The number of drunk-driving arrests were counted each month by the Millville police department. The results are given in the following table.

$\begin{array} { c c } \hline \text { Month } & \begin{array} { c } \text { Number of } \\\text { Drunk-Driving } \\\text { Arrests }\end{array} \\\hline \text { January } & 24 \\\text { February } & 21 \\\text { March } & 13 \\\text { April } & 11 \\\text { May } & 13 \\\text { June } & 11 \\\text { July } & 10 \\\text { August } & 13 \\\text { September } & 14 \\\text { October } & 14 \\\text { November } & 13 \\\text { December } & 25 \\\hline\end{array}$

Test the hypothesis that drunk-driving arrests are equally likely to occur in any month. Use the $a$ = 0.05 level Of significance.

Question

The number of drunk-driving arrests were counted each month by the Millville police department. The results are given in the following table.

\begin{array} { c c } \hline \text { Month } & \begin{array} { c } \text { Number of } \\\text { Drunk-Driving } \\\text { Arrests }\end{array} \\\hline \text { January } & 24 \\\text { February } & 21 \\\text { March } & 13 \\\text { April } & 11 \\\text { May } & 13 \\\text { June } & 11 \\\text { July } & 10 \\\text { August } & 13 \\\text { September } & 14 \\\text { October } & 14 \\\text { November } & 13 \\\text { December } & 25 \\\hline\end{array}

Test the hypothesis that drunk-driving arrests are equally likely to occur in any month. Use the

a

= 0.05 level Of significance.

Quizplus · Accepted Answer

To test the hypothesis that drunk-driving arrests are equally likely to occur in any month, we would typically use a chi-square test for goodness of fit. The null hypothesis (H0) is that the arrests are equally likely across all months. Given the data, the variation in the number of arrests from month to month does not seem extreme enough to reject the null hypothesis at the 0.05 level of significance without performing the actual chi-square calculation. The choice to not reject the null hypothesis is based on the understanding that fluctuations in the data do not necessarily indicate a significant deviation from the expected uniform distribution, especially without the chi-square statistic being calculated and compared to the critical value.

The Number of Drunk-Driving Arrests Were Counted Each Month by the Millville