Nature of data.

Data can be categorized into two main types:

a. Qualitative data

Descriptive in nature. Represents characteristics or attributes that cannot be measured numerically.

Examples: Gender (male/female), colors, types of soil, etc.

Subtypes:

1. Nominal data: Categories without a specific order (e.g., colors: red, green, blue).
2. Ordinal data: Categories with a meaningful order but no precise difference (e.g., ranks: first, second, third).

b. Quantitative data

Numerical in nature. Represents measurable quantities.

Examples: Temperature, rainfall, population size.

Subtypes:

1. Discrete data: Countable and takes specific values (e.g., number of students).
2. Continuous data: Measurable and takes any value within a range (e.g., height, weight).

1. Primary data: Collected firsthand through surveys, experiments, or observations.
2. Secondary data: Obtained from existing records, books, or reports.

1. Accuracy: Data should be precise and free from errors.
2. Relevance: Data should be related to the purpose of the study.
3. Timeliness: Data should be current and collected within the required timeframe.
4. Completeness: Data should cover all aspects of the study without omissions.

1. Observation: Watching and recording phenomena.
2. Interviewing: Asking questions directly to respondents.
3. Questionnaires: Distributing forms for respondents to fill out.
4. Sampling: Selecting a representative group from a larger population.

1. Textual presentation: Using words to describe data.
2. Tabular presentation: Organizing data in rows and columns.
3. Graphical presentation: Using charts and graphs (e.g., bar charts, pie charts).

1. Helps in analyzing spatial patterns and relationships.
2. Facilitates planning and decision-making in areas like resource management, urban planning, and environmental conservation.
3. Enables predictions about future trends such as population growth or climatic changes.