Interpreting Contingency Tables and Their Distributions

This video tutorial explains how to interpret data from contingency tables using side-by-side bar graphs and calculations of marginal and conditional distributions. It demonstrates how to analyze associations between variables, such as political party and gender, using percentages. The video emphasizes the importance of visual aids and calculations in understanding survey data.

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