Workers’ Compensation (WC) actuarial model workbook. Payroll data for the WC model should contain “only the actual hours worked” for specific Rate Schedule Codes (RSC) groups‚ including executives. The WC payroll data should exclude all paid leave types. A comparison of work hours from the NPHRS mainframe report to the summary in EDW reveals very small differences. We hope to align the NPHRS and EDW work hour data. Also‚ we (Technical Analysis‚ Accounting and Finance) need to understand and articulate
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Presentation and Data Analysis 1.) What product do you use? -Majority of the respondents in different year level choose “SMART” almost 80% of them prefer to use this product‚ while 18% of the respondents choose “GLOBE” and 2% selected “OTHERS”. 2.) How frequently do you purchase product from Smart load? -Majority of the respondents choose “SPECIFY” in this question on how frequently they purchase Smart product‚ 25% of the students selected “EVERYDAY”‚
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process. As Gilvan C Souza mentioned‚1 the speed to market of new products need to match that of the industry category so the company can be successful. 1. What are the competitive challenges of the automotive industry in 1997 and beyond? How is BMW affected? In general‚ in the last decade the market has witnessed a power shift from manufacturers to consumers. The automotive industry did not escape this trend. Customers wanted to have more variety of cars‚ and more affordable ones without
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University CS 450 Data Mining‚ Fall 2014 Take-Home Test N#1 Date: September 22nd‚ 2014 Final deadline for submission September 29th‚ 2014 Weighting: 5% Total number of points: 100 Instructions: 1. Attempt all questions. 2. This is an individual test. No collaboration is permitted for assessment items. All submitted materials must be a result of your own work. Part I Question 1 [20 points] Discuss whether or not each of the following activities is a data mining task.
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involvement. ____ 3. The work breakdown structure (WBS) is key to a successful project. ____ 4. Gantt charts become useless once the project begins. ____ 5. Project feasibility analysis is an activity that verifies whether a project can be started and successfully completed. ____ 6. Feasibility analysis essentially identifies all the risks of failure. ____ 7. Current trends indicate that iterative‚ evolutionary approaches help to improve project success. ____ 8. Economic feasibility
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Data Mining Weekly Assignment 6: LIFT; CRM; AFFINITY POSITIONING; CROSS-SELLING AND ITS ETHICAL CONCERNS. What is meant by the term “lift”? The term “lift” describes the improved performance of an exact or specific amount of effort on a modeled sampling‚ as opposed to a random sampling (Spang‚ 2010). In other words‚ if you are able to market via a model to say‚ a given number of random customers (e.g. 1000)‚ and we expect that 50 of them would be successful‚ then a model that can generate 75
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an era of big data‚ this data-driven world has the potential to improve the efficiencies of enterprises and improve the quality of our lives; however‚ there are a number of challenges that must be addressed to allow us to exploit the full potential of big data. This paper focuses on challenges faced by online retailers when making use of big data. With the provided examples of online retailers Amazon and eBay‚ this paper addressed the key challenges of big data analytics including data capture and
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Data Mining Project – Dogs Race Prediction Motivation Gambling is very popular in the Republic of Ireland‚ weather is online or not‚ more people are joining gambling communities formed all over the Island of Ireland. The majority of these communities are involved in horse races related gambling and other sports‚ but there is a significant amount of people dedicated to dogs races. This is a multimillion Euro industry developed on-line and live or face to face. Objective There are many websites
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IT433 Data Warehousing and Data Mining — Data Preprocessing — 1 Data Preprocessing • Why preprocess the data? • Descriptive data summarization • Data cleaning • Data integration and transformation • Data reduction • Discretization and concept hierarchy generation • Summary 2 Why Data Preprocessing? • Data in the real world is dirty – incomplete: lacking attribute values‚ lacking certain attributes of interest‚ or containing only aggregate data • e.g.‚ occupation=“ ”
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Secondary Data Analysis-Literature Review In the article “Violence‚ Older Peers‚ and the Socialization of Adolescent Boys in Disadvantage Neighborhoods” David J. Harding stated that “most theoretical perspectives on neighborhood effects on youth assume that neighborhood context serves as a source of socialization‚ but the exact sources and processes underlying adolescent socialization in disadvantaged neighborhoods are largely unspecified and unelaborated”. What Harding is saying is that most adolescent
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