Tools of Economic Analysis


A table is a systematic and orderly arrangement of information, facts, or data, using rows and columns for presentation which make it easier for a better understanding of the relationship between variables. It serves as the most commonly used tool in Economics for economic analysis.


1. Orderly Arrangement of Data: A table should provide a systematic and organized arrangement of data. The data within the table should be structured in a logical manner, with rows and columns used to clearly delineate different categories or variables.

2. Title or Heading: A table must have a clear and concise title or heading that describes the information presented in the table. The title should provide a brief overview of the data being displayed, enabling readers to quickly grasp the purpose and content of the table.

3. Numbering (if applicable): In cases where multiple tables are presented within a document or study, it is important to assign numbers to each table for easy reference. Numbering tables helps readers navigate through the document and locate specific information efficiently.

4. Simplicity and Ease of Understanding: Tables should be designed to be straightforward and easily comprehensible. Complex data should be simplified and presented in a clear manner, using concise labels and appropriate formatting. By avoiding unnecessary complexity, tables enable readers to grasp the information quickly and accurately.

5. Stated Units of Measurement: To ensure clarity and accuracy, tables should include explicit statements regarding the units of measurement used for the data. Whether it is time, distance, weight, or any other relevant quantity, providing the units helps readers interpret the data correctly and facilitates comparisons between different values within the table.

By adhering to these features, tables become powerful tools for effectively presenting data, enhancing communication, and aiding in the understanding and analysis of information.


1. Quick Information Retrieval: Tables are highly effective in revealing information at a glance. By presenting data in a structured format, tables allow readers to quickly scan and extract relevant information without the need for extensive reading or analysis. This makes tables particularly useful when time is limited or when a summary overview is needed.

2. Elimination of Repetition: Tables help avoid redundancy by organizing data in a concise and organized manner. Instead of repeating the same information multiple times in a text format, a table condenses the data into a compact and efficient format. This not only saves space but also enhances readability and clarity.

3. Enhanced Data Comprehension: The tabular format of a table makes complex data more accessible and understandable. By organizing data into rows and columns, tables provide a clear visual representation that enables readers to grasp relationships, patterns, and trends more easily. Tables help simplify complex information, making it more manageable and facilitating better comprehension.

4. Facilitates Data Interpretation: Tables assist in the interpretation of data by presenting it in a structured and standardized format. The clear arrangement of data within a table allows for systematic analysis and comparison. It enables readers to identify patterns, identify outliers, and draw meaningful conclusions from the data presented.

5. Simplifies Data Comparison: Tables are particularly useful when comparing different classes of data. By organizing related information side by side, tables provide a convenient framework for comparing values, trends, or characteristics. The structured layout of a table allows for quick and accurate comparisons, aiding in decision-making, analysis, and identifying similarities or differences between data sets.

Tables serve as powerful visual tools that simplify the presentation, understanding, and analysis of complex data. They condense information, promote clarity, and enable readers to quickly extract valuable insights. By harnessing the advantages of tables, individuals can effectively communicate and make informed decisions based on data-driven information.


The table below gives the performance of four students in an entrance examination.

Students Eng Maths Econs Total
A 60 80 50 190
B 80 40 70 190
C 60 60 80 200
D 40 50 60 150
Total 240 230 260 730


A graph serves as a visual representation that showcases the functional relationship between two variables. It provides a powerful tool for analyzing and interpreting data, allowing for a more comprehensive understanding of complex information. Here are some key uses and benefits of using graphs:

1. Visualizing Relationships: Graphs enable us to visualize relationships between variables, making it easier to identify patterns, trends, and correlations. By plotting data points on a graph, we can observe how changes in one variable impact the other, providing insights into the nature of their relationship.

2. Simplifying Complex Data: Graphs simplify complex data by condensing it into a visual format. They help in presenting large datasets in a more manageable and concise manner, making it easier to grasp the underlying information. Graphs can effectively communicate complex concepts, allowing viewers to quickly absorb and interpret the data.

3. Enhancing Data Interpretation: Graphs aid in the interpretation of data by presenting it in a visual and intuitive way. They provide a clearer representation of trends, patterns, and outliers compared to raw numerical data. By visualizing the data, graphs enable analysts to draw meaningful conclusions and make informed decisions based on the insights derived.

4. Supporting Comparative Analysis: Graphs facilitate comparative analysis by allowing the comparison of multiple variables or data sets simultaneously. By utilizing different types of graphs such as bar graphs, line graphs, scatter plots, or pie charts, analysts can easily identify similarities, differences, and relative proportions between different categories or groups within the data.

