2012-2013 PAWNSHOPS AND BEHAVIORAL ECONOMICS 193 PAWNSHOPS, BEHAVIORAL ECONOMICS, AND SELF-REGULATION SUSAN PAYNE CARTER* AND PAIGE MARTA SKIBA** I. Introduction Pawnbroking is the oldest source of credit.1 There is growing public interest in day-to-day pawnbroking operations, as evidenced by the popularity of reality shows such as “Pawn Stars” and “Hardcore Pawn.”2 Television viewers’ curiosity about an old credit institution may be due to the fact that 7% of all U.S. households have
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Probability & Statistics for Engineers & Scientists This page intentionally left blank Probability & Statistics for Engineers & Scientists NINTH EDITION Ronald E. Walpole Roanoke College Raymond H. Myers Virginia Tech Sharon L. Myers Radford University Keying Ye University of Texas at San Antonio Prentice Hall Editor in Chief: Deirdre Lynch Acquisitions Editor: Christopher Cummings Executive Content Editor: Christine O’Brien Associate Editor: Christina Lepre Senior
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2012-2013 PAWNSHOPS AND BEHAVIORAL ECONOMICS 193 PAWNSHOPS, BEHAVIORAL ECONOMICS, AND SELF-REGULATION SUSAN PAYNE CARTER* AND PAIGE MARTA SKIBA** I. Introduction Pawnbroking is the oldest source of credit.1 There is growing public interest in day-to-day pawnbroking operations, as evidenced by the popularity of reality shows such as “Pawn Stars” and “Hardcore Pawn.”2 Television viewers’ curiosity about an old credit institution may be due to the fact that 7% of all U.S. households have
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The Quality of Data Introduction Value Errors Missing Data and Bad Structures Entity Resolution Anonymous Resolution Conclusion 34 35 39 50 62 66 1 DATA MINING FOR INTELLIGENCE Introduction Computer processors are faster than ever, storage is fairly cheap, network bandwidth is continually expanding, and information technologies are capable of integrating massive amounts of data. With all of these high-end systems and capabilities, there is still a limitation on performing
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HR PRACTICES OF WIPRO STUDENT UNDERTAKING This is to certify that we have completed the Project titled “H R P r a c t i c e s o f W I P R O ” under the guidance of Prof Sana Danani in partial fulfillment of the requirement for the award of degree of Bachelor of Management Studies at Rizvi College of Arts, Seience & commerce. This is an original piece of work & we have not submitted it earlier elsewhere. ROLL NO. 105 86 100 89 71 NAME: Jyoti Singh Atul kumar Pandey Muzaffar Shaikh Asim Qureshi
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Math Review for the Quantitative Reasoning Measure of the GRE® revised General Test www.ets.org Overview This Math Review will familiarize you with the mathematical skills and concepts that are important to understand in order to solve problems and to reason quantitatively on the Quantitative Reasoning measure of the GRE revised General Test. The following material includes many definitions, properties, and examples, as well as a set of exercises (with answers) at the end of each review
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MAT 1348B Discrete Mathematics for Computer Science Winter 2011 Professor: Alex Hoffnung Dept. of Mathematics & Statistics, 585 King Edward (204B) email: hoffnung@uottawa.ca Important: Please include MAT1348 in the subject line of every email you send me. Otherwise your email may be deleted unread. Please do not use Virtual Campus to send me messages as I may not check them regularly. Course Webpages: This web page will contain detailed and up-to-date information on the course, including
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JSLHR Research Article Lexical Effects on Children’s Speech Processing: Individual Differences Reflected in the Autism-Spectrum Quotient (AQ) Mitsuhiko Ota,a Mary E. Stewart,b Alexandra M. Petrou,b and Catherine Dickiec Purpose: This study was undertaken to examine whether children exhibit the same relationship that adults show between lexical influence on phoneme identification and individual variation on the Autism-Spectrum Quotient (AQ). Method: Data from 62 4- to 7-year-olds
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STARBUCKS Case Study Alex Cochran May 2003 Starbucks – A Case Study. Table of Contents Alex Cochran 2003 Executive Summary..................................................................................................................................3 Market Segmentation ...............................................................................................................................4 Methods of Segmentation............................................................
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Once the proud capital of Germany Berlin was divided by a barrier that was patrolled day and night by armed soldiers and guard dogs. On August 13, 1961 shortly after midnight police and soldiers in the Communist controlled Berlin moved quickly to set up barriers. Berliners woke to find their city divided into east and west sectors. A communist nation led by the Soviet Union was in control of East Berlin. While West Berlin was controlled by a democratic nation led by the United States (Epler,
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