13,210 48,310 6,300 62,020 290 780 280 1,180 260,770 136,400 6,100 5,220 850,800 195,780 406,490 88,740 19,260 2,500 15,910 2,750 875,620 359,080 18,130 18,570 45,340 8,520 24,200 2,520 890 130 1,060 130 51,860 123,420 24,890 61,190 440 1,560 900 1,730 181,370 116,830 1,740 2,370 912,580 402,940 9,510 11,500 9,213,230 3,692,176 1,879,820 159,954 5,651 5,457 119 113 13,474 49,276 6,332 62,330 287 772 288 1,215 265,985 137,082 6,039 5,377 867,816 199,696 408,522 89,184 19,067 2,475 16,387 2,833 893,132 360,875 17,949 19,127 46,247 8,690 24,321 2,533 881 129 1,092 134 52,897 125,888 25,014 61,496 436 1,544 927 1,782 184,997 117,414 1,723 2,441 930,832 404,955 9,415 11,845 9,259,296 3,710,637 1,889,219 160,754 5,934 5,730 125 119 13,609 49,522 6,395 62,642 290 776 291 1,221 260,665 134,340 5,918 5,269 885,172 205,687 416,692 91,860 19,448 2,549 16,715 2,918 785,956 317,570 15,795 16,832 48,559 8,256 25,537 2,406 925 123 1,147 127 56,071 124,629 26,515 60,881 462 1,529 983 1,764 199,797 126,807 1,861 2,636 1,023,915 445,451 10,357 13,030
Signature Assignment: T Rowen Technology X-Chip Sales Jonas Smith III MTH/216 Professor Roger O. Oliver April 10, 2017 Jonas Smith III MTH/216 Signature Assignment Page 2 Table of Contents Purpose: Predict three months of sales of Rowen Technology X-Chips: from 7/01/2016 to quarter end. I chose the Sales Data for Rowen Technology Corporation because it provides the opportunity to use and teach all the “weapons in our mathematics arsenal” discussed so far. I performed each of the following steps: As a result, I learned: Conclusion The final 7 quarters (gradually increasing Conclusion: I predict that the 9/30/2016 quarter end from a low point) gave me an opportunity to sales, W/O RETURNS, will be 420,000 units. project sales to the 9/30/2016 quarter-end using a 2nd degree polynomial trend equation. Page 3 Analyzed the full data table (Including returns) using the Frequency Distribution and Linear Trend. Accumulated and analyzed the full data set by Quarterly Totals, using Linear, Exponential, and Polynomial 2nd Degree Trends. Page 4 Page 5 Page 6 Page 6 Page6 Analyzed the most recent 12 quarters, with Returns removed, using Linear, Exponential, and Polynomial 2nd Degree Trends. Analyzed the most recent 7 quarters, with Returns removed, using Linear, Exponential, and Polynomial 2nd Degree Trends. Calculated all Trend values using the 2nd Degree Polynomial Trend for the 7 quarters, and calculated Correlation to the original data (98%). Used the 2nd Degree trend line formula to calculate the next quarter (42). The data seems random, without pattern, and widely scattered. The three R2 values were now in the 90% range. Upon further discussion with the Sales Manager, we decided to reduce the number of months and remove the Returns from the data. Each of the graphics showed a definite LOW point. We decided to reduce the data set again, beginning at this low point. Each of the three R2 values were now in the high 9% area. The best was the 2nd Degree, at 99%. The 7 quarters with 2nd Degree Trend is the best math. We can now determine the estimated next quarter. The 9/30/2016 quarter end sales, W/O RETURNS, will likely be 420,000 units. Jonas Smith III MTH/216 Signature Assignment Page 3 Purpose: Predict three months of sales of Rowen Technology X-Chips: from 7/01/2016 to quarter end. Below is a full 88-month Frequency Distribution and Scatter Plot. All months combined, Linear Trend. All sales distributed by a difference of 1. Sales, as shown in these graphics, are very close to a Normal Distribution, strongly implying a random distribution of sales. Excel’s calculation of “Best Fit, R2 = 0.4305 shows very poor linear trending. It is not reasonable to use all 88 historical months to predict three months in the future. Central Tendency: The Mean, Median, and Mode are very close. Even though the Distribution seems close to Normal, since the Mean and Median are on the right of the Mode, the distribution is slightly skewed right. Correlation: We have 66% correlation between the Month Number and the number sold. This means that we have almost random directionality – data is not consistently increasing or decreasing. Linear Best Fit: 43% correlation shows us again that we have poor directionality. Jonas Smith III MTH/216 Signature Assignment