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CBC The Use of Probability and Statistics in Electrical Engineering Essay

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The Importance of Probability and Statistics in Engineering and Computer Science The Importance of Probability and Statistics in Engineering and Computer Science Sheila Gobes-Ryan, PhD, University of South Florida January 2, 2022 Increasing Role You are entering your professional lives at a time when how we do business is radically changing because of technology, and the data collection and analysis that it enables. The Global Datasphere: all the data created, captured, or replicated; is predicted to grow to 175 zettabytes (ZB) by 2025 (Reinsel, Gantz & Rydning, 2018). (A zettabyte equals 1,0007 bytes (Desjardins, 2019).) To put this growth in perspective, the Global Datasphere was 33 zettabytes in 2018 (Reinsel, Gantz & Rydning, 2018). For those that know how to effectively find, evaluate, use and communicate about this data, the ability to address problems successfully will be radically increased. This ability is referred to as data literacy, which is defined as “The ability to read, work with, analyze, and argue with data as part of a larger inquiry process” (D’Ignazio & Bhargava, 2016, p. 84). Learning how to make data professionally valuable is a large part of any probability and statistics course. The words we use – Connecting Probability and Statistics with data and data literacy To use probability and statistics in your professional life you will need to understand how data can be used to help address the problems or projects you will work on, where to find reliable data, and how to effectively present what you have done to your coworkers and bosses, other professionals, clients, and stakeholders. Professionals who can work with data in this way are considered ‘data literate’, and this skill is becoming increasingly important in engineering and computer science, as well as many other fields. Where are we getting data? Data is routinely collected in many facets of daily life by the systems we regularly interface with. This means that as you enter your professional lives, it will be important to understand how data is collected, who manages or owns the data, what data your organization and your client organizations have, what data is publicly available, and what data you can purchase. US government data is collected and made available through many different agencies including NASA, Environmental Protection Agency, National Geologic Survey, Bureau of Labor Statistics, and Department of Transportation. Categories in which data are collected that are relevant to the fields you will enter include national defense, foreign aid, energy and environment, trade, transportation, health, and climate. To get a quick start on the types of data available you can start with the USA Facts website that provides basic data visualizations with links to more detailed data sets. Many countries, regions, and international governmental organization have similar resources including Eurostat, Statistics Bureau of Japan, and OECD data. Gobes-Ryan 1/2022 1 The Importance of Probability and Statistics in Engineering and Computer Science Other sources There are increasing numbers of non-governmental organizations that assemble and make data available for use. A few examples are Data World, Gapminder, and Climate Trace. (Gapminder also makes available excellent data visualization tools.) Beyond these organizations are businesses specializing in collecting, assembling, and selling data. Phases of Work Problem Definition When a client comes to you with a problem or project, developing a suitable solution involves making sure you understand the specific issue you are being asked to address. Obtaining data surrounding the issue can go a long way to making the specifics of what is currently happening clear, and offer means to predict future trends. Examples Traffic data is collected on a wide range of traffic parameters which are analyzed using statistical (Abdel-Rahim, n.d.) and probability (Mugdha, n.d.) tools to aid in setting signal controls or in expansion planning of roads and bridges. Prediction of workforce demand is vital to keep many of our daily systems working smoothly. Several areas include supply chain planning, hospital staffing, retail staffing, and TSA staffing at the airport (Sefair, 2021). Health tracking devices are used to collect data to understand health issues in more detail. One example of this is the ‘Smart Bra’ to collect data to predict the indications of heart problems in women (Weintraub, 2018). Design Phase Evaluation As products and solutions are developed, testing what is being developed is crucial. For some products, this can involve understanding customer needs and preferences through user testing and analyzing it statistically. For others, this is accomplished by testing samples of what is in development to understand how it would perform with a larger population. Here probability can be used for evaluation. Finally, we can assemble and analyze numerous data sets to predict product performance across multiple characteristics. Examples Conjoint analysis of customer preferences: In this process, an experiment is designed to test several modifiable product characteristics. A set of potential customers are identified to test a product and respond to different product characteristics. Hahn and Doganaksoy (2008), discuss this approach being used to evaluate characteristics of a new bar soap including weight, size, shape, color, surface feel, type of fragrance, and intensity of fragrance. Energy consumption models for buildings: These are based on multiple data inputs from past conditions influencing a building’s energy use to predict the performance of a building from a digital model (Catalina, Virgone, & Iordache, 2011). These data include wind direction, wind speed, temperature, and orientation to the sun. Gobes-Ryan 1/2022 2 The Importance of Probability and Statistics in Engineering and Computer Science Process Tracking and Prediction Devices collect data on their own operation and the people and processes they are involved with. The volume and scope of data collected allows for specific and detailed analyses that were not possible a generation ago. These data are used for predictive maintenance, predictive monitoring, and predictive analytics, enabling increased efficiency and process optimization. Example Nuclear power plant safety, security, and safeguards. The need for security at nuclear power facilities and the data generated from their operational processes afford unique challenges balancing security, risk, and complex processes. To address these challenges, government and research organizations are developing statistical methods for such facilities so that only data needed for operational and predictive maintenance are accessible (Sandia National Laboratories, 2018). Product Performance Product performance is assessed to assure that a product meets performance expectations set by the manufacturer, customers, or standards organizations (i.e., ASTM, NFPA, ISO). Example Software reliability growth models. These inferential statistical tools are used in the early testing of software to describe