Grade10 Economics|| Statistics in Economics||Notes

1. Meaning of Statistics

Statistics has two common meanings:

a) In Singular Sense (Statistical Methods):
Statistics refers to the science and methods used for collecting, presenting, analyzing, and interpreting numerical data to make decisions.

b) In Plural Sense (Statistical Data):
Statistics refers to the numerical facts themselves, which are affected by many causes, expressed in numbers, collected systematically, and related to each other for a specific purpose.

Key Definition: According to Croxton and Cowden, "Statistics may be defined as the collection, presentation, analysis, and interpretation of numerical data."

2. Scope of Statistics

The scope of statistics is very wide. It includes:

• Statistical Methods: The techniques used for data handling.
• Applied Statistics: The use of statistical methods in fields like Economics, Business, Education, and Science.
• Descriptive Statistics: Describing the main features of collected data.
• Inferential Statistics: Making predictions or inferences about a population based on a sample.

Is Statistics a Science or an Art?

• As a Science: It is a systematic body of knowledge with its own laws and principles used to establish relationships.
• As an Art: It is the practical application of scientific knowledge to solve real-world problems in various fields.

3. Limitations of Statistics

Despite its importance, statistics has limitations:

1. It studies only quantitative data, not qualitative facts like beauty, honesty, or intelligence.
2. It studies aggregates (groups), not individual items.
3. Statistical results are true only on average and might not be applicable to every single case.
4. It requires homogeneous data. Comparing different types of data can lead to wrong conclusions.
5. Statistics can be misused by an unskilled person or for misleading purposes.
6. It is only a tool, not a solution in itself.

4. Functions/Importance of Statistics

Statistics is essential because it:

1. Simplifies Complexity: Presents large, complex data in a simple, understandable form (through tables, graphs).
2. Facilitates Comparison: Allows easy comparison between different sets of data.
3. Formulates Policies: Helps governments and businesses formulate policies (economic, industrial, etc.).
4. Forecasts Trends: Helps in predicting future trends (e.g., price rise, demand).
5. Tests Hypothesis: Used to test the validity of assumptions or claims.
6. Measures Uncertainty: Helps in measuring risks in business and economics (insurance, investment).

5. Importance of Statistics in Economics

Statistics is the backbone of economics. Its importance in various economic areas includes:

1. Consumption: To study patterns of consumer spending and demand.
2. Production: To decide what, how much, and how to produce.
3. Exchange: To study prices, markets, imports, and exports.
4. Distribution: To analyze wages, rent, interest, and profit.
5. Public Finance: To prepare government budgets, plan taxation, and public expenditure.
6. Planning: Essential for formulating and monitoring economic plans (like Nepal's Five-Year Plans).
7. Economic Theories: Helps in forming, testing, and validating economic laws and theories.

6. Collection of Data

Definition: The first step in any statistical inquiry is the systematic gathering of information (facts and figures) relevant to the study. This is called collection of data.

Types of Data Based on Source:

• Primary Data: Data collected for the first time, originally by the investigator. (e.g., conducting your own survey).

1. Methods: Direct Personal Interview, Indirect Oral Interview, Information from Local Sources, Mailed Questionnaire, Schedules filled by Enumerators.

• Secondary Data: Data that has already been collected by someone else for their own purpose and is used by another investigator. (e.g., using census data published by CBS).

1. Sources: Published (government reports, journals, websites) and Unpublished (records of institutions).

7. Methods of Data Collection

1. Census Method (Complete Enumeration):

1. Data is collected from every single unit of the population.
2. Advantages: Highly accurate and reliable.
3. Disadvantages: Costly, time-consuming, and not suitable for large populations.

2. Sampling Method:

1. Data is collected from only a selected part (sample) of the population, and conclusions are drawn for the whole.
2. Advantages: Saves time, cost, and effort.
3. Disadvantages: Less accurate if the sample is not properly selected; conclusions may not be 100% true for the population.

8. Classification and Tabulation of Data

a) Classification:

• Definition: The process of arranging raw data into groups or classes based on common characteristics.
• Objectives: To simplify complexity, to facilitate comparison, and to make data suitable for analysis.
• Types of Classification:

1. Geographical/ Spatial: Based on place (e.g., data by province).
2. Chronological/ Temporal: Based on time (e.g., data by year).
3. Qualitative: Based on attributes or qualities (e.g., gender, literacy).
4. Quantitative: Based on numerical values (e.g., income, age).

b) Tabulation:

• Definition: The systematic presentation of classified data in rows and columns of a table.
• Objectives: To organize data compactly, to simplify presentation, and to facilitate comparison and analysis.
• Types of Tables:

1. Simple/ One-way Table: Shows data according to one characteristic.
2. Two-way Table: Shows data according to two characteristics.
3. Three-way Table: Shows data according to three characteristics.

9. Diagrammatic and Graphical Presentation of Data

Data is presented visually to make it easily understandable and attractive.

A. Diagrams:

• Bar Diagrams:
• Simple Bar Diagram: Compares different values of a single variable.
• Multiple Bar Diagram: Compares two or more related variables simultaneously.
• Sub-divided/Component Bar Diagram: Shows different parts of a total value.
• Percentage Bar Diagram: Shows the percentage contribution of different components to the total (each bar is 100%).

Pie Diagram (Pie-chart): A circle divided into sectors, where each sector's area is proportional to the value it represents. The total angle is 360°.

B. Graphs:

1. Histogram: A graphical representation of a continuous frequency distribution using adjacent rectangles. The area of each rectangle is proportional to the frequency.
2. Frequency Polygon: Created by joining the mid-points of the tops of the rectangles in a histogram. The starting and ending points are joined to the base line.
3. Frequency Curve: A smooth free-hand curve drawn through the points of a frequency polygon.
4. Ogive (Cumulative Frequency Curve): A graph of a cumulative frequency distribution.

'Less than' Ogive: Plots upper limits against 'less than' cumulative frequencies.

'More than' Ogive: Plots lower limits against 'more than' cumulative frequencies.

10. Key Statistical Terms

• Variable: A characteristic that can take different values (e.g., age, income).
• Discrete Variable: Takes only whole number values (e.g., number of students).
• Continuous Variable: Can take any value within a range, including fractions (e.g., height, weight).
• Frequency Distribution: A table that shows how often each value (or range of values) of a variable occurs.
• Class Interval: The range of values grouped together in a frequency distribution (e.g., 10-20).
• Inclusive Class: Both limits are included (e.g., 10-19).
• Exclusive Class: The upper limit of one class is the lower limit of the next (e.g., 10-20, 20-30).
• Cumulative Frequency: The total frequency up to a certain point in a distribution.