Two-Way Tables & Relative Frequency

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What is a two-way table?

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A two-way table is a statistical table that displays the frequency distribution of two categorical variables, allowing for the analysis of the relationship between them.

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What is relative frequency?

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Relative frequency is the ratio of the number of times an event occurs to the total number of trials or observations, often expressed as a percentage.

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How do you calculate relative frequency from a two-way table?

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To calculate relative frequency, divide the frequency of a specific category by the total frequency of all categories, then multiply by 100 to get a percentage.

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What does it mean if a two-way table shows a higher relative frequency for one category?

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It indicates that the event or characteristic represented by that category occurs more frequently compared to others in the dataset.

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What is the purpose of using two-way tables in surveys?

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Two-way tables help summarize and analyze the relationship between two categorical variables, making it easier to identify patterns and trends.

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How can you determine which group preferred a certain option using a two-way table?

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By comparing the relative frequencies of the groups for that option, you can identify which group has the highest preference.

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What is the significance of the total row and column in a two-way table?

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The total row and column provide the overall counts for each category, which are essential for calculating relative frequencies.

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How do you interpret the results of a two-way table?

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Interpretation involves analyzing the frequencies and relative frequencies to draw conclusions about the relationships between the variables.

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What is a categorical variable?

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A categorical variable is a type of variable that can take on one of a limited, fixed number of possible values, representing different categories or groups.

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What is an example of a two-way table?

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An example could be a table showing the number of students who like different types of movies (Action, Comedy, Drama) categorized by gender (Male, Female).