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Class 24

Class 24. Case: Edgcomb Metals. Edgcomb Metals. 21 service centers doing $500 million in sales Supplied by large steel companies 35,000 customers (any firm using some kind of steel) Services include cutting, shaping, and daily delivery. Troy Plant.

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Class 24

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  1. Class 24 Case: Edgcomb Metals

  2. Edgcomb Metals • 21 service centers doing $500 million in sales • Supplied by large steel companies • 35,000 customers (any firm using some kind of steel) • Services include cutting, shaping, and daily delivery.

  3. Troy Plant • 72K plant serving customers in Virginia • Seven trucks, eight trailers • Seven drivers • $9.50 and hour with 50% for overtime • Customized uniforms with American flag patch • Daily deliveries to seven “sectors” • Product for each “run” was loaded at the plant each morning in “optimum” order. • Customers unload. • Drivers assigned “randomly” to sectors.

  4. Spencer versus Williams • Spencer spoke up at the most recent drivers’ meeting: • We get paid by the hour with time and a half for overtime. • Some of us hustle throughout the day, finish early, and help in the shop. • Some of us don’t hustle and end up with overtime as a result. • I want to work hard…..and it’s not right that others get rewarded for NOT working hard.

  5. Exhibit 2

  6. Exhibit 4 Williams

  7. Exhibit 4 Spencer

  8. Notes

  9. Task A • Calculate summary statistics for both the Williams and Spencer Data. • Be prepared to present and comment BRIEFLY on the results.

  10. Task B • Test the hypothesis that mean hours is equal for S and W. • Formulate you own alternative hypothesis • Do not use regression • Be prepared to report and interpret the results.

  11. Task C • Combine (Stack) the Williams and Spencer Data. • Create a dummy variable which designates driver • Regress hours on the dummy variable. • Be prepared to interpret the results and to test the statistical significance of the results.

  12. Task D • Test the hypothesis that Spencer’s mean miles (per run) is equal to Williams’ mean miles. • Ha: mean miles for Spencer is greater than mean miles for Williams. • Be prepared to interpret the results.

  13. Task E • Test the hypothesis that Spencer’s mean Stops (per run) is equal to Williams’ mean Stops. • Ha: mean Stops for Spencer is greater than mean Stops for Williams. • Be prepared to interpret the results.

  14. Task F • For the Williams Data • Regress Hours on both Miles and Stops. • Be prepared to report and interpret the results. • For the Spencer Data • Regress Hours on both Miles and Stops. • Be prepared to report and interpret the results. • Based on your comparison of the two models, who is the better driver?

  15. Task G • Combine (Stack) the Williams and Spencer Data. • Create a dummy variable which designates driver. • Regress hours on the dummy variable, Miles, and Stops. • A multiple regression with three X variables. • Be prepared to interpret the results and to test the statistical significance of the results.

  16. Edgcomb Metals What Happened

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