TABLE 10-14
A problem with a telephone line that prevents a customer from receiving or making calls is disconcerting to both the customer and the telephone company. The data on samples of 20 problems reported to two different offices of a telephone company and the time to clear these problems (in minutes) from the customers' lines are collected. Below is the Excel output to see whether there is evidence of a difference in the mean waiting time between the two offices assuming that the population variances in the two offices are not equal.

\begin{array}{l}\text { t- Test: Two- Sample Assuming Unequal Variances }\\\begin{array} { l r r } \hline & \text { Office 1 } & \text { Office 2 } \\\hline \text { Mean } & 2214 & 2.0115 \\\text { Variance } & 2.951657 & 3.57855 \\\text { Observations } & 20 & 20 \\\text { Hypothesized Mean Difference } & 0 & \\\mathrm { df } & 38 & \\\mathrm { t } \text { Stat } & 0.354386 & \\\mathrm { P } ( \mathrm { T } < = \mathrm { t } ) \text { one- tail } & 0.362504 & \\\mathrm { t } \text { Critical one- tail } & 1.685953 & \\\mathrm { P } ( \mathrm { T } < = \mathrm { t } ) \text { two- tail } & 0.725009 & \\\mathrm { t } \text { Critical two- tail } & 2.024394 &\end{array}\end{array}

-Referring to Table 10-14, state the null and alternative hypotheses for testing if there is evidence of a difference in the variability of the waiting time between the two offices.
A)

H_{0}: \sigma_{I}^{2}-\sigma_{I I}^{2} \geq 0

versus

H_{1}: \sigma_{I}^{2}-\sigma_{I I}^{2}<0

H_{0}: \sigma_{I}^{2}-\sigma_{I I}^{2} \neq 0

versus

H_{1}: \sigma_{I}^{2}-\sigma_{I I}^{2}=0

H_{0}: \sigma_{I}^{2}-\sigma_{I I}^{2} \leq 0

versus

H_{1}: \sigma_{I}^{2}-\sigma_{I I}^{2}>0

H_{0}: \sigma_{I}^{2}-\sigma_{I I}^{2}=0

versus

H_{1}: \sigma_{I}^{2}-\sigma_{I I}^{2} \neq 0

TABLE 10-6 Two Samples Each of Size 25 Are Taken from Independent

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