Your team has been selected to manage the pricing decisions for an online travel-booking website in the early days of dynamic pricing. There are not algorithms to make pricing decisions for you yet, but you do have access to historical data to help you decide how to set prices. You've been asked to start with a single flightUA (United Airlines) 396 from San Francisco (SFO) to Honolulu (HNL)to see how well your pricing decisions perform.

You will be managing the price of flight UA 396 from San Francisco (SFO) to Honolulu (HNL) for the five weeks prior to takeoff. Takeoff will occur at 10:20 am PST on a Friday in June.

You'll be setting a single price for all Economy seats on the flight at the start of each of the five weeks, based on the number of remaining seats. You don't want to set the price so low that everyone is clamoring to purchase the tickets and they sell out five weeks before the flight takes off. You also don't want to set the price so high that you have many open seats left when the flight takes off.

This flight uses a Boeing 757 aircraft, which can hold 265 Economy passengers. At the start of your trial run, 95 seats had already been booked, leaving 170 remaining. The price of booking an Economy seat last week was $450.

To help you decide how to set prices, you have data on previous seat sales on flight UA 396. To ensure comparability, you have data only for UA 396 flights leaving at 10:20 am PST on Fridays. The data consists of prices and seat sales for each of the five weeks prior to these flights. You also know the month of each flight's departure.

Conceptually, you want to balance two things: (1) how many seats you think you can sell at a given price this week, and (2) how many seats you think you can sell at that price (or another price) in the following weeks.

This means that for any given week, you want to try to predict how many sales you can expect to get in the future, from among the remaining seats that you don't sell this week.

You will be writing a report to your boss chronicling your first attempt at dynamic pricing. The "sections" of this report, and instructions as to what they should include, are listed below.

Section 1: Variables in the Model

Your dataset contains variables that you know are predictive of seat sales. Begin your report by (1.1) writing a description of why these variables are important in this empirical context (forecasting sales to optimize pricing in the airline industry), and (1.2) providing evidence from the raw data to support your hypotheses about the relationship between these variables and sales, where possible.

The variables you should be including in your model are:

(1) Dummy variables for season (instead of Month, due to Excel variable limits). Use March-May for Spring, June-Aug for Summer, and Sept-Nov for Fall.

(2) Dummy variables for "Weeks before take-off"

(3) Price, interacted with "Weeks before take-off"

Section 2: Model parameter estimates ("coefficients" in Excel)

Run a model with the variables described in section 1, provide the output of your statistical software, and do the following:

(1) Discuss whether the parameter estimates of your model are consistent with your hypotheses from section 1 (and any corresponding data patterns you offered as support for your hypotheses).

(2) Discuss the interpretation of our price coefficients with respect to weeks before take-off. When are consumers most and least price sensitive? What implication does this have for our pricing decisions, generally speaking? (HINT: Think about what each coefficient means with respect to a unit change in X...).

Excel Data:

