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Here's another one John....

Here's another one John. I need this one by March 31st explained in great detail in Excel. Like the last one you helped me with. Thanks.

Note: In column B, 1=Atlanta, 2=Boston, 3=Cleveland, and 4=Denver

Week Warehouse Cost Orders

1 1NNN-NN-NNNN

1 2 38.96 655

1 3 67.02 812

1 4 115.43 689

2 1 82.99 1016

2 2 76.43 887

2 3 95.29 1302

2 4 149.18 839

3 1 56.23 1000

3 2 33.69 552

3 3 55.35 630

3 4 106.42 689

4 1 41.40 599

4 2 38.73 626

4 3 95.47 1085

4 4NNN-NN-NNNN

5 1 62.29 738

5 2 86.94 1017

5 3 34. 71 406

5 4 90.49 814

6 1 42.58 668

6 2 67.82 868

6 3 33.95 559

6 4NNN-NN-NNNN

7 1 76.67 959

7 2 44.21 677

7 3 41.40 619

7 4 99.03 761

8 1 85.16 1063

8 2 38.80 583

8 3 79.95 853

8 4NNN-NN-NNNN

9 1NNN-NN-NNNN

9 2NNN-NN-NNNN

9 3 61.82 911

9 4 55.46 429

10 1 92.19 1294

10 2NNN-NN-NNNN

10 3NNN-NN-NNNN

10 4 93.15 893

11 1 60.35 934

11 2NNN-NN-NNNN

11 3 76.29 1011

11 4NNN-NN-NNNN

12 1 62.53 827

12 2 84.88 1135

12 3 95.21 1355

12 4NNN-NN-NNNN

13 1 90.40 1227

13 2NNN-NN-NNNN

13 3NNN-NN-NNNN

13 4NNN-NN-NNNN

14 1 64.48 895

14 2 38.46 517

14 3 36.15 592

14 4 104.85 916

15 1 95.48 1412

15 2 41.99 527

15 3 33.24 468

15 4 64.64 921

16 1 33.66 408

16 2 65.66 758

16 3 40.57 497

16 4 90.14 629

17 1 92.95 1039

17 2NNN-NN-NNNN

17 3 58.19 726

17 4 96.62 886

18 1 30.64 418

18 2 74.19 945

18 3 40.00 446

18 4NNN-NN-NNNN

19 1 86.78 1331

19 2 30.03 438

19 3 43.56 457

19 4NNN-NN-NNNN

20 1 37.65 476

20 2 91.30 1128

20 3 57.96 755

20 4NNN-NN-NNNN

21 1 30.37 400

21 2 48.91 480

21 3 77.39 914

21 4 93.49 1225

22 1NNN-NN-NNNN

22 2 85.96 1258

22 3NNN-NN-NNNN

22 4 75.58 559

23 1 49.17 528

23 2 65.01 966

23 3 46.43 563

23 4 89.07 1251

24 1NNN-NN-NNNN

24 2 25.36 384

24 3 46.83 641

24 4 111.23 854

25 1 33.73 451

25 2 71.81 1233

25 3 84.99 1044

25 4 63.22 541

26 1NNN-NN-NNNN

26 2 68.36 1070

26 3NNN-NN-NNNN

26 4 95.36 1135

Problem 4.1

"The first issue your boss has asked you to address is whether or not there are differences in distribution cost between each of the four warehouses. Use the 0.05 level of significance to:

a) Perform a one-way ANOVA to look for differences in distribution costs between warehouses.

b) If the results in (a) indicate that it is appropriate, use the Tukey-Kramer procedure to determine which distribution centers differ in mean distribution costs.

c) Briefly summarize (in plain English) your procedures and the results of (a) and (b) for your manager.

Problem 4.2

"In addition to looking at differences between distribution centers, your manager also wants to know the relationship between the number of orders routed through each center and the distribution cost. Thus, the number of orders is your independent variable and the cost is your dependent variable.

a)Construct a scatter plot of the two variables.

b)Estimate a simple linear regression between these two variables.

c)Interpret the meaning of β0 and β1.

d)Predict the mean distribution costs of 500 orders, 1000 orders, and 1500 orders. Are these appropriate predictions?

e)Comment briefly on the predictive power/statistical significance of your estimates. "

Problem 4.3

Here, your task is to combine 4.1 and 4.2 into a single multiple regression model using dummy variables. To put the data in an Excel-friendly format, you will first need to create dummy variables for each of the different processing centers. a) Estimate a multiple regression model, again using cost as the dependent variable, however for your independent variables you will want to use orders AND your set of dummy variables (see technical note below).

b) Comment on the results from (a) in light of your results in 4.1 and 4.2. Does the coefficient on orders here match your results from 4.2? Do the coefficients on your dummy variables, and differences between the coefficients on your dummy variables, match up with your results from 4.1?

