Question about Multiple Regression and Model Building
Questions you need to answer:
1. What factors appear to influence beverage sales at the Westar?
2. Should Westar consider re-hiring the piano player for the Pour Nous Lounge?
3. How about happy hour food?
4. Can you suggest a new forecasting method for Jane to try out?
5. Explain what is Multicollinearity
CASE:
The Westar hotel, a 250-room business hotel located in a resort area of Colorado Rockies, has two lounges on its premises: the Pour Nous Lounge, a pub-style lounge that seats about 50, and Gaiete, a 200-seat nightclub.
Jane Bartley is a staff analyst for Westar's corporate parent, charged (among other things) with predicting and analyzing beverages sales (liquor, beer, and wine, primarily) for the chain's business hotels. In fact, her deadline for producing some sort of forecast for Westar beverage sales was the end of the week. After years in the business, Jane had seen a lot of approaches to forecasting beverages sales, but none that struck her as particularly helpful. Most of them used only room occupancy as a basis for forecasting.
Not that it was unreasonable to consider room occupancy: clearly, the number of people staying in the hotel might well influence beverage sales. Further, hotel occupancy was (at least to some extent) predictable, as people made advance reservations, so it could be used for forecasting. In the case of the Westar, though, Jane suspected other things were involved, and the existing corporate forecasting methods (such as they were) made no allowance for them. Specifically Jane wondered if season, promotional food offered during happy hour, and the presence or absence of a piano player in the Pour Nous Lounge might be factors to consider. She was able to gather data for the most recent 30 months on some relevant variables described below.
The promotional food had been discontinued after 12 month and the piano player after month 13 as the previous management attempted to cut costs. Data from month 14 and 15 were unavailable because a change in ownership about them had led to some changes in record-keeping techniques.
Jane suspected that with these data she could develop a better understanding of the factors that really influenced beverages sales at the Westar and perhaps even influence her department to consider some new and better ways to do the forecasting.
See attached excel file for the Dataset,
This question has the following supporting file(s):
- Case_Westar Beverage Sales_Dataset.xls
- Multiple Regression and Model Building.doc
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- Multiple+Regression+and+Model+Building.doc
- Case_Westar+Beverage+Sales_Dataset.xls
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Extracted Content from Question Files:
- Case_Westar Beverage Sales_Dataset.xls
MONTH BEVTOTAL BEVPUB BEVNITE FOODTOT FOODPUB FOODNITE ROOMS
1 24443 7254 17189 2317 210 2107 4154
2 22924 7238 15686 2341 200 2141 4457
3 27501 8150 19351 3353 230 3123 5077
4 29282 8081 21201 2796 210 2586 4456
5 21447 7011 14436 2508 220 2288 4733
6 20404 6076 14328 2674 220 2454 5383
7 19051 8001 11050 2381 210 2171 5010
8 19235 8636 10599 2977 230 2747 5030
9 21498 9064 12434 1624 220 1404 5372
10 26619 8760 17859 1852 210 1642 4889
11 25600 7794 17806 1676 220 1456 4295
12 30373 8171 22202 2706 220 2486 3425
13 23483 8186 15297 2497 0 2497 3893
14
15
16 26004 7703 18301 2465 0 2465 4395
17 19624 6764 12860 2183 0 2183 4524
18 20459 6524 13935 3589 0 3589 5044
19 20956 4209 16747 3010 0 3010 5174
20 29754 7084 22670 3275 0 3275 6331
21 29098 5321 23777 3225 0 3225 4706
22 30559 5879 24680 3448 0 3448 4259
23 31038 5882 25156 3835 0 3835 4284
24 33833 4440 29393 3785 0 3785 3364
25 30095 6935 23160 3198 0 3198 4250
26 28163 4421 23742 2657 0 2657 4342
27 34051 5561 28490 3054 0 3054 5006
28 33139 6033 27106 2930 0 2930 4761
29 29468 5180 24288 1657 0 1657 3860
30 28896 6222 22674 2407 0 2407 5300
VARIABLES
MONTH = Month number (1=January)
BEVTOTAL = Total beverage sales, pub and nightclub
BEVPUB = Beverage sales for pub
BEVNITE = Beverage sales for night club
FOODTOT = Total amount spent on food, pub and night club.
FOODPUB = Amount spent on food for pub.
FOODNITE = Amount spent on food for night club.
ROOMS = Total room occupancy for Westar.
- Multiple Regression and Model Building.doc
Multiple Regression and Model Building - Westar Beverage Sales Case Study
BEWARE OF MULTICOLLINEARITY.
The statistical analysis of the data involves Multiple Regression analysis.
Questions you need to answer:
1. What factors appear to influence beverage sales at the Westar?
2. Should Westar consider re-hiring the piano player for the Pour Nous Lounge?
3. How about happy hour food?
4. Can you suggest a new forecasting method for Jane to try out?
5. Explain what is Multicollinearity
CASE:
The Westar hotel, a 250-room business hotel located in a resort area of Colorado Rockies, has
two lounges on its premises: the Pour Nous Lounge, a pub-style lounge that seats about 50, and
Gaiete, a 200-seat nightclub.
Jane Bartley is a staff analyst for Westar’s corporate parent, charged (among other things) with
predicting and analyzing beverages sales (liquor, beer, and wine, primarily) for the chain’s
business hotels. In fact, her deadline for producing some sort of forecast for Westar beverage
sales was the end of the week. After years in the business, Jane had seen a lot of approaches to
forecasting beverages sales, but none that struck her as particularly helpful. Most of them used
only room occupancy as a basis for forecasting.
Not that it was unreasonable to consider room occupancy: clearly, the number of people
staying in the hotel might well influence beverage sales. Further, hotel occupancy was (at least
to some extent) predictable, as people made advance reservations, so it could be used for
forecasting. In the case of the Westar, though, Jane suspected other things were involved, and
the existing corporate forecasting methods (such as they were) made no allowance for them.
Specifically Jane wondered if season, promotional food offered during happy hour, and the
presence or absence of a piano player in the Pour Nous Lounge might be factors to consider.
She was able to gather data for the most recent 30 months on some relevant variables
described below.
The promotional food had been discontinued after 12 month and the piano player after month
13 as the previous management attempted to cut costs. Data from month 14 and 15 were
unavailable because a change in ownership about them had led to some changes in record-
keeping techniques.
Jane suspected that with these data she could develop a better understanding of the factors
that really influenced beverages sales at the Westar and perhaps even influence her
department to consider some new and better ways to do the forecasting.
See attached excel file for the Dataset,
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