Classify the data into qualitative or quantitative, then into a subcategory of nominal, ordinal, discrete or continuous: year of birth, levels of fluency (language), height of play... Classify the data into qualitative or quantitative, then into a subcategory of nominal, ordinal, discrete or continuous: year of birth, levels of fluency (language), height of players on a team, Cricket team jersey number, political party, university GPA, egg size (small, medium, large, extra large).
Understand the Problem
The question is asking to classify various types of data into qualitative or quantitative categories and further into nominal, ordinal, discrete, or continuous subcategories. Each item listed needs to be analyzed based on these statistical classifications.
Answer
Year of birth: Quantitative, Discrete; Levels of fluency: Qualitative, Ordinal; Height: Quantitative, Continuous; Jersey number: Qualitative, Nominal; Political party: Qualitative, Nominal; GPA: Quantitative, Continuous; Egg size: Qualitative, Ordinal.
["Year of birth: Quantitative, Discrete","Levels of fluency (language): Qualitative, Ordinal","Height of players on a team: Quantitative, Continuous","Cricket team jersey number: Qualitative, Nominal","Political party: Qualitative, Nominal","University GPA: Quantitative, Continuous","Egg size (small, medium, large, extra large): Qualitative, Ordinal"]
Answer for screen readers
["Year of birth: Quantitative, Discrete","Levels of fluency (language): Qualitative, Ordinal","Height of players on a team: Quantitative, Continuous","Cricket team jersey number: Qualitative, Nominal","Political party: Qualitative, Nominal","University GPA: Quantitative, Continuous","Egg size (small, medium, large, extra large): Qualitative, Ordinal"]
More Information
This classification helps in analyzing data correctly depending on whether it involves measurements or categorical differentiation.
Tips
A common mistake is categorizing discrete data as continuous or failing to recognize the ordinal nature of certain qualitative data.
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