The Pancake House did a brisk business on the weekend and the maître d' was always on the lookout for ways to improve the customer experience

He carefully tracked the number of customers that graced their establishment over the last four weekends. He was hopeful that he could forecast the number of customers that would come for the world's finest pancakes the next weekend.

Weekend 1 Weekend 2 Weekend 3 Weekend 4
Friday 131 216 286 355
Saturday 225 311 408 490
Sunday 166 249 330 415

Using the data in the table, first plot the data and comment on the appearance of the demand pattern. Then develop a forecast for weekend #5 that fits the data.
What will be an ideal response?

Answer: Data are highly seasonal as the graph and data table indicate.

The overall average for the data set is 298.5. Dividing each day's average by the overall average yields the seasonal relatives shown in the table.

Day Average Seasonal
Friday 247 0.827
Saturday 358.5 1.201
Sunday 290 0.972

Dividing each entry in the table by the seasonal relative and using linear regression with the first Friday as 1 and the last Sunday as 12 yields the regression equation
Customers = 122.8 + 27.03 ∗ Day #
Reseasoning the forecasted values by the seasonal relatives gives the results in the table and the graph below.

Day # Day Customers Regression Adjusted Regression
1 Friday 131 149.83 123.91
2 Saturday 225 176.86 212.41
3 Sunday 166 203.89 198.18
4 Friday 216 230.92 190.97
5 Saturday 311 257.95 309.80
6 Sunday 249 284.98 277.00
7 Friday 286 312.01 258.03
8 Saturday 408 339.04 407.19
9 Sunday 330 366.07 355.82
10 Friday 355 393.10 325.09
11 Saturday 490 420.13 504.58
12 Sunday 415 447.16 434.64