To discover whether the region where someone lives affects whether or not they have health insurance, you need a table where each region has its own set of conditional percentages for insurance status (insured vs uninsured) that sum to 100% within that region. In other words, the table must show row percentages by region , not overall percentages and not percentages by insurance status.

What the “appropriate” table must do

You are treating:

  • Explanatory variable: Region (Northeast, Midwest, South, West)
  • Response variable: Insurance status (Insured, Uninsured)

To see whether region affects insurance status, you compare the distribution of insurance status within each region. That means:

  • For each region, compute
    \text{Percent uninsured in that region}=\frac{\text{# uninsured in that region}}{\text{total people in that region}}\times 100%
    \text{Percent insured in that region}=\frac{\text{# insured in that region}}{\text{total people in that region}}\times 100%

  • In the correct conditional table, each row (each region) should add to 100% (up to rounding).

So the appropriate table is the one where:

  • Rows = regions.
  • Columns = uninsured, insured, and possibly a row total.
  • Entries for uninsured and insured are row percentages , not raw counts, and each region’s percentages sum to 100%.

An example of the correct structure (HTML as requested):

html

<table>
  <thead>
    <tr>
      <th>Region</th>
      <th>Uninsured (%)</th>
      <th>Insured (%)</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>Northeast</td>
      <td>...</td>
      <td>...</td>
    </tr>
    <tr>
      <td>Midwest</td>
      <td>...</td>
      <td>...</td>
    </tr>
    <tr>
      <td>South</td>
      <td>...</td>
      <td>...</td>
    </tr>
    <tr>
      <td>West</td>
      <td>...</td>
      <td>...</td>
    </tr>
  </tbody>
</table>

If you are given several candidate tables (say “Table A, Table B, Table C”), the correct choice is:

  • The table in which the percentages are computed within each region (row percentages), not within each insurance category and not using the grand total as the denominator.

If you paste the candidate tables, I can point out explicitly which one it is and why.