A consumer preference study compares the effects of three different bottle designs (A, B, and C) on sales of a popular fabric softener. A completely randomized design is employed. Specifically, 15 supermarkets of equal sales potential are selected, and 5 of these supermarkets are randomly assigned to each bottle design. The number of bottles sold in 24 hours at each supermarket is recorded. The data obtained are displayed in the following table.

Bottle Design Study Data

A B C

16 33 23

18 31 27

19 37 21

17 29 28

13 34 25

The Excel output of a one-way ANOVA of the Bottle Design Study Data is shown below.

SUMMARY

Groups Count Sum Average Variance

Design A 5 83 16.6 5.3

Design B 5 164 32.8 9.2

Design C 5 124 24.8 8.2

ANOVA

Source of Variation SS df MS F P-Value F crit

Between Groups 656.1333 2 328.0667 43.35683 3.23E-06 3.88529

Within Groups 90.8 12 7.566667

Total 746.9333 14

Picture Click here for the Excel Data File

(a)

Test the null hypothesis that A, B, and C are equal by setting = .05. On the basis of this test, can we conclude that display panels A, B, and C have different effects on the mean time to stabilize the emergency condition? (Round your answer to 2 decimal places.)

F =

p-value =

H0: bottle design have an impact on sales.

(b)

Consider the pairwise differences B - A, C - A , and C - B. Find a point estimate of and a Tukey simultaneous 95 percent confidence interval for each pairwise difference. Interpret the results in practical terms. Which bottle design maximizes mean daily sales? (Round your answers to 2 decimal places. Negative amounts should be indicated by a minus sign.)

Bottle design maximizes sales.

(c)

Find a 95 percent confidence interval for each of the treatment means A, B, and C. Interpret these intervals. (Round your answers to 2 decimal places. Negative amounts should be indicated by a minus sign.)