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An Analysis of ABC Auction House. Demetria Henderson Regression Final Project Department of Management December 4, 2012. Agenda. Background. Purpose. Variables. Statistical Analysis Methods. All Possible Regressions. Results of Initial Model. Results of Final Model. - PowerPoint PPT Presentation
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An Analysis of ABC Auction House
Demetria HendersonRegression Final Project
Department of ManagementDecember 4, 2012
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AgendaBackground
Purpose
Multiple Regression Variables
Statistical Analysis Methods
Results
Questions
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BackgroundABC Auctions
• Wholesale vehicle auction in business for over 50 years.
• Over 20,000 employees in 100 locations worldwide.
• In North America, 75 auctions are in operation.• Handles approximately 500,000 vehicles.• Has approximately 11,000 full-time employees.• Does business with over 75,000 auto dealers.
• Generates annual revenues of a billion dollars.
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PurposeTo determine which
variables; performance, employee statistics, and safety variables, predict the profitability of ABC
Auctions by using multiple regression
analysis.
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VariablesObservations• N = 75 (record for each NA location)
Dependent Variable• Profitability (Earnings as percentage of revenue)
Independent Variables• Legacy• Full-time turnover rate• Part-time turnover rate• Number of cars sold• Workman’s comp claims/10,000 cars• Lot damage per consigned cars• Occupational and Safety Health Administration (OSHA)
accidents/10,000 cars
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Statistical Analysis MethodsAll Possible Regressions Report
Multiple Regression Analysis
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All Possible Regressions4 0.573452 8.039024 3.393803 ACFG 4 0.534053 8.402098 9.711158 ACEG 4 0.524857 8.484607 11.185708 ACDG 4 0.521444 8.515019 11.732858 ABCG 4 0.503544 8.672813 14.603107 AEFG 4 0.500734 8.697324 15.053679 ADFG 4 0.489046 8.798533 16.927683 ABFG 4 0.466447 8.991002 20.551236 ABDG 4 0.466405 8.991357 20.557976 ADEG 4 0.456464 9.074724 22.151919 ABEG
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Results of Initial Model4 Variables Selected• Sold, lotdampercar, ohsapercar,ftturn
Summary• R2 = .5735• Data was normal• No multicollinearity• No apparent issues with dependence or equal
variance• I.V. statisticially significant except for number of
cars sold• Transformation of number of cars sold may be
required
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Results of Final Model
4 Variables Selected
• Inverse(Sold), lotdampercar, ohsapercar, ftturn
Summary
• R2 = .5968• Data was normal• No multicollinearity• All I.V. statisticially significant• No apparent issues with dependence or equal variance
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Results: Final ModelProfitability
51.27
– .13*ftturn
– 82079.59*inverse_sold
– 11.47*lotdampercar
– 1.11*oshapercar
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Summary of Results
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Questions