Amedo Florence
Mar 10
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Mar 10
1. Qualitative data: This type of data describes qualities or characteristics and cannot be measured numerically. Examples include colors, shapes, and emotions.
2. Quantitative data: This type of data consists of numerical values that can be measured and analyzed. It can be further divided into two subtypes:
- Discrete data: Consists of whole numbers or counts, such as the number of students in a class.
- Continuous data: Consists of measurements that can be broken down into smaller units, such as weight or height.
3. Categorical data: This type of data represents categories or groups and can be further divided into two subtypes:
- Nominal data: Consists of categories with no inherent order, such as colors or types of fruit.
- Ordinal data: Consists of categories with a specific order or ranking, such as education levels or customer satisfaction ratings.
4. Time series data: This type of data is collected over a period of time and can be used to analyze trends and patterns. Examples include stock prices, weather data, and sales figures.
5. Spatial data: This type of data is related to geographical locations and can be used to analyze patterns and relationships in a specific area. Examples include maps, GPS coordinates, and satellite images.
2. Quantitative data: This type of data consists of numerical values that can be measured and analyzed. It can be further divided into two subtypes:
- Discrete data: Consists of whole numbers or counts, such as the number of students in a class.
- Continuous data: Consists of measurements that can be broken down into smaller units, such as weight or height.
3. Categorical data: This type of data represents categories or groups and can be further divided into two subtypes:
- Nominal data: Consists of categories with no inherent order, such as colors or types of fruit.
- Ordinal data: Consists of categories with a specific order or ranking, such as education levels or customer satisfaction ratings.
4. Time series data: This type of data is collected over a period of time and can be used to analyze trends and patterns. Examples include stock prices, weather data, and sales figures.
5. Spatial data: This type of data is related to geographical locations and can be used to analyze patterns and relationships in a specific area. Examples include maps, GPS coordinates, and satellite images.
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