The document analyzes forecasting models to predict daily customer covers (number of customers) for a new restaurant, Fantasies Unlimited, opening in Las Vegas. It selects variables like daily visitors and traffic and uses historical data to conduct a linear regression analysis in Excel. This predicts covers based on the variables, though the analysis is limited as it does not test for multicollinearity between variables. Still, the variables largely explain the variance in covers, so the model can forecast employee needs and revenues if validated further.

1. Page 1
FORECASTING COVERS FOR THE ECLIPSE AT LUNAI’S
“The Best Restaurant in the Universe.”
by david harold moore

2. Page 2
TABLE OF CONTENTS
Headings
1. USE OF FORECAST, ITEMS TO BE FORECASTED AND TIME HORIZON OF FORECAST Page 3
2. FORECASTING MODEL SELECTION Page 4
3. AN EXERCISE IN LINEAR REGRESSION WITH MULTIPLE VARIABLES Page 6
4. ANALYSIS OF DATA Page 8
Charts & Tables
1. DIRECT COMPETITION TABLE Page 4
2. LUNAI’S MARKET SHARE PIE CHART Page 5
3. LINEAR REGRESSION VARIABLE TABLE Page 6
4. OUTPUT SUMMARY TABLE Page 7

3. Page 3
FORECASTING COVERS FOR THE ECLIPSE AT LUNAI’S RESTAURANT
by david harold moore
USE OF FORECAST, ITEMS TO BE FORECASTED AND TIME HORIZON OF FORECAST
Fantasies Unlimited Incorporated is opening its first restaurant in Las Vegas, Nevada and needs to forecast their
daily number of covers for lunch and dinner. There is no historical data to be used, so outside independent variables
must be chosen to help predict daily covers for lunch and dinner. This information will then help Fantasies Unlimited,
Inc. forecast employee scheduling, inventory levels, and revenues for the feasibility study.
To help forecast daily covers, Fantasies Unlimited, Inc. has chosen to use historical data from 6 different
independent variables: Daily Visitors, Daily Convention Attendance, Daily Air Traffic, Daily Southern California
Automobile Traffic and Projected Daily Advertising Dollars Spent by Fantasies Unlimited Incorporated.
These variables were chosen through the Target Market Strategy designed by the company. Fantasies Unlimited
has chosen to go after the young hip Generation X’ers, the Yuppies and those with a middle to upper income classification
while marketing a LOVERULES theme. This target market generally comes from Southern California, attends conventions
and flies into Las Vegas with the intent of Visiting Las Vegas.
Of the 30.6 million visitors to Las Vegas; 28% are from Southern California and 76% come solely for pleasure
(LVCVA, 1999). With these statistics steadily rising, Las Vegas will support the opening of a new restaurant.

4. Page 4
FORECASTING MODEL SELECTION
Fantasies Unlimited has chosen to use a linear regression model with multiple variables. The purpose of the enclosed
preliminary exercise is to determine whether or not the chosen variables are statistically significant in forecasting the
number of monthly covers. To do this Fantasies Unlimited has selected its direct competition based upon the style of the
restaurant, the location of the restaurant, the number of seats and the price range.
Direct Competition
LUNCH DINNER
seat L-P.P.A. L-Cover L-Est. Rev D-P.P.A. D-Cover D-Est. Rev. Est. Gross Rev Tot Covers
Circo 175 $35.00 240 $8,400.00 $65.00 450 $29,250.00 $13,403,400.00 690
Coyote Café 190 $13.00 250 $3,250.00 $17.00 400 $6,800.00 $3,577,800.00 650
Cozymel's 320 $11.00 200 $2,200.00 $18.00 400 $7,200.00 $2,565,400.00 600
Emeril's 200 $25.50 200 $5,100.00 $46.50 600 $27,900.00 $9,937,500.00 800
Freddie G's 200 $17.00 350 $5,950.00 $23.00 140 $3,220.00 $1,152,270.00 490
Gordon Biersch 310 $13.50 500 $6,750.00 $27.50 1200 $33,000.00 $11,754,750.00 1700
House of Blues 346 $13.50 600 $8,100.00 $18.00 1200 $21,600.00 $7,697,700.00 1800
Il Fornaio 280 $15.00 350 $5,250.00 $28.00 1100 $30,800.00 $10,970,050.00 1450
Lucky's 160 $11.00 300 $3,300.00 $14.00 550 $7,700.00 $2,744,500.00 850
Lupos 160 $18.00 200 $3,600.00 $39.00 450 $17,550.00 $7,529,400.00 650
McCormick 230 $15.00 240 $3,600.00 $31.00 450 $13,950.00 $4,969,800.00 690
Olives 180 $20.00 250 $5,000.00 $35.00 400 $14,000.00 $6,764,000.00 640
PF Chang's 248 $15.00 500 $7,500.00 $20.00 800 $16,000.00 $5,703,500.00 1300
Sam's Amer 180 $17.50 200 $3,500.00 $37.50 360 $13,500.00 $6,052,000.00 560
Spago Café 140 $18.00 450 $8,100.00 $35.00 400 $14,000.00 $7,867,600.00 850
Wollensky's 65 $25.00 50 $1,250.00 $40.00 125 $5,000.00 $1,781,250.00 175
Z-tejas 275 $13.00 150 $1,950.00 $26.00 270 $7,020.00 $2,501,070.00 420
Eclipse@Lunais 250 $17.50 400 $7,000.00 $35.00 600 $21,000.00 $7,483,000.00 1000
Average 215 $17.41 296 $4,870.59 $30.62 547 $15,793.53 $6,292,470.00 842