Seats Sold	Weeks Before	Month_of_Flight	Price
48	5	Jan	$ 366
56	4	Jan	$ 373
45	3	Jan	$ 403
31	2	Jan	$ 417
40	1	Jan	$ 456
23	5	Jan	$ 423
42	4	Jan	$ 440
34	3	Jan	$ 439
49	2	Jan	$ 397
35	1	Jan	$ 441
47	5	Jan	$ 413
53	4	Jan	$ 364
23	3	Jan	$ 441
40	2	Jan	$ 444
43	1	Jan	$ 431
37	5	Jan	$ 428
65	4	Jan	$ 363
35	3	Jan	$ 359
36	2	Jan	$ 437
25	1	Jan	$ 404
37	5	Feb	$ 468
21	4	Feb	$ 449
19	3	Feb	$ 434
36	2	Feb	$ 393
44	1	Feb	$ 403
29	5	Feb	$ 459
15	4	Feb	$ 468
31	3	Feb	$ 416
36	2	Feb	$ 437
33	1	Feb	$ 376
53	5	Feb	$ 394
49	4	Feb	$ 401
17	3	Feb	$ 466
40	2	Feb	$ 408
35	1	Feb	$ 390
40	5	Feb	$ 457
50	4	Feb	$ 405
27	3	Feb	$ 444
30	2	Feb	$ 396
28	1	Feb	$ 426
45	5	Mar	$ 446
56	4	Mar	$ 421
53	3	Mar	$ 417
42	2	Mar	$ 403
32	1	Mar	$ 494
45	5	Mar	$ 462
41	4	Mar	$ 436
35	3	Mar	$ 406
33	2	Mar	$ 456
26	1	Mar	$ 487
51	5	Mar	$ 406
28	4	Mar	$ 458
25	3	Mar	$ 449
31	2	Mar	$ 455
29	1	Mar	$ 485
53	5	Mar	$ 433
42	4	Mar	$ 406
42	3	Mar	$ 456
57	2	Mar	$ 428
41	1	Mar	$ 487
52	5	Apr	$ 427
37	4	Apr	$ 444
49	3	Apr	$ 407
36	2	Apr	$ 412
23	1	Apr	$ 493
62	5	Apr	$ 406
64	4	Apr	$ 415
43	3	Apr	$ 446
45	2	Apr	$ 442
35	1	Apr	$ 420
36	5	Apr	$ 491
47	4	Apr	$ 463
46	3	Apr	$ 423
28	2	Apr	$ 478
29	1	Apr	$ 465
37	5	Apr	$ 435
30	4	Apr	$ 498
27	3	Apr	$ 492
48	2	Apr	$ 411
33	1	Apr	$ 469
22	5	May	$ 493
43	4	May	$ 442
17	3	May	$ 493
31	2	May	$ 483
21	1	May	$ 500
40	5	May	$ 416
33	4	May	$ 470
52	3	May	$ 427
49	2	May	$ 462
27	1	May	$ 477
39	5	May	$ 470
47	4	May	$ 422
20	3	May	$ 490
33	2	May	$ 512
22	1	May	$ 493
37	5	May	$ 423
32	4	May	$ 509
43	3	May	$ 471
44	2	May	$ 487
40	1	May	$ 451
59	5	Jun	$ 441
29	4	Jun	$ 530
52	3	Jun	$ 453
54	2	Jun	$ 461
34	1	Jun	$ 507
22	5	Jun	$ 495
21	4	Jun	$ 537
35	3	Jun	$ 487
40	2	Jun	$ 487
41	1	Jun	$ 445
30	5	Jun	$ 467
18	4	Jun	$ 538
45	3	Jun	$ 470
31	2	Jun	$ 530
40	1	Jun	$ 530
35	5	Jun	$ 460
43	4	Jun	$ 447
35	3	Jun	$ 470
37	2	Jun	$ 509
39	1	Jun	$ 511
46	5	Jul	$ 488
30	4	Jul	$ 503
32	3	Jul	$ 518
34	2	Jul	$ 522
25	1	Jul	$ 501
37	5	Jul	$ 486
27	4	Jul	$ 530
40	3	Jul	$ 481
52	2	Jul	$ 490
31	1	Jul	$ 521
26	5	Jul	$ 491
40	4	Jul	$ 504
40	3	Jul	$ 503
26	2	Jul	$ 560
45	1	Jul	$ 504
16	5	Jul	$ 543
38	4	Jul	$ 495
26	3	Jul	$ 527
45	2	Jul	$ 489
41	1	Jul	$ 544
34	5	Aug	$ 460
35	4	Aug	$ 475
38	3	Aug	$ 470
49	2	Aug	$ 504
38	1	Aug	$ 493
23	5	Aug	$ 506
48	4	Aug	$ 477
55	3	Aug	$ 447
41	2	Aug	$ 463
42	1	Aug	$ 525
49	5	Aug	$ 480
45	4	Aug	$ 474
32	3	Aug	$ 440
35	2	Aug	$ 494
48	1	Aug	$ 502
37	5	Aug	$ 480
49	4	Aug	$ 447
44	3	Aug	$ 485
29	2	Aug	$ 534
32	1	Aug	$ 534
32	5	Sep	$ 424
12	4	Sep	$ 503
22	3	Sep	$ 432
19	2	Sep	$ 468
10	1	Sep	$ 502
17	5	Sep	$ 493
35	4	Sep	$ 453
10	3	Sep	$ 489
38	2	Sep	$ 436
43	1	Sep	$ 415
28	5	Sep	$ 439
28	4	Sep	$ 459
21	3	Sep	$ 455
26	2	Sep	$ 510
28	1	Sep	$ 421
31	5	Sep	$ 477
31	4	Sep	$ 449
19	3	Sep	$ 440
23	2	Sep	$ 450
48	1	Sep	$ 415
42	5	Oct	$ 440
38	4	Oct	$ 448
48	3	Oct	$ 398
40	2	Oct	$ 473
27	1	Oct	$ 400
42	5	Oct	$ 405
38	4	Oct	$ 470
15	3	Oct	$ 452
40	2	Oct	$ 447
36	1	Oct	$ 466
45	5	Oct	$ 401
50	4	Oct	$ 415
16	3	Oct	$ 471
19	2	Oct	$ 474
37	1	Oct	$ 406
41	5	Oct	$ 434
46	4	Oct	$ 430
30	3	Oct	$ 440
25	2	Oct	$ 419
39	1	Oct	$ 392
40	5	Nov	$ 419
47	4	Nov	$ 430
31	3	Nov	$ 374
39	2	Nov	$ 369
32	1	Nov	$ 410
48	5	Nov	$ 382
55	4	Nov	$ 406
48	3	Nov	$ 381
27	2	Nov	$ 430
31	1	Nov	$ 463
46	5	Nov	$ 421
46	4	Nov	$ 407
44	3	Nov	$ 396
35	2	Nov	$ 371
21	1	Nov	$ 437
32	5	Nov	$ 416
51	4	Nov	$ 379
34	3	Nov	$ 392
31	2	Nov	$ 410
37	1	Nov	$ 455
26	5	Dec	$ 436
36	4	Dec	$ 419
40	3	Dec	$ 386
42	2	Dec	$ 432
28	1	Dec	$ 434
39	5	Dec	$ 435
37	4	Dec	$ 406
22	3	Dec	$ 413
45	2	Dec	$ 449
33	1	Dec	$ 489
52	5	Dec	$ 396
51	4	Dec	$ 386
34	3	Dec	$ 419
40	2	Dec	$ 424
35	1	Dec	$ 449
23	5	Dec	$ 423
45	4	Dec	$ 376
38	3	Dec	$ 418
44	2	Dec	$ 454
47	1	Dec	$ 421