an application’s failure in terms of its internal structure to assess its reliability (Febrero, Calero, & Moraga, 2014). Business Evaluation Collection and evaluation of statistical data are foundational for businesses and their leadership. Data allows organizations to evaluate market demand, details of business performance, and identify problems, as well as to provide predictive analytics for decision making on issues such as business location (Richards, n.d.a, Richards, n.d.b) Example Profitability. In the article “The True Measure of Success,” Michael Mauboussin (2012) describes the importance of knowing profitability by customer. He discusses how he discovered that his organization’s largest customers were the least profitable, while their mid-sized customers were less demanding and more profitable. Software & Data Visualizations Probability is an essential foundation creating algorithms that allow two possibilities in software. The first is that probabilistic algorithms make random choices possible in computing. The second, the probabilistic analysis of algorithms, incorporates randomness into the data processed by an algorithm (National Research Council, 1992). Statistics and probability are used to create data visualizations essential for representing data in straightforward and understandable ways. This approach, when used well, tells stories with data that would not be clear or accessible in other formats. In addition, probability allows businesses to use data to reduce future uncertainty through scenario analysis, sales forecasting, risk management, and budget forecasting. Gobes-Ryan 1/2022 3 The Importance of Probability and Statistics in Engineering and Computer Science Examples Communication protocols introduce randomness for multiple packets of information using the same communication channels, allowing them to avoid being in the same place at the same time in the communication system (National Research Council, 1992). Pandemic data. Johns Hopkins created the widely used COVID-19 dashboard to visualize current COVID-19 data in several different ways globally and by country (Johns Hopkins Coronavirus Resource Center, 2022) This presentation made it possible for many people to easily access and understand massive amounts of data on COVID-19 numbers and distributions. References Abdel-Rahim, A. (n.d.) Models of traffic flow. University of Idaho. https://www.webpages.uidaho.edu/ahmed/ce322/class%20notes/class%2015%20_traffic_ stream_parameters.pdf Catalina, T., Virgone, J., & Iordache, V. (2011, November 14-16). Study on the impact of the building form on energy consumption [Conference paper]. Proceedings of Building Simulation 2011. pp. 1726-1729. 12th Conference of International Building Performance Simulation Association, Sydney, Australia. Desjardins, J. (2019, April 17). How much data is generated each day? World Economic Forum. https://www.weforum.org/agenda/2019/04/how-much-data-is-generated-each-daycf4bddf29f/ D’Ignazio, C., & Bhargava, R. (2016). DataBasic: design principles, tools and activities for data literacy learners. The Journal of Community Informatics 12(3) 83-107. Febrero, F., Calero, C., & Maraga, M. Á. (2014). A systematic mapping study of software reliability modeling. Information and Software Technology 35 839-849. Doi: 10.1016/j.infsof.2014.03.006 Hahn, G. J., & Doganaksoy, N. (2008). The role of statistics in business and industry. [ebook] John Wiley & Sons. http://ebookcentral.proquest.com/lib/usf/detail.action?docID=819142 Johns Hopkins Coronavirus Resource Center (2022). COVID-19 Dashboard [Data Visualization]. https://coronavirus.jhu.edu/map.html Mauboussin, M. J. (2012, October) The true measure of success. Harvard Business Review. https://hbr.org/2012/10/the-true-measures-of-success National Research Council (1992). Probability and Algorithms. Washington, DC: The National Academies Press. https://doi.org/10.17226/2026. Reinsel, D., Gantz, J., & Rydning, J. (2018, November). The digitization of the world: From edge to core [White Paper]. International Data Corporation. https://www.seagate.com/files/www-content/our-story/trends/files/idc-seagate-dataagewhitepaper.pdf Richards, L. (n.d.a). The role of probability distribution in business management. The Houston Chronical Chron website, Small Business. https://smallbusiness.chron.com/roleprobability-distribution-business-management-26268.html Richards, L. (n.d.b) How confidence interval affects business. The Houston Chronical Chron website, Small Business. https://smallbusiness.chron.com/role-probability-distributionbusiness-management-26268.html Gobes-Ryan 1/2022 4 The Importance of Probability and Statistics in Engineering and Computer Science Sandia National Laboratories. (2018). Industrial internet-of-things & data analytics for nuclear power & safeguards. https://www.osti.gov/servlets/purl/1481947 Sefair, J. A. (2021, December 6). Dynamic workforce3 allocation to improve airport screening operations [Faculty Candidate Presentation]. University of South Florida, Industrial and Management Systems Engineering Weintraub, K. (2018, June 27). This bra could save lives: An MIT startup reimagines the bra as a heart monitor–and aims to fill the data gap for women’s heart disease. The MIT Technology Review. https://www.technologyreview.com/2018/06/27/240494/this-bracould-save-lives/ Gobes-Ryan 1/2022 5 Name: Element 1: Introduction (including thesis statement) Thesis Statement Restated Element 2: Use of Statistics in Source 1: In-text citation Reference Listing for this source How the article informs the argument you will be making. How you will use the ideas from the article to support your argument. Source 2: In-text citation Reference Listing for this source How the article informs the argument you will be making. How you will use the ideas from the article to support your argument. Source 3: In-text citation Reference Listing for this source How the article informs the argument you will be making. How you will use the ideas from the article to support your argument. Element 3: Probability and Statistics Literacy will Enhance Your Employability Source 1 In-text citation Reference Listing for this source What data from each document you will use to support your argument? You must use numerical data from both documents. The data must be presented in a way that a reader will understand what it means without referencing the two web pages you are using. Do not use any statistical data that is not relevant to your argument How you will use the numerical data to support your argument. Source 2 In-text citation Reference Listing for this source What data from each document you will use to support your argument? You must use numerical data from both documents. The data must be presented in a way that a reader will understand what it means without referencing the two web pages you are using. Do not use any statistical data that is not relevant to your argument How you will use the numerical data to support your argument. Source 3 In-text citation Reference Listing for this source Which competency or competencies do you think you will enhance in probability and statistics? How you will incorporate the definitions into your work. Element 4: Reference List
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