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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.

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Presentation on theme: "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."— Presentation transcript:

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 MonthTONSMILESSTOPSREG HRS OT HRS Jun May Apr Mar Feb Jan Dec Nov Oct Sep Aug Jul

6 Exhibit 4 Williams Miles(W)Stops(W)Hours(W)

7 Exhibit 4 Spencer Miles(S)Stops(S)Hours(S)

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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