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1. Classifying as Qualitative (categorical) or Quantitative (numerical):
1. Color of a ball - Qualitative (categorical)
2. Breed of a dog - Qualitative (categorical)
3. Height of a person - Quantitative (numerical), Continuous
4. Number of pets owned - Quantitative (numerical), Discrete
5. Shoe size - Quantitative (numerical), Discrete
6. Weight of a doll - Quantitative (numerical), Continuous
7. Marital status - Qualitative (categorical)
8. Number of years of education - Quantitative (numerical), Discrete
9. Age of a person - Quantitative (numerical), Continuous
10. Gender (male or female) - Qualitative (categorical)
2. Determining the level of measurement:
1. Annual income of teachers - Ratio
2. BMI (Body Mass Index) of a student - Ratio
3. Skin color - Nominal
4. Class officers' position - Ordinal
5. Eye color - Nominal
These classifications are based on the nature of the data and the level of information provided by each variable. Understanding these distinctions can help in correctly analyzing and interpreting data in different contexts.