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President Trump’s approval rating stands at 41% this week among all Americans.
Reuters/Ipsos Data: Core Political (06/13/2018)
Reuters/Ipsos Data: Core Political (06/13/2018)
Anne Marie Moran
Immigration remains the top of mind issue among all Americans. President Trump's approval remains steady at 41%.
Reuters/Ipsos Data: Core Political (07/03/2018)
Reuters/Ipsos Data: Core Political (07/03/2018)
Ipsos Public Affairs
President Trump’s approval rating remains statistically unchanged from last week, with 42% of all Americans approving of the job he is doing as president.
Reuters/Ipsos Data: Core Political (06/27/2018)
Reuters/Ipsos Data: Core Political (06/27/2018)
Ipsos Public Affairs
President Trump’s approval stands at 43% among all American adults as Attorney General Jeff Sessions continues to top the Reuters/Ipsos Wh’Exit poll.
Reuters/Ipsos Data: Core Political (06/20/2018)
Reuters/Ipsos Data: Core Political (06/20/2018)
Ipsos Public Affairs
Let’s not cite the drop, just state what it is… This week, 42% of all Americans approve of President Trump’s job performance. Registered voters are in line with the rest of the general public, with 44% approving of the job Trump is doing as president. Party lines continue to show division with one third of Independents (33%), 85% of Republicans, and just 14% of Democrats approving of Trump’s job performance. Republicans and Democrats are neck and neck in this week’s generic ballot question – 33% of Americans would vote for a Democrat and 33% would vote for a Republican if the election were held today. Among registered voters, Democrats (39%) have a slight edge on Republicans (37%). Independents are evenly split between Republicans (15%), Democrats (17%) and a third party (18%). Healthcare (16%) continues to be perceived as the most important problem facing America today. The economy (13%) is a close second to healthcare. Terrorism (11%) and morality (11%) are tied, and round out of the top issues. Attorney General, Jeff Sessions, continues to lead the Wh’Exit Poll, with 17% of Americans reporting they believe he will be the next to resign or be fired from the White House. EPA Administrator, Scott Pruitt, is no longer perceived to be in the hot seat, with 10% of Americans reporting he will be next on the chopping block. Betsy Devos, Secretary of Education (10%) is tied for second place with Scott Pruitt.
Reuters/Ipsos Data: Core Political (05/30/2018)
Reuters/Ipsos Data: Core Political (05/30/2018)
Ipsos Public Affairs
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President Trump’s approval rating stands at 41% this week among all Americans.
Reuters/Ipsos Data: Core Political (06/13/2018)
Reuters/Ipsos Data: Core Political (06/13/2018)
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Immigration remains the top of mind issue among all Americans. President Trump's approval remains steady at 41%.
Reuters/Ipsos Data: Core Political (07/03/2018)
Reuters/Ipsos Data: Core Political (07/03/2018)
Ipsos Public Affairs
President Trump’s approval rating remains statistically unchanged from last week, with 42% of all Americans approving of the job he is doing as president.
Reuters/Ipsos Data: Core Political (06/27/2018)
Reuters/Ipsos Data: Core Political (06/27/2018)
Ipsos Public Affairs
President Trump’s approval stands at 43% among all American adults as Attorney General Jeff Sessions continues to top the Reuters/Ipsos Wh’Exit poll.
Reuters/Ipsos Data: Core Political (06/20/2018)
Reuters/Ipsos Data: Core Political (06/20/2018)
Ipsos Public Affairs
Let’s not cite the drop, just state what it is… This week, 42% of all Americans approve of President Trump’s job performance. Registered voters are in line with the rest of the general public, with 44% approving of the job Trump is doing as president. Party lines continue to show division with one third of Independents (33%), 85% of Republicans, and just 14% of Democrats approving of Trump’s job performance. Republicans and Democrats are neck and neck in this week’s generic ballot question – 33% of Americans would vote for a Democrat and 33% would vote for a Republican if the election were held today. Among registered voters, Democrats (39%) have a slight edge on Republicans (37%). Independents are evenly split between Republicans (15%), Democrats (17%) and a third party (18%). Healthcare (16%) continues to be perceived as the most important problem facing America today. The economy (13%) is a close second to healthcare. Terrorism (11%) and morality (11%) are tied, and round out of the top issues. Attorney General, Jeff Sessions, continues to lead the Wh’Exit Poll, with 17% of Americans reporting they believe he will be the next to resign or be fired from the White House. EPA Administrator, Scott Pruitt, is no longer perceived to be in the hot seat, with 10% of Americans reporting he will be next on the chopping block. Betsy Devos, Secretary of Education (10%) is tied for second place with Scott Pruitt.
Reuters/Ipsos Data: Core Political (05/30/2018)
Reuters/Ipsos Data: Core Political (05/30/2018)
Ipsos Public Affairs
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Crime in India - compendium 2008 : By National Crime Records Bureau, Ministry...
