Interpreting Contingency Tables and Their Distributions

What is one method mentioned for interpreting data from contingency tables?

• Creating line graphs

• Using scatter plots

• Drawing side-by-side bar graphs

• Using pie charts

What does marginal distribution help us understand?

• The relationship between two variables

• The distribution of a specific category

• Percentages calculated from the margins of the table

• The total number of people surveyed

How do you calculate the percentage of a category in marginal distribution?

• Divide the category count by the total count and multiply by 100

• Subtract the category count from the total count

• Multiply the category count by the total count

• Add the category count to the total count

What does conditional distribution focus on?

• The entire sample

• The margins of the table

• A specific category or categories

• The total number of people surveyed

In the example given, what percentage of independent voters are male?

• 37.3%

• 45.7%

• 31.4%

• 62.7%

What percentage of independent voters are female in the given example?

• 62.7%

• 45.7%

• 31.4%

• 37.3%

What does the example of independent voters illustrate about conditional distributions?

• They provide insight into how categories are broken up

• They show the total number of people surveyed

• They are the same as marginal distributions

• They focus on the margins of the table

What is the conditional distribution of male Republicans in the given example?

• 33.3%

• 35%

• 22.9%

• 31.7%

What is the conditional distribution of female Republicans in the given example?

• 35%

• 22.9%

• 45.7%

• 31.4%

What conclusion can be drawn if the percentages of conditional distributions are different?

• There is no association between the variables

• The sample size is too small

• The data is incorrect

• There is an association between the variables