behavior on an L2 video listening test. Thirty-six test-takers were videotaped while taking a listening test composed of six separate video texts‚ and the amount of time test-takers made eye contact with the video monitor was computed. An analysis of the data indicated that the group of participants oriented to the video monitor 69% of the time while the video text was played. In addition‚ the study yielded valuable information concerning the consistency of the test-takers’ viewing behavior. INTRODUCTION
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Secondary data refers to the data which an investigator does not collect himself for his purpose rather he obtains them from some other source‚ agency or office. In other words‚ this data has already been collected by some other source and an investigator makes use of it for his purpose. Secondary data is different from primary data on the basis of the sources of their collection. The difference between the two is relative - data which is primary at one place become secondary at another place.
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240 - INTRODUCTION TO DATA PROCESSING/MANGEMENT OF INFORMATION TECHNOLOGY Objectives To give a thorough and up-to-date grounding in the realities of commercial applications of Information and Communication Technologies (ICT’s). To Examine the roles of data‚ information and knowledge within modern business organisations‚ and the roles that information and communication technologies (ICT’s) play in supporting people and groups within organisations. To provide students with a thorough understanding
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|Case Study: Data for Sale | |Management Information System | | | |
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Introduction and overview 1.1 Coverage 1.1 Health consequences of tobacco consumption 2. The impact of price on the demand for tobacco products 2.1 Conventional studies of cigarette demand 2.1.1 Analysis of aggregate data 2.1.2 Analysis of individual level data 2.2 Addiction models and cigarette demand 2.2.1 Imperfectly rational addiction models 2.2.2 Myopic addiction models 2.2.3 Rational addiction models 2.2.4 Critiques of the rational addiction model 2.3 Behavioral economic
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URINALYSIS 1. What are the possible explanations for the abnormal findings seen in Unknown A? Sample A had large ketones and a mild amount of protein. It also tested positive for a notable amount of blood but it fell within normal limits. There are several scenarios that might explain these results. As for the ketones‚ the patient could have Diabetes Mellitus‚ they could be suffering from starvation or they could be on a low carbohydrate or very high-fat diet. A mild amount of protein in the urine
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Data Representation Data and instructions that are presented in a written or typed format can only be understood by the user. If the data is not in the user’s language‚ s/he will not be able to understand it. It is the same way with the computer; the computer’s language is binary 0s and 1s. The computer cannot understand typed or written instructions or data. Whenever data or instructions or input to the computer it is first converted to 0s and 1s‚ these are called binary digits (bits). There
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in intermediate examination is linked with students’ outline consisted of his approach towards communication‚ learning facilities‚ proper guidance and family stress. The research is based on student profile developed on the bases of information and data collected through assessment from students of a group of private colleges. Abstract Keywords : Students performance‚ communication‚ learning facilities‚ proper guidance and family stress. GJMBR-A Classification : FOR Code: 140204 JEL Code: I21
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Enhancing Customer Data Enhanced Customer Data Repository is a secure and fully supported data repository with problem determination tools and functions. It updates problem management records (PMR) and maintains full data life cycle management. · combination of all the internal structured business data (CRM‚ ERP‚ POS and all the internal system data) and external unstructured data ( Social media data‚ feedback surveys‚ Audios‚ Videos‚ streaming data‚ Call center data‚ images) · unmanageable volumes
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into the 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
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