Note: In column B, 1=Atlanta, 2=Boston, 3=Cleveland, and 4=Denver

Week Warehouse Cost Orders

1 1NNN-NN-NNNN

1 2 38.96 655

1 3 67.02 812

1 4 115.43 689

2 1 82.99 1016

2 2 76.43 887

2 3 95.29 1302

2 4 149.18 839

3 1 56.23 1000

3 2 33.69 552

3 3 55.35 630

3 4 106.42 689

4 1 41.40 599

4 2 38.73 626

4 3 95.47 1085

4 4NNN-NN-NNNN

5 1 62.29 738

5 2 86.94 1017

5 3 34. 71 406

5 4 90.49 814

6 1 42.58 668

6 2 67.82 868

6 3 33.95 559

6 4NNN-NN-NNNN

7 1 76.67 959

7 2 44.21 677

7 3 41.40 619

7 4 99.03 761

8 1 85.16 1063

8 2 38.80 583

8 3 79.95 853

8 4NNN-NN-NNNN

9 1NNN-NN-NNNN

9 2NNN-NN-NNNN

9 3 61.82 911

9 4 55.46 429

10 1 92.19 1294

10 2NNN-NN-NNNN

10 3NNN-NN-NNNN

10 4 93.15 893

11 1 60.35 934

11 2NNN-NN-NNNN

11 3 76.29 1011

11 4NNN-NN-NNNN

12 1 62.53 827

12 2 84.88 1135

12 3 95.21 1355

12 4NNN-NN-NNNN

13 1 90.40 1227

13 2NNN-NN-NNNN

13 3NNN-NN-NNNN

13 4NNN-NN-NNNN

14 1 64.48 895

14 2 38.46 517

14 3 36.15 592

14 4 104.85 916

15 1 95.48 1412

15 2 41.99 527

15 3 33.24 468

15 4 64.64 921

16 1 33.66 408

16 2 65.66 758

16 3 40.57 497

16 4 90.14 629

17 1 92.95 1039

17 2NNN-NN-NNNN

17 3 58.19 726

17 4 96.62 886

18 1 30.64 418

18 2 74.19 945

18 3 40.00 446

18 4NNN-NN-NNNN

19 1 86.78 1331

19 2 30.03 438

19 3 43.56 457

19 4NNN-NN-NNNN

20 1 37.65 476

20 2 91.30 1128

20 3 57.96 755

20 4NNN-NN-NNNN

21 1 30.37 400

21 2 48.91 480

21 3 77.39 914

21 4 93.49 1225

22 1NNN-NN-NNNN

22 2 85.96 1258

22 3NNN-NN-NNNN

22 4 75.58 559

23 1 49.17 528

23 2 65.01 966

23 3 46.43 563

23 4 89.07 1251

24 1NNN-NN-NNNN

24 2 25.36 384

24 3 46.83 641

24 4 111.23 854

25 1 33.73 451

25 2 71.81 1233

25 3 84.99 1044

25 4 63.22 541

26 1NNN-NN-NNNN

26 2 68.36 1070

26 3NNN-NN-NNNN

26 4 95.36 1135

Problem 4.1

"The first issue your boss has asked you to address is whether or not there are differences in distribution cost between each of the four warehouses. Use the 0.05 level of significance to:

a) Perform a one-way ANOVA to look for differences in distribution costs between warehouses.

b) If the results in (a) indicate that it is appropriate, use the Tukey-Kramer procedure to determine which distribution centers differ in mean distribution costs.

c) Briefly summarize (in plain English) your procedures and the results of (a) and (b) for your manager.

Problem 4.2

"In addition to looking at differences between distribution centers, your manager also wants to know the relationship between the number of orders routed through each center and the distribution cost. Thus, the number of orders is your independent variable and the cost is your dependent variable.

a)Construct a scatter plot of the two variables.

b)Estimate a simple linear regression between these two variables.

c)Interpret the meaning of β0 and β1.

d)Predict the mean distribution costs of 500 orders, 1000 orders, and 1500 orders. Are these appropriate predictions?

e)Comment briefly on the predictive power/statistical significance of your estimates. "

Problem 4.3

Here, your task is to combine 4.1 and 4.2 into a single multiple regression model using dummy variables. To put the data in an Excel-friendly format, you will first need to create dummy variables for each of the different processing centers. a) Estimate a multiple regression model, again using cost as the dependent variable, however for your independent variables you will want to use orders AND your set of dummy variables (see technical note below).

b) Comment on the results from (a) in light of your results in 4.1 and 4.2. Does the coefficient on orders here match your results from 4.2? Do the coefficients on your dummy variables, and differences between the coefficients on your dummy variables, match up with your results from 4.1?

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