TABLE 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. \[\begin{array}{l} \text { t-Test: Two-Sample Assuming Unequal Variances }\\ \begin{array} { l r r } \hline & \text { Hotel 1 } &{ \text { Hotel 2 } } \\ \hline \text { Mean } & 2.214 & 2.0115 \\ \hline \text { Variance } & 2.951657 & 3.57855 \\ \text { Observations } & 20 & 20 \\ \hline \text { Hypothesized Mean Difference } & 0 & \\ \hline \text { df } & 38 & \\ \hline \text { t Stat } & 0.354386 & \\ \hline \text { P(T <=t) one-tail } & 0.362504 & \\ \text { t Critical one-tail } & 1.685953 & \\ \hline \text { P(T<=t) two-tail } & 0.725009 & \\ \text { t Critical two-tail } & 2.024394 & \\ \hline \end{array} \end{array}\] -Referring to Table 10-13, what is(are)the critical value(s)of the relevant hypothesis test if the level of significance is 0.10?

A) 1.6860 B) ±1.6860 C) 2.0244 D) ±2.0244 A) 1.6860 B) ±1.6860 C) 2.0244 D) ±2.0244

TABLE 10-2 A researcher randomly sampled 30 graduates of an MBA program and recorded data concerning their starting salaries. Of primary interest to the researcher was the effect of gender on starting salaries. The result of the pooled-variance t test of the mean salaries of the females (Population 1) and males (Population 2) in the sample is given below. $\begin{array}{|l|r|} \hline \text { Hypothesized Difference } & 0 \\ \hline \text { Level of Significance } & 0.05 \\ \hline \quad \quad \quad \quad \quad \quad{\text { Population 1 Sample }} \\ \hline \text { Sample Size } & 18 \\ \hline \text { Sample Mean } & 99210 \\ \hline \text { Sample Standard Deviation } & 13577 \\ \hline \quad \quad \quad \quad \quad \quad{\text { Population 2 Sample }} \\ \hline \text { Sample Size } & 12 \\ \hline \text { Sample Mean } & 105820 \\ \hline \text { Sample Standard Deviation } & 11741 \\ \hline & \\ \hline \text { Difference in Sample Means } & -6610 \\ \hline \boldsymbol{t} \text { Test Statistic } & -1.37631 \\ \hline \quad \quad \quad \quad \quad \quad \quad \quad{\text { Lower-Tail Test }} \\ \hline \text { Lower Critical Value } & -1.70113 \\ \hline \boldsymbol{p} \text {-Value } & \mathbf{0 . 0 8 9 8 1 6} \\ \hline \end{array}$ -Referring to Table 10-2, the researcher was attempting to show statistically that the female MBA graduates have a significantly lower mean starting salary than the male MBA graduates. The proper conclusion for this test is

A) at the ? = 0.10 level, there is sufficient evidence to indicate a difference in the mean starting salaries of male and female MBA graduates. B) at the ? = 0.10 level, there is sufficient evidence to indicate that females have a lower mean starting salary than male MBA graduates. C) at the ? = 0.10 level, there is sufficient evidence to indicate that females have a higher mean starting salary than male MBA graduates. D) at the ? = 0.10 level, there is insufficient evidence to indicate any difference in the mean starting salaries of male and female MBA graduates. A) at the ? = 0.10 level, there is sufficient evidence to indicate a difference in the mean starting salaries of male and female MBA graduates. B) at the ? = 0.10 level, there is sufficient evidence to indicate that females have a lower mean starting salary than male MBA graduates. C) at the ? = 0.10 level, there is sufficient evidence to indicate that females have a higher mean starting salary than male MBA graduates. D) at the ? = 0.10 level, there is insufficient evidence to indicate any difference in the mean starting salaries of male and female MBA graduates.