A management consultant wants to determine whether the age and gender of a restaurant’s wait staff influence the size of the tip the customer leaves. Three age brackets (factor A in columns: young, middle-age, older) and gender (factor B in rows: male, female) are used to construct a two-way ANOVA experiment with interaction. For each combination, the percentage of the total bill left as a tip for 10 wait staff is examined. The following ANOVA table is produced.
ANOVA | ||||||
Source of Variation | SS | df | MS | F | p-value | |
Sample | 0.04278 | 1 | 0.04278 | 16.595 | 0.000 | |
Columns | 0.01793 | 2 | 0.00897 | 3.479 | 0.038 | |
Interaction | 0.00561 | 2 | 0.00281 | 1.089 | 0.344 | |
Within | 0.1392 | 54 | 0.00258 | |||
Total | 0.20552 | 59 | ||||
a. Can you conclude that there is interaction between age and gender at the 1% significance level?
Yes, since the p-value associated with interaction is less than significance level.
Yes, since the p-value associated with interaction is greater than significance level.
No, since the p-value associated with interaction is less than significance level.
No, since the p-value associated with interaction is greater than significance level.
b-1. Are you able to conduct tests based on the main effects?
Yes, since the interaction between the two factors is not significant.
Yes, since the interaction between the two factors is significant.
No, the interaction between the two factors is not significant.
No, the interaction between the two factors is significant.
b-2. If yes, conduct these tests at the 1% significance level. (You may select more than one answer. Single click the box with the question mark to produce a check mark for a correct answer and double click the box with the question mark to empty the box for a wrong answer. Any boxes left with a question mark will be automatically graded as incorrect.)
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