Part One: The relationship between extraversion, agreeableness, and neuroticismCorrelation CoefficientTable 1 below presents the Pearson correlation coefficients depicting the relationship between each pair of variables.Table 1: Correlation MatrixCorrelation MatrixEXTRANEUROAGREEEXTRAPearson's rp-valueNEUROPearson's r-0.243*p-value0.015AGREEPearson's r0.299**-0.046p-value0.0030.649Note. * p .05, ** p .01, *** p .001 There is a statistically significant negative association between extraversion and neuroticism as shown by the Pearson correlation value of r = -0.243 and the p-value of .015, which is lower than the p=0.05 significance level. Negative correlation values range from 0 to -1, where -1 indicates an extremely strong negative association between the variables (Christmann & Badgett, 2009). The r value of -0.243 points to a weak negative association between extraversion and neuroticism (Christmann & Badgett, 2009). The implication is that a unit increase in the level of extraversion decreases neuroticism by 0.243 units. At the same time, the association between agreeableness and extraversion is positive…...

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References

Christmann, E. P, & Badgett, J. L. (2009). Interpreting assessment data: Statistical techniques you can use. NSTA Press.

Saliya, C. A. (2023). Doing social research and publishing results: A guide to non-native English speakers. Springer Nature.

Excel
"According to T-100 Domestic Market, the top seven airlines in the United States by domestic boarding in a recent year were Southwest Airlines with 81.1 million, Delta Airlines with 79.4 million, American Airlines with 72.6 million, United Airlines with 56.3 million, Northwest Airlines with 43.3 million, US Airways with 37.8 million, and Continental Airlines with 31.5 million. Using Excel, construct a pie chart and a bar graph to depict this information". ( Kaplan University, 2016 p 1).

Pie Chart

Bar Graph

Department of the Interior releases figures on mineral production. The following are the 15 leading states in nonfuel mineral production in the United States in 2008". ( Kaplan University, 2016 p 2).

State

Value ($Billon)

Arizona

Nevada

Florida

Utah

California

Texas

Minnesota

Alaska

Missouri

Colorado

Michigan

Wyoming

Georgia

New Mexico

Pennsylvania

1.68

Descriptive Statistics

Value ($Billion)

Mean

3,613333

Standard Error

0,496542

Median

2,98

Mode

#N/A

Standard Deviation

1,923099

Sample Variance

3,69831

Kurtosis

3,583069

Skewness

1,783901

ange

7,27

Minimum

1,68

Maximum

8,95

Sum

54,2

Count

15

Confidence Level (95,0%)

1,064977

The…...

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Reference

Kaplan University (2016). Using Data Analysis in Excel. USA

the country aims for the working population that had been estimated to be 125 million in 2006 to reach 147.8 million by 2020 (Economy watch, 2010).
elationship between unemployment and Brazil's economy

Unemployment in Brazil directly affects the economy of the country in several ways this include economic costs; rates of unemployment being persistent in Brazil indicate that there will be market failure in the country. Unemployment is an indication that resources are being wasted thus leading to loss of potential output and eventually a reduction in allocative efficiency. Brazil's economy will then be seen to operate below the maximum output that the country has potential of.

Unemployment also affects the economy of Brazil through financial costs. The Government of Brazil and the entire nation suffers some costs due to unemployment. There are some benefits that the government has to pay the unemployed and therefore the higher the rates of unemployment the more…...

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References

Economy watch. (2010).Brazil unemployment. Retrieved March 09, 2013 from  http://www.economywatch.com/unemployment/countries/brazil.html 

Trading Economies. (2012). Brazil Unemployment Rate. Retrieved March 09, 2013 from http://www.tradingeconomics.com/brazil/unemployment-rate

From Supply Chain Efficiency to Customer Segmentation Focus

Because of this focus on supply chain forecasting accuracy and efficiency, the need for capturing very specific customer data becomes critical. The case study portrays the capturing of segmentation data as focused on growing each of the brands mentioned that VF relies on this data to base marketing, location development and store introductions, and pricing strategies on. In reality, the data delivered for these marketing programs and location-based analyses is also providing an agile and scalable platform for VF to more effectively manage and mitigate its supply chain risk as well.

elying on Alteryx for data analysis as it has superior capability to Microsoft Access and Excel in conjunction with the use of SC Software for geo-demographic analysis, VF has created a workflow for translating data warehouses into the basis of marketing and supply chain strategies. The strategic goal of getting the right product…...

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References

Adnan, M., Longley, P., Singleton, a., & Brunsdon, C. (2010). Towards Real-Time Geodemographics: Clustering Algorithm Performance for Large Multidimensional Spatial Databases. Transactions in GIS, 14(3), 283-297.

