A recent national poll showed the reading habits of adults during the last 12 months: $$\begin{array} { l r }
3 \text { or more books } & 5 \
2 \text { boxlss } & 27 \
1 \text { book } & 38 \
\text { No hooks } & 30
\end{array}$$ A poll in a small Midwestern town shows the following percentages:
$$\begin{array} { l r }
3 \text { or more broks } & 1 \
2 \text { books } & 22 \
1 \text { boxk } & 39 \
\text { No hooks } & 38
\end{array}$$ Comparing the critical and observed values of the goodness-of fit chi-squared statistic and
$\alpha$ = 0.05, the appropriate decision is ______.

Question

A recent national poll showed the reading habits of adults during the last 12 months: $$\begin{array} { l r } 
3 \text { or more books } & 5 \
2 \text { boxlss } & 27 \
1 \text { book } & 38 \
\text { No hooks } & 30
\end{array}$$ A poll in a small Midwestern town shows the following percentages:
$$\begin{array} { l r } 
3 \text { or more broks } & 1 \
2 \text { books } & 22 \
1 \text { boxk } & 39 \
\text { No hooks } & 38
\end{array}$$ Comparing the critical and observed values of the goodness-of fit chi-squared statistic and
$\alpha$ = 0.05, the appropriate decision is ______.

Quizplus · Accepted Answer

The question does not provide the actual chi-squared statistic or the critical value needed to make a decision based on a chi-squared test. However, the correct approach to this type of question involves comparing the observed frequencies (from the small Midwestern town) with the expected frequencies (from the national poll) to see if there is a significant difference. Without the specific values, we can't calculate the chi-squared statistic here, but the correct decision in the context of hypothesis testing would be to either reject or fail to reject the null hypothesis that the two distributions are equal. Since the options provided do not allow for a calculation-based decision, the most appropriate answer based on the information given would be to "fail to reject the null hypothesis that the two distributions are equal," assuming we do not have evidence strong enough to prove a significant difference.