5. Communicating Insights: Graphs are highly effective in communicating insights and findings to a wide range of audiences. They can be easily understood by individuals with varying levels of technical expertise, helping to convey complex information in a visually appealing and accessible manner. Graphs enable effective communication of trends, comparisons, and key findings, enhancing the impact of data-driven messages.

In economic analysis, various types of graphs are utilized based on the nature of the data and the intended purpose. Common examples include line graphs to depict trends over time, scatter plots to explore relationships between variables, bar graphs to compare categorical data, and pie charts to represent proportions or percentages. Each type of graph offers specific advantages in analyzing economic data and conveying key insights effectively.

Overall, graphs play a vital role in data analysis, visualization, and communication. By leveraging their visual power, graphs enhance our understanding of data, enable more informed decision-making, and facilitate effective communication of complex information.


1. Title: A graph must have a clear and descriptive title that summarizes the information being presented. The title provides context and helps readers understand the purpose or topic of the graph at a glance.

2. Appropriate Scales: Graphs require appropriate scales on both the x-axis (horizontal) and y-axis (vertical) to accurately represent the data. The scales should be chosen carefully to ensure that the data is proportionally and accurately depicted. The scale units should be evenly spaced and clearly labeled to facilitate accurate interpretation.

3. Different Variables on the Axes: The y-axis and x-axis of a graph should represent different variables. By assigning distinct variables to each axis, the graph can demonstrate the relationship between them. For example, in a scatter plot, the x-axis may represent the independent variable, while the y-axis represents the dependent variable.

4. Source of Data: It is essential to include the source of the data presented in the graph. This allows readers to verify the reliability and credibility of the information. The source can be mentioned in the form of a citation or a note beneath the graph, providing transparency and acknowledging the origin of the data.

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5. Proper Orientation: A graph should be oriented in a standard manner, with the y-axis positioned on the vertical side and the x-axis on the horizontal side. This convention ensures consistency and facilitates easy comprehension across different graphs. The orientation allows for a natural flow of interpretation from left to right and bottom to top.

6. Clear Labels: To enhance understanding, a graph should be well labeled. The axes, data points, and any additional elements such as legends or annotations should be labeled appropriately. Clear labeling helps readers interpret the graph accurately and allows for quick comprehension of the information being presented.

By incorporating these features, a graph becomes a powerful visual tool for effectively conveying information and insights. It ensures clarity, accuracy, and ease of interpretation, enabling viewers to comprehend the relationships, trends, and patterns within the data.


1. Revealing Relationships: One of the key benefits of using a graph is that it visually represents the relationship between two variables. By plotting data points and connecting them with lines or bars, graphs provide a clear and intuitive way to understand how changes in one variable affect another. This helps uncover patterns, trends, and correlations that may not be immediately apparent in tabular form.

2. Enhanced Clarity and Efficiency: Graphs offer a clearer and quicker impression of quantitative information compared to tabular form alone. While tables provide precise numerical data, graphs condense and simplify the information into a visual format, allowing viewers to grasp the overall patterns and trends at a glance. The visual nature of graphs aids in comprehension, especially when dealing with complex or large datasets.

3. Facilitating Interpretation: Graphs assist in the interpretation of values by providing a visual representation. When data is presented in a graph, it becomes easier to identify and understand the values of variables. The visual cues and patterns displayed in a graph make it simpler to draw conclusions, make comparisons, and derive insights from the data. Graphs help translate raw numbers into meaningful information.

4. Understanding and Appreciating Quantities: Graphs help individuals better understand and appreciate the values or quantities presented in a table. By visually representing the data, graphs bring it to life and allow for a more intuitive understanding of the magnitudes and proportions. Seeing the data in a graphical format can make it more relatable and relieves the cognitive burden of solely relying on numerical values.

5. Enabling Variable Comparison: Graphs provide a basis for comparing variables presented in a table. By visually aligning different data sets or categories, graphs make it easier to discern relative magnitudes, trends, or differences between variables. Whether it’s comparing sales figures between different products or analyzing trends over time, graphs offer a visual framework for making effective and meaningful comparisons.

In summary, graphs play a crucial role in data analysis, presentation, and understanding. They enable us to visualize relationships, simplify complex information, interpret values, appreciate quantities, and compare variables. By harnessing the power of graphs, individuals can derive valuable insights, make informed decisions, and effectively communicate data-driven information.