Paul Sheldon Foote, & Malini Krishnamurthi. (2001). Forecasting using data warehousing model: Wal-Mart's experience. The Journal of Business Forecasting Methods & Systems, 20(3), 13-17.

Yang-Im Lee, & Peter R.J. Trim. (2006). Retail marketing strategy: The role of marketing intelligence, relationship marketing and trust. Marketing Intelligence & Planning, 24(7), 730-745.

Lewis, M., Hornyak, R., Patnayakuni, R., & Rai, a.. (2008). Business Network Agility for Global Demand-Supply Synchronization: A Comparative Case Study in the Apparel Industry. Journal of Global Information Technology Management, 11(2), 5-29.

Argumentative Essay between Data Collection Versus Data Privacy Argumentative Essay between Data Collection Versus Data PrivacyBased on Statista esearch Department (2022), the amount of data created, captured, copied, and consumed worldwide is approximated to be reaching 64.2 zettabytes. It is projected to increase rapidly to over 180 zettabytes over the next five years. The growth of these individual data sets collected daily exposes many individuals to data privacy issues. For instance, many global researchers rely primarily on various data sets, introducing the notion that they should openly share this data. Still, the primary concern is that these researchers are given little guidance on the concept of data privacy. It has been a common scenario mainly in developing countries, which many people have feared. They are afraid that it might lead to breaching confidentiality with the claim that the infrastructure used is not up to the standards. There are no safe…...

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ReferencesDepartment, S. (2022). Total data volume worldwide 2010-2025 | Statista. Statista. Retrieved 24 April 2022, from   M., & RAINIE, L. (2015). Americans\\\\\\\\\\\\\\\' Views About Data Collection and Security. Pew Research Center: Internet, Science & Tech. Retrieved 24 April 2022, from  https://www.pewresearch.org/internet/2015/05/20/americans-views-about-data-collection-and-security/ .Pisani, E., Aaby, P., Breugelmans, J., Carr, D., Groves, T., & Helinski, M. et al. (2016). Beyond open data: realising the health benefits of sharing data: Table 1. BMJ, i5295.  https://doi.org/10.1136/bmj.i5295 Strain, M., Eberstadt, N., Nunn, R., & Schanzenbach, D. (2017). In order that they might rest their arguments on facts: The vital role of government-collected data. Brookings. Retrieved 24 April 2022, from  https://www.brookings.edu/research/in-order-that-they-might-rest-their-arguments-on-facts-the-vital-role-of-government-collected-data/ .Wieczorkowski, J., & Polak, P. (2017). Big data and privacy: The study of privacy invasion acceptance in the world of big data. Online Journal Of Applied Knowledge Management, 5(1), 57-71.  https://doi.org/10.36965/ojakm.2017.5(1)57-71https://www.statista.com/statistics/871513/worldwide-data-created/ .MADDEN,

i) Part 1: Descriptive Variables Frequency Tables for Demographic VariablesTable 1.Variable 1: Agency where one WorksAGENCYFrequencyPercentValid PercentCumulative PercentValidAF333515.65.65.6AG433527.27.212.8AM1837.3.313.1A7000511.711.724.8BG526.1.124.9BO338.1.125.0CM207253.53.528.5CT476.1.128.5CU633.1.128.6DD308775.25.233.8DJ309785.25.239.0DL80751.41.440.3DN86241.41.441.8D1115.2.242.0ED2592.4.442.4EE1379.2.242.6EP79721.31.344.0FC594.1.144.1FQ470.1.144.1FT638.1.144.2GS71571.21.245.4HE430297.27.252.6HF412.1.152.7HS7389912.412.465.1HU4628.8.865.8IB829.1.166.0IN282904.74.770.7KS326.1.170.8NF940.2.270.9NL859.1.171.1NN115681.91.973.0NQ1743.3.373.3NU2308.4.473.7NV478828.08.081.7OM3069.5.582.2413.1.182.3SB1543.3.382.5SE3394.6.683.1SK355.1.183.1SN520.1.183.2ST72281.21.284.4SZ263184.44.488.8TD215523.63.692.4T420277.07.099.5XX3157.5.5100.0Total598003100.0100.0The Department of Health and Human Services (HHS) accounted for the greatest number of participants among the 85 participating agencies, at 12.4 percent, followed by the Department of Agriculture at 11.7 percent. The Department of Commerce accounted for 7 percent of participants in the survey.Table 2.Variable 2: SexDSEXFrequencyPercentValid PercentCumulative PercentValid7910013.213.213.2Male29301449.049.062.2Female22588937.837.8100.0Total598003100.0100.049 percent of the survey participants were male, 37.8 percent were female, and 13.2 percent preferred did not indicate their gender.Table 3.Variable 3: Education LevelDEDUCFrequencyPercentValid PercentCumulative PercentValidNo degree7738012.912.912.9Doctorate14916324.924.937.9Bachelors18297930.630.668.5Masters18848131.531.5100.0Total598003100.0100.024.9 percent of participants had a Doctorate degree, 31.5 percent had a masters degree, 30 percent had a Bachelors degree, and 12.9 percent had some college education with no degree.Table 4Variable 4: Tenure in the Federal AgencyDFEDTENFrequencyPercentValid PercentCumulative PercentValid1-3 years7307612.212.212.26 -10years22171237.137.149.311-20 years16263427.227.276.54 -5 years14058123.523.5100.0Total598003100.0100.037 percent of participants have been with the federal government for 6 to 10…...