Crime in India - compendium 2008 : By National Crime Records Bureau, Ministry...
People's Archive of Rural India
The concern about national security has increased significantly since the 26/11 attacks at Mumbai, India. However, information and technology overload hinders the effective analysis of criminal and terrorist activities. Data mining applied in the context of law enforcement and intelligence analysis holds the promise of alleviating such problem. In this paper we use a clustering/classify based model to anticipate crime trends. The data mining techniques are used to analyze the city crime data from Tamil Nadu Police Department. The results of this data mining could potentially be used to lessen and even prevent crime for the forth coming years
An Intelligence Analysis of Crime Data for Law Enforcement Using Data Mining
An Intelligence Analysis of Crime Data for Law Enforcement Using Data Mining
Waqas Tariq
Table of Contents Introduction 2 Descriptive statistics 3 Violent and property crimes 3 Categories of Violent and property crimes 5 Graphical Representation 9 Histogram 9 Boxplot 11 Scatterplot 12 Confidence interval 13 Murder 13 Non-negligent manslaughter rate 14 Paired t test 15 Ethical issues 17 Conclusion 18 Introduction I consider data sets namely number of violent crimes, number of property crimes, violent crime rates, and property crime rates for the years 1960 through 2012 for my analysis. Violent crime have has 4 categories Murder and non-negligent Manslaughter, Legacy rape /1, Robbery and Aggravated assault. Property crimes has 3 categories Burglary, Larceny-theft and Motor vehicle theft. Descriptive statisticsViolent and property crimes The table of descriptive statistics for total of violent crimes, total of property crimes, violent crime rates, and property crime rates along with population is given below. Population Violent crime total Property crime total Violent Crime rate Property crime rate Mean 1027647.396 2332.321 51414.22642 213.0736 4940.675 Standard Error 30163.37005 152.2037 2356.550621 11.26415 179.483 Median 1062000 2744 54573 247.6 5143.3 Mode 1187000 #N/A #N/A 231.9 #N/A Standard Deviation 219592.6486 1108.06 17155.94748 82.00426 1306.656 Sample Variance 48220931312 1227797 294326533.9 6724.699 1707349 Kurtosis -1.241236819 -0.76307 -0.34898103 0.141618 -0.73214 Skewness -0.208033283 -0.78788 -0.684862884 -1.25813 -0.43517 Range 757318 3470 67533 277.7 4906.3 Minimum 632772 138 14405 21.8 2276.5 Maximum 1390090 3608 81938 299.5 7182.8 Sum 54465312 123613 2724954 11292.9 261855.8 Count 53 53 53 53 53 There are 53 observation for each year from 1960 to 2012. All the variables total of violent crimes, total of property crimes, violent crime rates, and property crime rates are observed to be skewed to the left. For a skewed data, median is considered as the best measure of central tendency. Median is the middle observation in a series of data when arranged in ascending order. The value of median for total of violent crimes, total of property crimes, violent crime rates, and property crime rates is 2744, 54573, 247.6, and 5143.3 respectively. Mean is defined as the sum of all observation divided by total number of observations. The mean value for total of violent crimes, total of property crimes, violent crime rates, and property crime rates is 2332.321, 51414.22, 213.07 and 4940.67 respectively. The value of standard deviation and variance is a measure of dispersion around mean. The value of standard deviation for total of violent crimes, total of property crimes, violent crime rates, and property crime rates is 1108.06, 17155.94, 82 and 1306.65. The low value of standard deviation of Violent Crime rate and Property crime rate implies that its mean is reliable. But high value of standard deviation of total of violent crimes and total of property crimes implies that mean is not reliable. Categories of Violen ...
Table of ContentsIntroduction2Descriptive statistics3Viole.docx
Table of ContentsIntroduction2Descriptive statistics3Viole.docx
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High Point Police Department: Statistical Report 2013 (Focused Deterrence)
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Lviv IT Arena is a conference specially designed for programmers, designers, developers, top managers, inverstors, entrepreneurs and startuppers. Annually it takes place at the beginning of October in Lviv at Arena Lviv stadium. In 2016 the conference gathered more than 1800 participants and over 100 speakers from companies like Microsoft, Philips, Twitter, UBER and IBM. More details about the conference at itarena.lviv.ua.
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Why is Cybersecurity so important today than ever before? Because it is one of the 10 most important risks facing human race. Data thefts and breaches are causing a great deal of damage to businesses both in terms of revenue and reputation losses.
