To determine which generalization is most accurate based on the given table, let's analyze the given data:
[tex]\[
\begin{tabular}{|c|c|c|}
\hline
Gender & \text{Percent Supporting Obama} & \text{Percent Supporting Romney} \\
\hline
Men & 42\% & 52\% \\
\hline
Women & 53\% & 43\% \\
\hline
\end{tabular}
\][/tex]
- For Men:
- Percent supporting Obama: [tex]\(42\%\)[/tex]
- Percent supporting Romney: [tex]\(52\%\)[/tex]
- For Women:
- Percent supporting Obama: [tex]\(53\%\)[/tex]
- Percent supporting Romney: [tex]\(43\%\)[/tex]
Now let's evaluate each statement:
1. Women are more likely than men to support Democrats (Obama):
- We compare the support for Obama between men and women.
- [tex]\(53\%\)[/tex] of women support Obama, which is higher than [tex]\(42\%\)[/tex] of men who support Obama.
- This statement is accurate based on the data.
2. Women are more likely than men to support Republicans (Romney):
- We compare the support for Romney between men and women.
- [tex]\(43\%\)[/tex] of women support Romney, which is lower than [tex]\(52\%\)[/tex] of men who support Romney.
- This statement is not accurate based on the data.
3. Men voted in higher numbers than women:
- The table provides percentages, not the number of votes, so we cannot determine the actual numbers of votes cast by men and women from the given data.
- This statement cannot be verified based on the provided data.
4. Men are more likely than women to support Democrats (Obama):
- We compare the support for Obama between men and women.
- [tex]\(42\%\)[/tex] of men support Obama, which is lower than [tex]\(53\%\)[/tex] of women who support Obama.
- This statement is not accurate based on the data.
Based on the analysis above, the most accurate generalization is:
"Women are more likely than men to support Democrats (Obama)."