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References Hair, J. F., Hult, G. T., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural modeling (3rd ed.). SAGE.

Data Warehousing: A Strategic Weapon of an Organization.
Within Chapter One, an introduction to the study will be provided. Initially, the overall aims of the research proposal will be discussed. This will be followed by a presentation of the overall objectives of the study will be delineated. After this, the significance of the research will be discussed, including a justification and rationale for the investigation.

The aims of the study are to further establish the degree to which data warehousing has been used by organizations in achieving greater competitive advantage within the industries and markets in which they operate. In a recent report in the Harvard Business eview (2003), it was suggested that companies faced with the harsh realities of the current economy want to have a better sense of how they are performing. With growing volumes of data available and increased efforts to transform that data into meaningful knowledge that can…...

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References

Agosta, L. (2003). Ask the Expert. Harvard Business Review, 81(6), 1.

Database: Business Source Premier.

Babcock, Charles (1995). Slice, dice & deliver. Computerworld, 29, 46, 129 -132.

Beitler, S.S., & Lean, R. (1997). Sears' EPIC Transformation: Converting from Mainframe Legacy Systems to Online Analytical Processing (OLAP). Journal of Data Warehousing (2:2), 5-16.

City of Alexandria -- Time Series Data
Tufte (2001) and other ambassadors of the visual display of data have shown us how easily it is to understand complex data when it is graphically represented in ways that our minds are designed to understand. Tufte argues that "experience with the analysis of data…is essential for achieving precision and grace in the presence of statistics, .but even textbook of graphical design are silent about how to think about numbers" (Tufte, 2001, p. 104). Tufke remarks, that "Illustrators too often see their work as an exclusively artistic enterprise -- the words "creative," "concept," and "style" combine regularly in all possible permutations -- a Big Think jargon for the small task of constructing a time-series a few data points long" (Tufte, 2001, p. 204). Visual display of data has other uses than simply an elegant way to view, appreciate, and analyze data. The process of…...

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References

Averch, H.A. (XXX). Chapter 10 Using expert judgment. [In XXXX].

Gladwell, M. (2007, November 12). Dangerous minds: Criminal profiling made easy. The New Yorker.

Miller, J.E. (2005). The Chicago guide to writing about multivariate analysis. Chicago, Il.: University of Chicago Press.

Meier,, K.J., Brudney, J.L., and Bohte, J. (2005). Applied statistics for public and nonprofit Administration, 6th ed. Belmont, CA: Thompson Wadsworth.

SQL and ig Data
Gaining greater insights into terabytes of unstructured and structured data organizations have been collecting in many cases for decades across diverse computing and storage platforms are increasingly being unified through advanced data and system architectures. ig Data is the term used to define very large, diverse data sets that contain both structured and unstructured data that defy analysis using conventional database management and analytics applications (International Journal of Micrographics & Optical Technology, 2010). ig Data is an area generating much interest in enterprises as this collection of data analysis, aggregation and extraction techniques continue to deliver valuable insights into how companies can become more competitive (Datskovsky, 2013). Structured Query Language (SQL) is a widely accepted approach to querying databases, aggregating and analyzing data and creating useful reports that guide decision making in organizations (Rys, 2011). Enterprise software companies are creating ig Data analytics applications that include…...

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Bibliography

Baker, B. 2013, "Enterprise Analytics: Optimize Performance, Process and Decisions Through Big Data," Quality Progress, vol. 46, no. 6, pp. 68.

Datskovsky, Galina, PhD., C.R.M. 2013, "Harnessing Big Data for Competitive Advantage," Information Management, vol. 47, no. 1, pp. 1.

Ferguson, R.B. 2013, "The Big Deal About a Big Data Culture (and Innovation)," MIT Sloan Management Review, vol. 54, no. 2, pp. 1-5.

