# BEASTBUY Data Analysis

See attached files.

Problem Set 4

You work in the shipping and logistics department for Beast Buy, an American mail order company that specializes in pet food for VERY exotic pets. Beast Buy has four warehouses/distribution centers that provide product to each of four different regions in the US; Atlanta services the southeast, Boston services the northeast, Cleveland services the midwest, and Denver services the west. Your manager has recently asked you to analyze the efficiency of these distribution centers.

The data tab contains data from each of the last 26 weeks for each of the four distribution centers. The first column simply denotes the week in which the data were collected. The second column indicates which warehouse the data are from (1=Atlanta, 2=Boston, 3=Cleveland, 4=Denver). The third column contains the distribution cost (in thousands of US dollars) associated with the particular warehouse in each week, and the final column contains data on the number of orders routed through each warehouse each week. Use this data to answer the following questions. Problem 4.1 comes from week 9 material, 4.2 comes from week 10 material, and 4.3 relates to week 11 material. Because this is due before week 11, question 4.3 is extra credit. Should you encounter any difficulties with these problems, the optional problems below are very similar to the questions in this problem set, and the answers to the optional questions can be found in the back of the textbook. You can also request that the tutor work extensively with you on the optional problems.

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 centres 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 centres, your manager also wants to know the relationship between the number of orders routed through each centre 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 ?0 and ?1.

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

EXTRA CREDIT. To answer this question, you will need to read the materials for week 11 of the class. 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 centres.

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?

The data set is given below:

Week Warehouse Cost Orders

1 1 96.40 1285

1 2 32.37 432

1 3 105.76 1366

1 4 91.68 1037

2 1 57.90 889

2 2 28.72 442

2 3 109.99 1466

2 4 98.33 762

3 1 58.65 827

3 2 50.87 842

3 3 63.26 938

3 4 62.43 518

4 1 106.28 1220

4 2 98.65 1326

4 3 44.30 795

4 4 143.62 892

5 1 34.08 553

5 2 57.54 817

5 3 51.74 611

5 4 99.78 1138

6 1 94.71 1340

6 2 75.03 1061

6 3 88.43 1261

6 4 105.58 796

7 1 101.74 1198

7 2 113.49 1336

7 3 34.72 402

7 4 150.34 1234

8 1 32.15 447

8 2 128.25 1358

8 3 41.43 489

8 4 118.36 908

9 1 25.54 428

9 2 97.43 1259

9 3 57.50 844

9 4 182.18 1259

10 1 65.22 957

10 2 35.74 490

10 3 67.83 984

10 4 73.76 585

11 1 30.94 393

11 2 77.66 826

11 3 65.10 697

11 4 79.66 1155

12 1 130.68 1301

12 2 76.60 1061

12 3 79.00 1022

12 4 88.93 816

13 1 59.56 843

13 2 69.96 855

13 3 81.63 1203

13 4 117.69 1450

14 1 61.79 644

14 2 41.62 485

14 3 80.25 976

14 4 82.21 733

15 1 41.75 539

15 2 58.73 601

15 3 71.79 1002

15 4 60.34 776

16 1 51.70 571

16 2 36.41 595

16 3 83.02 859

16 4 159.00 1355

17 1 38.68 453

17 2 70.74 1056

17 3 33.73 430

17 4 80.77 561

18 1 100.92 1124

18 2 81.44 886

18 3 42.55 597

18 4 82.48 726

19 1 82.78 901

19 2 80.02 947

19 3 60.69 667

19 4 70.91 991

20 1 56.34 800

20 2 27.71 461

20 3 51.33 650

20 4 125.94 901

21 1 32.15 481

21 2 42.12 461

21 3 52.69 663

21 4 65.18 421

22 1 108.26 1216

22 2 50.07 522

22 3 32.78 445

22 4 48.60 420

23 1 61.72 1020

23 2 42.33 522

23 3 98.49 1275

23 4 64.26 707

24 1 54.12 555

24 2 30.81 516

24 3 84.06 1201

24 4 55.44 606

25 1 70.39 828

25 2 69.07 1160

25 3 69.31 1107

25 4 105.83 1134

26 1 52.87 596

26 2 104.18 1155

26 3 35.25 396

26 4 42.11 484

https://brainmass.com/statistics/regression-analysis/beastbuy-data-analysis-438193

#### Solution Summary

The solution provides step by step method for the calculation of ANOVA and multiple regression analysis. Formula for the calculation and Interpretations of the results are also included.