A chart serves as a valuable tool for interpreting and presenting data in a visually engaging manner. By transforming raw data into diagrams or pictures, charts enhance the understanding and analysis of complex information. In the field of economics, various types of charts are utilized for economic analysis, two prominent examples being bar charts and pie charts. Here are some key uses and benefits of using charts:

1. Data Visualization: Charts provide a means to visually represent data, allowing for easier comprehension and interpretation. They condense large amounts of information into a compact and digestible format, making it easier for viewers to grasp key trends, patterns, and comparisons at a glance. By presenting data visually, charts bring the numbers to life and facilitate a deeper understanding of the underlying economic concepts.

2. Comparative Analysis: Charts enable comparative analysis by displaying multiple data sets or categories side by side. For instance, bar charts are often used to compare different variables or groups by representing them as distinct bars along a common axis. This facilitates the identification of relative proportions, trends, and variations, making it easier to draw meaningful conclusions and support decision-making in economics.

3. Trend Analysis: Charts are particularly useful for examining trends over time. Line charts, for example, can showcase the progression or fluctuations of economic indicators, such as GDP growth or unemployment rates. By visually connecting data points, these charts provide insights into the direction, speed, and magnitude of changes, aiding economists in identifying long-term patterns and making predictions.

4. Proportional Representation: Pie charts are effective in representing proportions or percentages of a whole. They visually depict how different components contribute to a total value, such as the distribution of income sources or market share of different companies. Pie charts provide a clear visual representation of relative sizes and proportions, allowing economists to assess market concentration, resource allocation, or income distribution.

5. Communicating Insights: Charts are powerful tools for communicating economic insights to various stakeholders. Their visual nature makes them accessible to a wide range of audiences, enabling effective communication of complex concepts and data-driven messages. Charts can be included in reports, presentations, or publications, enhancing the clarity and impact of economic analysis.

In summary, charts play a vital role in economic analysis by visually representing and interpreting data. They facilitate data visualization, comparative analysis, trend identification, proportional representation, and effective communication of economic insights. By leveraging the capabilities of charts, economists can present information in a more engaging and accessible manner, supporting better decision-making and understanding of economic phenomena.


A bar chart is a visual representation of tabulated data that uses evenly spaced bars with equal gaps to display information. Bar charts are widely used for their simplicity and effectiveness in presenting data. They can be categorized into three main types based on the number of variables they represent:

Simple bar charts are used to display data with one variable. In this type of bar chart, each bar represents a single category or value, and the height or length of the bar corresponds directly to the value of the variable being represented. The bars are typically arranged along the x-axis (horizontal axis) with their corresponding values marked on the y-axis (vertical axis). Simple bar charts are ideal for comparing values within a single dataset or category.

Example: A simple bar chart can be used to represent the sales figures of different products, where each bar represents the sales value of a specific product.

Component bar charts, also known as stacked bar charts, are used for data with two variables. In this type of bar chart, each bar is divided into segments or components, and each segment represents a specific category within the second variable. The length or height of each segment within the bar represents the proportion or contribution of that category to the total value of the first variable. Component bar charts are excellent for comparing parts of a whole and understanding the composition of different variables.

Example: A component bar chart can illustrate the distribution of household expenses, with each bar showing the total expenditure, and each segment representing the proportion spent on categories like rent, groceries, utilities, etc.

Multiple bar charts are used to present data with more than two variables. In this type of bar chart, multiple sets of bars are grouped together, each set representing a different category or group within the data. The bars within each group are positioned side by side, and each bar represents a specific variable. Multiple bar charts are valuable for comparing values across different groups or categories simultaneously.

Example: A multiple bar chart can be employed to compare the revenue of different companies for multiple years, where each group of bars represents the revenue data for each year, and each bar within the group represents the revenue of a specific company.

In summary, bar charts are versatile and widely used graphical tools for representing tabulated data. The three main types of bar charts – simple, component, and multiple – provide effective means of visualizing data with one, two, or more than two variables, respectively. By utilizing bar charts, analysts and decision-makers can gain valuable insights, make comparisons, and communicate information in a clear and accessible manner.

Examples of simple Bar Charts

Score 2 4 6 8 10
Frequency 7 8 9 4 2

Example of Component Bar Charts

Class Boys Girls Total
S. S. 1p 24 18 42
S. S. 1w 17 22 89
Total 41 40 81

Examples of Multiple Bar Charts

Produce line Tunner 2000 200 2002
Cocoa 500 700 200
Cotton 400 300 500
Groundnut 300 400 400

2.    Highlight three types of chart


Pie charts are circular graphical representations used to display data in a way that shows the proportional relationship of different categories or parts to the whole. The circle represents the total data, which is divided into sections expressed in degrees. Each section, also known as a slice, corresponds to a specific category or attribute and is sized proportionately based on its frequency or value relative to the total.

