SCENARIO 10-13
the Amount of Time Required to Reach a Customer

question 283

Multiple Choice

SCENARIO 10-13
The amount of time required to reach a customer service representative has a huge impact on customer satisfaction.Below is the Excel output from a study to see whether there is evidence of a difference in the mean amounts of time required to reach a customer service representative between two hotels.Assume that the population variances in the amount of time for the two hotels are not equal. $$\begin{array}{l}\text { t-Test: Two-Sample Assuming Unequal Variances }\\\begin{array} { l r r } \hline & \text { Hotel 1 } & \text { Hotel 2 } \\\hline \text { Mean } & 2.214 & 2.0115 \\\text { Variance } & 2.951657 & 3.57855 \\\text { Observations } & 20 & 20 \\\text { Hypothesized Mean Difference } & 0 & \\\text { df } & 38 & \\\text { t Stat } & 0.354386 & \\\text { P } ( T < = t ) \text { one-tail } & 0.362504 & \\\text { t Critical one-tail } & 1.685953 & \\\text { P } ( \text { T } < \text { t) two-tail } & 0.725009 & \\\text { t Critical two-tail } & 2.024394 & \\\hline\end{array}\end{array}$$
-Referring to Scenario 10-13, suppose $\alpha$ = 0.05.Which of the following represents the correct conclusion for a test on a difference in the variabilities of the amount of time required to reach a customer service representative between the two hotels?

• A)There is no evidence of a difference in the variabilities of the amount of time required to reach a customer service representative between the two hotels.
• B)There is evidence of a difference in the variabilities of the amount of time required to reach a customer service representative between the two hotels.
• C)There is no evidence that the variabilities of the amount of time required to reach a customer service representative between the two hotels are the same.
• D)There is evidence that the variabilities of the amount of time required to reach a customer service representative between the two hotels are the same.

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Definitions:

Main Effect

The direct influence of an independent variable on a dependent variable in an experimental design.

Simple Effect

The effect of one variable at a specific level of another variable in a factorial experiment.

Interaction Effect

In statistics, the effect that exists when two or more variables combine to affect the outcome of a dependent variable in a way that cannot be simply added.

Main Effect

In experimental design, a main effect is the direct impact of an individual independent variable on a dependent variable, ignoring the effects of other variables.