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Microsoft's Threat Matrix for Kubernetes helps organizations understand the attack surface a Kubernetes deployment introduces to their environments. This ensures that adequate detections and mitigations are in place. By covering over 40 different attacker techniques, defenders can learn about Kubernetes-specific mitigations and controls to deploy to their environments. In this session, we will explore the MS-TA9013 Host Path Mount technique, which is commonly used by attackers to perform privilege escalation in a Kubernetes cluster. Attendees will learn how attackers and defenders can: * Escape the container's host volume mount to gain persistence on an underlying node * Move laterally from the underlying node into the customer's cloud environment * Analyze Kubernetes audit logs to detect pods deployed with a hostPath mount * Deploy an admission controller that prevents new pods from using a hostPath mount
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The concern about national security has increased significantly since the 26/11 attacks at Mumbai, India. However, information and technology overload hinders the effective analysis of criminal and terrorist activities. Data mining applied in the context of law enforcement and intelligence analysis holds the promise of alleviating such problem. In this paper we use a clustering/classify based model to anticipate crime trends. The data mining techniques are used to analyze the city crime data from Tamil Nadu Police Department. The results of this data mining could potentially be used to lessen and even prevent crime for the forth coming years
An Intelligence Analysis of Crime Data for Law Enforcement Using Data Mining
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Table of Contents Introduction 2 Descriptive statistics 3 Violent and property crimes 3 Categories of Violent and property crimes 5 Graphical Representation 9 Histogram 9 Boxplot 11 Scatterplot 12 Confidence interval 13 Murder 13 Non-negligent manslaughter rate 14 Paired t test 15 Ethical issues 17 Conclusion 18 Introduction I consider data sets namely number of violent crimes, number of property crimes, violent crime rates, and property crime rates for the years 1960 through 2012 for my analysis. Violent crime have has 4 categories Murder and non-negligent Manslaughter, Legacy rape /1, Robbery and Aggravated assault. Property crimes has 3 categories Burglary, Larceny-theft and Motor vehicle theft. Descriptive statisticsViolent and property crimes The table of descriptive statistics for total of violent crimes, total of property crimes, violent crime rates, and property crime rates along with population is given below. Population Violent crime total Property crime total Violent Crime rate Property crime rate Mean 1027647.396 2332.321 51414.22642 213.0736 4940.675 Standard Error 30163.37005 152.2037 2356.550621 11.26415 179.483 Median 1062000 2744 54573 247.6 5143.3 Mode 1187000 #N/A #N/A 231.9 #N/A Standard Deviation 219592.6486 1108.06 17155.94748 82.00426 1306.656 Sample Variance 48220931312 1227797 294326533.9 6724.699 1707349 Kurtosis -1.241236819 -0.76307 -0.34898103 0.141618 -0.73214 Skewness -0.208033283 -0.78788 -0.684862884 -1.25813 -0.43517 Range 757318 3470 67533 277.7 4906.3 Minimum 632772 138 14405 21.8 2276.5 Maximum 1390090 3608 81938 299.5 7182.8 Sum 54465312 123613 2724954 11292.9 261855.8 Count 53 53 53 53 53 There are 53 observation for each year from 1960 to 2012. All the variables total of violent crimes, total of property crimes, violent crime rates, and property crime rates are observed to be skewed to the left. For a skewed data, median is considered as the best measure of central tendency. Median is the middle observation in a series of data when arranged in ascending order. The value of median for total of violent crimes, total of property crimes, violent crime rates, and property crime rates is 2744, 54573, 247.6, and 5143.3 respectively. Mean is defined as the sum of all observation divided by total number of observations. The mean value for total of violent crimes, total of property crimes, violent crime rates, and property crime rates is 2332.321, 51414.22, 213.07 and 4940.67 respectively. The value of standard deviation and variance is a measure of dispersion around mean. The value of standard deviation for total of violent crimes, total of property crimes, violent crime rates, and property crime rates is 1108.06, 17155.94, 82 and 1306.65. The low value of standard deviation of Violent Crime rate and Property crime rate implies that its mean is reliable. But high value of standard deviation of total of violent crimes and total of property crimes implies that mean is not reliable. Categories of Violen ...
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Enterprise Knowledge’s Urmi Majumder, Principal Data Architecture Consultant, and Fernando Aguilar Islas, Senior Data Science Consultant, presented "Driving Behavioral Change for Information Management through Data-Driven Green Strategy" on March 27, 2024 at Enterprise Data World (EDW) in Orlando, Florida. In this presentation, Urmi and Fernando discussed a case study describing how the information management division in a large supply chain organization drove user behavior change through awareness of the carbon footprint of their duplicated and near-duplicated content, identified via advanced data analytics. Check out their presentation to gain valuable perspectives on utilizing data-driven strategies to influence positive behavioral shifts and support sustainability initiatives within your organization. In this session, participants gained answers to the following questions: - What is a Green Information Management (IM) Strategy, and why should you have one? - How can Artificial Intelligence (AI) and Machine Learning (ML) support your Green IM Strategy through content deduplication? - How can an organization use insights into their data to influence employee behavior for IM? - How can you reap additional benefits from content reduction that go beyond Green IM?
Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...
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