Page 4 A fully summarized 26-quarter Frequency Distribution and Scatter Plot Upon discussion with the Sales Manager, we concluded: a) We need to reduce the quantity of data. b) A Monthly trend may be better. Compares Quarter Number (X-Axis) to Sales (Y-Axis) Distribution: Each 3 difference in Sales Quarterly Distribution is no longer “Random”. The linear trend is much better. (R2 = 0.9512). Central Tendency: The mean and median are now much further from the mode. We no longer see “Random” data. Correlation between the quarter number and the sales in the quarter is 98% (Very good). The linear trend has a 98% fit, also very good. But a smaller subset of the data may also prove better. Jonas Smith III MTH/216 Signature Assignment Page 5 We decided to further reduce the data to just the final 12 quarters, and remove returns. The most recent 12 quarters, shown with Exponential and 2nd degree Trending, show a clear pattern – decreasing values in the first 5 quarters, and increasing final quarters. Central Tendency: The mean and median are to the right of the mode, but reasonably close. Standard Deviation returns to about 4. The Correlation between quarter number and sales is very far off. The best fit Polynomial 2nd Degree is 96%. The combination of poor correlation and great fit implies that a 2nd degree trend is the best. This encourages us to further limit our data to the final 7 quarters. Jonas Smith III MTH/216 Signature Assignment Page 6 The final 7 quarters: Exponential and 2nd Degree Trends Central Tendency: Mean and Median are nearly equal. The final 7 quarters are increasing with a very strong 2nd degree polynomial trend. The Standard Deviation is very small, the Correlation Coefficient is very close to 1, and the Best Fit R2 value is very close to 1. This data set is our best bet for a future projection. Pie Chart – Final 7 quarters. The final 7 quarters are gradually increasing from a low point. This gave us an opportunity to project sales using a 2nd degree polynomial. Predicted next quarter sales, W/O RETURNS: 420,000 units. Conclusion: The seven most recent quarters produced the best results. Jonas Smith III MTH/216 Signature Assignment Introducing Rowen Technology Corporation Monthly Sales Start Date 1/1/2010 2/1/2010 3/1/2010 4/1/2010 5/1/2010 6/1/2010 7/1/2010 8/1/2010 9/1/2010 10/1/2010 11/1/2010 12/1/2010 1/1/2011 2/1/2011 3/1/2011 4/1/2011 5/1/2011 6/1/2011 7/1/2011 8/1/2011 9/1/2011 10/1/2011 11/1/2011 12/1/2011 1/1/2012 2/1/2012 3/1/2012 4/1/2012 5/1/2012 0 1 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 X 10,000 2 4 -1 2 3 1 2 4 0 4 4 -2 5 2 0 3 4 0 5 6 -1 5 1 5 1 2 8 5 2 6/1/2012 39 6 7/1/2012 8/1/2012 40 41 9 7 9/1/2012 42 -3 10/1/2012 11/1/2012 43 44 3 3 12/1/2012 45 8 1/1/2013 2/1/2013 46 47 7 6 3/1/2013 48 1 4/1/2013 5/1/2013 49 50 1 3 6/1/2013 51 10 7/1/2013 52 2 8/1/2013 53 4 9/1/2013 54 9 10/1/2013 55 3 11/1/2013 56 3 12/1/2013 57 10 1/1/2014 58 4 2/1/2014 59 6 3/1/2014 60 6 4/1/2014 61 5 5/1/2014 62 3 6/1/2014 63 11 7/1/2014 64 8 8/1/2014 65 7 9/1/2014 66 4 10/1/2014 11/1/2014 67 68 5 4 12/1/2014 69 14 1/1/2015 70 4 2/1/2015 71 6 3/1/2015 72 13 4/1/2015 5/1/2015 73 74 8 5 6/1/2015 75 11 7/1/2015 8/1/2015 76 77 9 7 9/1/2015 78 12 10/1/2015 11/1/2015 79 80 7 9 12/1/2015 81 12 1/1/2016 2/1/2016 82 83 7 6 3/1/2016 84 15 4/1/2016 5/1/2016 85 86 8 10 6/1/2016 87 15 After discussing the Signature Assignment with Rowen Technology Corporation Chief Sales Officer, I received the following note: “Thank-you for your interest in Rowen Technology Corporation. We are traded on NASDAQ. We are pleased to share with you our sales history, as you requested. Rowen Technology Corporation manufactures and sells multiply configurations of computer chips. This data table represents a recent sample of sales: monthly sales of X-Chips to the Super Computer Industry. All sales data for the Company is reported in units of 10,000. For example, in the month starting 1/1/2010, 20,000 X-Chips were sold. All sales are reported as they happen each month; all reporting adjustments are recorded each quarter-end. For example, total sales for month 3/1/2010 include adjustments for 1/1, 2/1, and 3/1.” As the project continued, I had further discussions with him and other appropriate employees. Page 2
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