5. Page 5
The Direct Competition was interviewed by david harold moore who has developed relationships with most of the
management. They were asked for their last year’s gross revenue, their average per person average for lunch and dinner,
the number of seats available for dining and their check average for lunch and dinner. From that point, Fantasies
Unlimited was able to estimate the check average and the number of covers for lunch and dinner.
The number of covers for lunch and dinner was simply 10% of the market share plus a 2% increase in market.
Therefore, Fantasies Unlimited believes they will receive 12% of the total covers now available. Fantasies Unlimited
believea they will receive such a generous portion of the market due to their location, food quality, style of restaurant,
service, reasonable prices, and their aggressive marketing strategies for the Las Vegas visitors and the local market.
Coyote Café
5%
Cozymel's
5%
Emeril's
7%
Freddie G's
4%
Gordon Biersch
16%
House of Blues
15%
Il Fornaio
11%
Lucky's
7%
McCormick
6%
PF Chang's
11%
Wollensky's
1%
Lunai's
12%
Total Covers

6. Page 6
AN EXERCISE IN LINEAR REGRESSION WITH MULTIPLE VARIABLES
The data for the four independent variables were taken from the Las Vegas Convention and Visitors Authority for
1998. The data are monthly and the dependent variable (covers) was estimated from the 12% share of available covers (an
average of 40,000 covers per month). Again, this is a simple exercise in linear regression and not to be mistaken for the
real thing.
The purpose is to decide whether or not these independent variables are significant in forecasting the number of
covers. However, it sure seems ass-backwards to do it this way.
1998 Total Visitors Conv Attend Air Traffic So. Ca Autos Covers
JANUARY 2427013 301849 2292453 404070 38500
FEBRUARAY 2321774 382067 2345720 304509 41000
MARCH 2667136 256578 2729433 392677 41000
APRIL 2579068 333506 2568721 366281 39000
MAY 2636908 310886 2615388 447986 38500
JUNE 2443892 198568 2465008 434995 36000
JULY 2559046 130020 2529206 530175 38000
AUGUST 2690058 247693 2626127 534688 45000
SEPTEMBER 2517402 304154 2376670 417752 41000
OCTOBER 2759837 222950 2655328 388068 40000
NOVEMBER 2556856 521075 2558746 451850 45000
DECEMBER 2446138 92359 2429980 399182 36000

7. Page 7
OUTPUT SUMMARY
Regression Statistics
Multiple R 0.92189332
R Square 0.84988729
Adj. R Square 0.76410861
Stnd. Error 2115.36954
Observations 12
ANOVA
df SS MS F Significance F
Regression 4 177343149 44335787.2 9.90790724 0.00520901
Residual 7 31323518 4474788.28
Total 11 208666667
Coefficients StandardError t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept -23147.994 13184.2434 -1.7557317 0.12256359 -54323.753 8027.76559 -54323.753 8027.76559
Total Visitors 0.05596061 0.01122467 4.98550186 0.00159107 0.02941851 0.08250272 0.02941851 0.08250272
Conv Attend 0.00856434 0.00582111 1.47125563 0.18469377 -0.0052004 0.02232908 -0.0052004 0.02232908
Air Traffic -0.026805 0.01008932 -2.6567694 0.03261966 -0.0506624 -0.0029476 -0.0506624 -0.0029476
So. Ca Autos -0.0327007 0.01128706 -2.8971863 0.02307837 -0.0593903 -0.0060111 -0.0593903 -0.0060111
RESIDUAL OUTPUT
Observation Predicted
covers
Residuals
January 40591 -591
February 37217 2782
March 42300 -300
April 43202 -2202
May 42322 2677
June 35015 -15
July 36038 961
August 41632 367
September 42964 35
October 49337 662
November 41034 -2034
December 36341 -2341

8. Page 8
ANALYSIS OF DATA
Ho: The four independent variables (total visitors, convention attendance, air traffic and Southern California
automobile traffic) will not significantly explain the variance in projected covers.
Ha: The four independent variables (total visitors, convention attendance, air traffic and Southern California
automobile traffic) will significantly explain the variance in projected covers.
According to the linear regression data analysis done by Excel, 84.98% (r square) of the variance in projected covers
for THE ECLIPSE AT LUNAI’S RESTAURANT has been explained by the four independent variables. However, with an r-
square over 70% the chances of multicollinearity between the independent variables is great. Since this analysis was not
done on SPSS Fantasies Unlimited is unable to prove multicollinearity between the independent variables.
The independent variables, on the other hand, are all statistically significant according to the t-statistic, except
Convention Attendance, which is approaching statistical significance with a t-value of 1.47. If it wasn’t for the
multicollinearity and the minimal amount of data as well as the completely made-up covers used for each period, these
might be good variables.
Y = -23147.994 + 0.05596061(Tot Vis) + 0.00856434(Conv Attend) – 0.026805(Air Traffic) -.0327007(So. Ca Autos)
According to the data analysis, the alternate hypothesis will be accepted until the multicollinearity has been
proven.