Question1 Quantitative data are measures of values or counts and are expressed as numbers (www.abs.gov.au). In other words‚ quantitative data are data about numeric variables (www.abs.gov.au). Four types of quantitative data are interval‚ nominal‚ ordinal and ratio. Firstly‚ interval scales are numeric scales in which we know not only the order‚ but also the exact differences between the values (www.mymarketresearchmethods.com). Other than that‚ interval data also sometimes called integer is measured
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Analyzing Data IT206 September 2‚ 2011 Don Shultz Analyzing Data The five basic steps that are required for analyzing data using Microsoft Access according to the article by Samuels and Wood (2007. The steps are to gather the data‚ create a database‚ edit and validate data‚ connect data files‚ and perform queries. The first step is to determine exactly what analyzes you want to perform and ensure that you gather all that is needed. Keep in mind to import into Access it has to be formatted
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Data mining and warehousing and its importance in the organization Data Mining Data mining is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue‚ cuts costs‚ or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles‚ categorize it‚ and summarize the relationships identified. Technically‚ data
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Type I and II errors Mistakenly rejecting the null hypothesis is a type 1 error. These errors are not avoidable and are part of statistical testing‚ but we can lessen the occurrence by setting the significance at a lower level. However‚ by setting the significance level lower; let us say .001‚ we then increase the chance of type 2 errors. Failing to correctly reject the null hypothesis creates a type 2 error‚ this is because; according to Aron (2009) “with an extreme significance level like
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Analyzing and Interpreting Data Team “A” Mona Anderson‚ Jeanine Camacho‚ Mary Hopkins QNT/351 April 25‚ 2013 John Carroll Analyzing and Interpreting Data Our team has collected‚ summarized‚ and interpreted data from the employee survey during the third and fourth weeks of investigation. Findings from the first survey reported that a second survey will be pre-tested before distribution to the employees to ensure effectiveness. The survey questions will be written differently or
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DATA PROVENANCE IN E-LEARNING ENVIRONMENT M.S.A. Ahsan 100022U A. Burusothman 100063U S. Paraneetharan 100369M B. Sanjith 100484K K. Sureshkumar 100527X 1. Introduction We live in an information age‚ where the volume of data processed by humans and organizations increases exponentially by grid middleware and availability of huge storage capacity. So‚ Data management comprises all the disciplines related to managing data as a valuable resource.
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How Data Mining‚ Data Warehousing and On-line Transactional Databases are helping solve the Data Management predicament. Robert Bialczak Walden University How Data Mining‚ Data Warehousing and On-line Transactional Databases are helping solve the Information Management predicament. Data in itself can be powerful‚ but also has many pitfalls if left to disparate databases and data collection routines. A collection of spreadsheets with account numbers entered into them can be view as a business
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Learning and Data Mining Overview: Efficient asset allocation through statistical learning methods and comparison of methods for the creation of an index tracking ETF (Exchange traded fund) Datasets: The datasets are chosen from the website of the book “Statistics and Data Analysis for Financial Engineering” by David Ruppert. The book is mentioned as one of the references for this course. The two data sets chosen are 1. Stock_FX_Bond.csv 2. Stock_FX_Bond_2004_to_2006.csv The data includes
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CRS Web Data Mining: An Overview Updated December 16‚ 2004 Jeffrey W. Seifert Analyst in Information Science and Technology Policy Resources‚ Science‚ and Industry Division Congressional Research Service ˜ The Library of Congress Data Mining: An Overview Summary Data mining is emerging as one of the key features of many homeland security initiatives. Often used as a means for detecting fraud‚ assessing risk‚ and product retailing‚ data mining involves the use of data analysis tools
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3 Different Types of Sport Fans Everybody enjoys going to a sporting event. Whether the sporting event is Football‚ Baseball‚ Basketball‚ or even Hockey. Sporting events bring excitement to all of us‚ thinking that our team will win. If our team wins‚ we usually go home happy. Then if our team goes home losing‚ we get upset or sad inside. Anyways‚ we will always cheer for our team no matter what especially at the game. There are always those fans though that bugs people to death. Anytime at a
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