Please use R to answer this

I cannot upload the Excel, so I copy paste the data and provide screenshot

A call center is examining its data regarding the complaints of its callers. Two complaint areas are identified: customers who complain due to the wait time to talk to an agent, and customers who complain due to inexperienced agents. Each data set consists of number of complaints per week. The Excel file Assignment3_Problem1_Data includes the complaint data for the last year for these two categories.

a.What is the distribution of weekly complaints in each category? Identify the name of the distribution and its mean and standard deviation. Fit a continuous distribution to the data and use the K-S test as goodness-of-fit criteria.

b.Are the two categories of weekly complaints correlated to each other? If yes, describe their correlation.

Set up a simulation that calculates the total complaints by week. In your simulation, there will be two input variables - one for weekly complaints due to the wait time to talk to an agent and one for the weekly complaints due to inexperienced agents. Use the fitted distributions that you identified in (a). If there is a correlation between the complaint types, include it in your model. Using your simulation model, answer the following questions.

c.What is the mean and standard deviation of the total complaints by week?

d.What is the 95% confidence interval for the mean complaints by week? Interpret this interval.

e.What is the probability that the total complaints in a week will be less than 600?

f.What is the probability that the total complaints in a week will be bigger than 2600?

Now, re-run the simulation by ignoring the correlation among the complaint types and re-answer

questions (c)- (f) above. How would your answers to those questions change? What is the impact of

ignoring correlation in this setting?

Excel data:

Week, Wait Time, Inexperienced Agents

1 627 1652

2 605 1668

3 741 1902

4 631 1834

5 601 1554

6 678 2132

7 692 1808

8 655 1360

9 626 1750

10 536 1602

11 636 1538

12 643 1606

13 607 1636

14 419 1196

15 703 1772

16 571 1476

17 579 1790

18 531 1584

19 725 2084

20 691 1672

21 517 1468

22 546 1420

23 685 1694

24 740 1970

25 653 1274

26 632 1544

27 809 1934

28 611 1510

29 493 1220

30 542 1244

31 593 1278

32 458 1396

33 680 1876

34 621 1570

35 502 1318

36 528 1590

37 587 1368

38 583 1818

39 588 1796

40 587 1636

41 553 1676

42 669 1878

43 554 1532

44 740 2130

45 624 1970

46 545 1722

47 536 1428

48 574 1494

49 450 1092

50 702 1598

51 464 1174

52 688 1550

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