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Categorical variables are pivotal in data analysis, representing non-numeric groups like nationality or brand preference. They are classified as nominal or ordinal, with the former lacking a natural order and the latter having a ranked sequence. Understanding these variables is key to qualitative data analysis, requiring specific collection and analytical methods to interpret.
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Categorical variables are non-numerical data that can be divided into distinct groups or categories
Quantitative Data
Quantitative data includes numerical values that can be counted or measured, while categorical data does not involve numerical measurement
Continuous Data
Continuous data represents measurements that can take on any value within a given range
Categorical data is simple to collect and analyze, but can oversimplify responses and requires large sample sizes for reliable analysis
Categorical data is commonly collected through surveys or questionnaires with multiple-choice questions
Calculation of Frequencies and Proportions
Frequencies and proportions are calculated to analyze categorical data
Visual Representations
Bar graphs, pie charts, and mosaic plots are used to visually represent categorical data
Cross-tabulation and statistical tests, such as the chi-square test, are used to assess relationships between categorical variables
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Unlike ______ data, which deals with quantities, categorical data includes attributes like nationality or brand preference that can't be measured.
quantitative
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Quantitative Data Subtypes
Includes discrete and continuous data; discrete is countable, continuous allows any value within range.
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Example of Discrete Data
Number of students in a class; countable, distinct values.
