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Case Description

Demand for French Bread at Howie's Bakery

Howie's Bakery is one of the most popular bakeries in town, and the favorites at Howie's is

French bread. Each day of the week, Howie's bakes a number of loaves of French bread, more or

less according to a daily schedule. To maintain its fine reputation, Howie's gives away to charity

any loaves not sold on the day they are baked. Although this occurs frequently, it is also common

for Howie's to run out of French bread on any given day - more demand than supply. In this case,

no extra loaves are baked that day; the customer have to go elsewhere (or come back to Howie's

next day) for their French bread. Although French bread of Howie's is always popular, Howie's

stimulates demand by running occasional 10% off sales.

Howie's has collected data for 20 consecutive weeks, 140 days in all. These data are listed in the

file Howie.xls. The variables are Day (Mon- Sunday), Supply (number of loaves baked that day),

Onsale (whether French bread is sale at reduced price that day), and Demand (Loaves actually sold

that day).

Sample data

Day Supply On Sale DemandMonday 46 No 44Tuesday 48 Yes 27Wednesday 48 No 48Thursday 53 No 29Friday 99 Yes 88Saturday 134 No 112Sunday 98 No 64Monday 52 Yes 47Tuesday 47 Yes 46. . . .Howie's would like to see whether regression can be used successfully to estimate demand from

the other data in the file.

Howie reasons that if these other variables can be used to predict demand, then might be able to

determine his daily supply (number of loaves to bake) in a more cost effective way.

How successful is regression with this data?

Is Howie correct that regression can help him determine his daily supply? Is any information

missing that would be useful? How would you obtain it? How would you use it? Is this extra

information is really necessary?