Ferguson, R.B. 2012, "The Storage and Transfer Challenges of Big Data," MIT Sloan Management Review, vol. 53, no. 4, pp. 1-4.

Scores
Generally speaking, a set of test scores such as those provided would not be considered continuous data, as in most test scenarios there are a finite number of scores possible. If a test has 100 questions and is worth one point per question, then the only possible scores are those whole numbers from 0 to 100 -- a finite set of responses rather than an infinitely reducible continuum. In order for this data set to be considered continuous, it would need to be demonstrated that partial points were possible to a degree that enabled a full continuum of scores to be presented.

As the data set represents test scores, it must certainly be considered at least an interval scale, with the two point difference between 79 and 81 the same interval as the two points between 97 and 99. It can also be inferred that these are percentages, and that…...

tatus of Open Data in Europe
Open Data

Open data refers to the idea of having certain data freely available for people to republish and use as they wish (Open Government Data, n.d). There are no restrictions like patents, mechanism control, or copyright placed on the person using the data. Open data is mainly aimed at allowing governments to share their information with the public. This brings about Open Government Data that refers to any data commissioned or produced by a government that can be freely used, redistributed, and reused by anyone. For data to be considered open, it should be readily available and the person requiring the data should not have to make a request (Bedini et al.). The advancement of the internet and World Wide Web has pushed for open government data. The advancements made on the internet have allowed people from across the world to access data without having…...

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Sheridan, J., & Tennison, J. (2010). Linking UK Government Data. Paper presented at the LDOW.

Vienna City Administration. (N.D,). Open Data in Vienna, from  http://www.wien.gv.at/english/politics-administration/open-data.html 

Wonderlich, J. (2010). TEN PRINCIPLES FOR OPENING UP GOVERNMENT INFORMATION, from  https://sunlightfoundation.com/policy/documents/ten-open-data-principles/

Visualize Your Data
In defining a system to be used for visualizing case information and also capable of supporting geographic information systems (GIS) crime and incident analysis, standardizing on the ESI ArcGIS Server and its Law Enforcement Data Model would provide a scalable, agile foundation for data visualization and future growth. The components or modules that need to be included in the system include Crime Analysis, Data Fusion/Intelligence Analysis, Community Corrections, COMPSTAT statistics module, esource Management, Process outing and MDT/MDC integration. Taken together these components and ArcGIS Server platform would provide for greater criminal analysis visualization and prediction over time. The use of data visualization in crime prediction and prevention, coupled with pattern and data analysis, has shown to have significant effects on reducing crime in targeted regional and metro areas (Xu, Chen, 2005).

Advantages Of Including A Data Visualization Component

There are many advantages of having a data visualization software platform and…...

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References

Heather Havenstein. (2006, January). Data Analysis May Help LAPD Fight Terrorism. Computerworld, 40(3), 4.

Pike, W., Bruce, J., Baddeley, B., Best, D., Franklin, L., May, R., Rice, D., Riensche, R., & Younkin, K.. (2009). The Scalable Reasoning System: Lightweight visualization for distributed analytics. Information Visualization: Special Issue on Visual Analytics, Science and Technology, 8(1), 71-84.

Post, F.. (2011). Data Visualization: Featuring Interactive Visual Analysis. Computer Graphics Forum, 30(2), xxiii-xxiii.

Jennifer Xu, & Hsinchun Chen. (2005). Criminal network analysis and visualization. Association for Computing Machinery. Communications of the ACM, 48(6), 100-107.

epidemiological data, and then exploring possibility of a causal connection between lack of government funding for community-based treatments and increase in HIV incidence in queer male communities.
Both statistical descriptive and inferential tests will be employed.

The descriptive tests summarize and describe the data. These would include frequency analysis e.g. Of the amount of men diagnosed as queer, and frequency that the participants experienced homophobia. Univariate analysis would focus on one variable, e.g. frequency of homophobia, by analyzing the mean, the distribution, the central tendency, and the dispersion of the occurrence.

The distribution would provide some assumption of the pattern of the range: whether normal or skewed.

The central tendency would, in this case, measure the mean of the data, i.e. average number of males that experienced discriminatory treatment. Dispersion would be another descriptive tool that measures the spread of values around the central tendency, i.e. range and standard deviation.

Finally, we would want…...

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Reference

AllPsych Online:  http://allpsych.com/onlinetexts.html

Discussion of Analytics1There are three main types of analytical techniques: descriptive, predictive, and prescriptive. Each type of analysis has its own strengths and weaknesses, and it is important to choose the right technique for the job at hand.Descriptive analytics is all about understanding what has happened in the past. It involves collecting data and then using that data to generate insights. For example, a company might use descriptive analytics to understand patterns in customer behavior. By analyzing data on customer purchases, interactions, and other behaviors, companies can gain insights into which customers are most loyal, what motivates them to buy, and what factors influence their decision-making. This information can then be used to tailor marketing and sales strategies to better meet the needs of specific customer groups (Frankenfield, 2020).Predictive analytics takes things one step further by using historical data to make predictions about what will happen in the future. For…...

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ReferencesChen, C. H., Härdle, W. K., & Unwin, A. (Eds.). (2007). Handbook of data visualization. Springer Science & Business Media.Delen, D. (2014). Real-world data mining: applied business analytics and decision making. FT Press.Frankenfeld, J. (2020). Descriptive analytics. Retrieved from   M., & Denis, D. (2005). The early origins and development of the scatterplot. Journal of the History of the Behavioral Sciences, 41(2), 103-130.Hsu, M. F., Hsin, Y. S., & Shiue, F. J. (2022). Business analytics for corporate risk management and performance improvement. Annals of Operations Research, 315(2), 629-669.Pröllochs, N., & Feuerriegel, S. (2020). Business analytics for strategic management: Identifying and assessing corporate challenges via topic modeling. Information & Management, 57(1), 103070.Seddon, P. B., Constantinidis, D., Tamm, T., & Dod, H. (2017). How does business analytics contribute to business value?. Information Systems Journal, 27(3), 237-269.Segal, T. (2021). What is prescriptive analytics. Retrieved from  https://www.investopedia.com/terms/p/prescriptive-analytics.asp Shanks, G., & Bekmamedova, N. (2012). Achieving benefits with business analytics systems: An evolutionary process perspective. Journal of Decision Systems, 21(3), 231-244.Shmueli, G., Bruce, P. C., Yahav, I., Patel, N. R., & Lichtendahl Jr, K. C. (2017). Data mining for business analytics: concepts, techniques, and applications in R. John Wiley & Sons.https://www.investopedia.com/terms/d/descriptive-analytics.asp Friendly,

A full data set that has been converted thusly is a standardized normal distribution.
Assuming z follows a standard normal distribution, if a = 1 than P (z...

Modern newsgathering technologies have significantly increased the efficiency and effectiveness of gathering news. These technologies have revolutionized the way journalists collect information, process data, and report news stories. Some of the key benefits of modern newsgathering technologies include:

1. Real-time reporting: With tools like social media platforms, live streaming services, and mobile journalism apps, journalists can report news as it happens, providing timely and up-to-date information to the audience.

2. Enhanced multimedia capabilities: Modern newsgathering technologies enable journalists to capture high-quality photos, videos, and audio recordings, enhancing the visual and auditory elements of news stories.

3. Improved accessibility: With the internet and digital....

Effectiveness of Modern Newsgathering Technologies

Modern newsgathering technologies have revolutionized the way journalists gather and report news. These technologies have brought about numerous benefits, enabling journalists to work more efficiently, effectively, and reach a wider audience.

Real-Time Reporting and Breaking News Coverage

One of the most significant advantages of modern newsgathering technologies is the ability to report breaking news events in real-time. Social media platforms, such as Twitter and Facebook, allow journalists to share updates, videos, and photos from the scene of an event almost instantly. Live streaming services enable news organizations to broadcast live video footage, providing viewers with an immersive....

Ethical Implications of Self-Driving Cars

The autonomous revolution-driven vehicles are poised to transform the transportation landscape, offering promising benefits such as reduced traffic congestion, improved road safety, and increased accessibility for people with disabilities. However, the advent of self-driving cars also raises a myriad of ethical concerns that demand careful consideration.

1. Safety and Liability

Self-driving cars rely on complex algorithms and sensors to navigate roads. While these technologies promise to enhance safety by eliminating human error, they also introduce new challenges. Unforeseen scenarios, such as unexpected road conditions or obstacles, could lead to accidents. Determining liability in such cases becomes complex. Who....

A study conducted by Guo et al. (2018) investigated the performance of the ARIMAX-ANN hybrid forecasting model in predicting rice yield compared to the traditional ARIMAX and Artificial Neural Network (ANN) models. The researchers found that the ARIMAX-ANN hybrid model outperformed both the ARIMAX and ANN models in terms of forecasting accuracy and efficiency. The hybrid model was able to capture the non-linear relationships and complex patterns in the rice yield data more effectively, resulting in more accurate predictions.
Similarly, a study by Wang and Ding (2019) compared the performance of various time series forecasting models, including ARIMAX, ANN, and the....