Key features and uses of pie charts

1. Proportional Representation: The primary advantage of pie charts lies in their ability to show the relative proportions of different categories within the total dataset. The size of each slice is directly proportional to the frequency or value it represents, allowing viewers to quickly understand the distribution of data at a glance.

2. Whole-to-Part Visualization: Pie charts effectively illustrate the concept of “parts of a whole.” They show how individual components contribute to the entirety of the data. The complete circle represents the entire dataset, while each slice represents a specific category’s contribution to that total.

3. Comparing Categories: Pie charts are useful for comparing categories based on their respective shares or proportions. It is easy to identify which categories dominate the dataset or which ones are relatively smaller in magnitude.

4. Simple and Intuitive: Pie charts are simple and intuitive to interpret, making them accessible to a wide range of audiences. The circular shape and clear division into slices aid in conveying information without the need for complex numerical analysis.

5. Limited Data: While pie charts are effective for displaying a few categories, they become less efficient when there are many categories or when the differences in proportions are subtle. In such cases, a bar chart or other graphical representations might be more appropriate.

6. Aesthetics and Visual Appeal: Properly designed pie charts can be visually appealing and engaging. By using different colors or patterns for each slice, the chart becomes more aesthetically pleasing and easier to understand.

It is worth noting that drawing pie charts manually with a compass and protractor can be time-consuming and impractical, especially when dealing with large datasets or complex data. Nowadays, various software tools and spreadsheet applications make it easy to create accurate and visually appealing pie charts with just a few clicks.

In conclusion, pie charts provide a valuable visual tool for understanding the distribution and proportions of data. When used appropriately, they offer an intuitive representation of how different categories contribute to the whole, making it easier to analyze and communicate data-driven insights.

Example: In a school, the number of periods for the subjects are given in a table. Represent the information in a pie chart.

English 9 periods
Mathematics 6 Periods
Science 5 Periods
Others 20 Periods


Subjects Periods Working Degree
English 9 9/40 x 3600 810
Mathematics 6 6/40 x 3600 540
Science 5 5/40 x 3600 450
Others 20 20/40 x 3600 1800
40 3600


A linear graph is a graphical representation used to compare two variables, displaying a straight-line relationship between them. In a linear graph, the x-axis typically represents the independent variable, while the y-axis represents the dependent variable. The straight-line nature of the graph indicates that the relationship between the two variables is linear, meaning that as one variable changes, the other changes at a constant rate.

Key characteristics and uses of linear graphs

1. Direct Relationship: A linear graph illustrates a direct relationship between the two variables. As the independent variable increases or decreases, the dependent variable changes proportionally, resulting in a straight-line pattern on the graph. This relationship is commonly described by a linear equation, such as y = mx + b, where ‘m’ represents the slope (rate of change) of the line, and ‘b’ is the y-intercept (the value of the dependent variable when the independent variable is zero).

2. Simple Representation: Linear graphs are straightforward and easy to interpret. Their simplicity allows viewers to quickly understand the relationship between the variables and make predictions based on the graph’s trend.

3. Predictive Power: Linear graphs allow for predictions to be made within the range of the data. By identifying the slope of the line, one can determine how the dependent variable is expected to change for each unit change in the independent variable. This predictive power is useful in making forecasts or extrapolating data beyond the observed values.

4. Slope and Intercept Analysis: The slope of the linear graph represents the rate of change of the dependent variable concerning the independent variable. A positive slope indicates a positive correlation, while a negative slope indicates a negative correlation between the variables. The y-intercept, where the line crosses the y-axis, represents the value of the dependent variable when the independent variable is zero.

5. Trend Identification: Linear graphs help identify trends and patterns in the data. An upward-sloping line indicates a positive relationship, while a downward-sloping line signifies a negative relationship. A flat line indicates a lack of correlation between the variables.

6. Linear Regression: Linear graphs are often used in statistical analysis, specifically in linear regression, to model and quantify the relationship between the variables. Linear regression is a powerful tool for estimating the parameters of the linear equation and making predictions based on the observed data.

In conclusion, linear graphs provide a valuable visual representation of the relationship between two variables, showcasing a straight-line pattern that indicates a direct and proportional correlation. Their simplicity and predictive capabilities make them essential tools in various fields, such as economics, physics, engineering, and social sciences, for analyzing data, identifying trends, and making informed decisions based on the relationship between variables.


Given that Q = 42 – 2p find the value of Q when p = N1, N3, N5, N7, and N9, where P = Price and Q = Quantities. Represent the information on a line graph 


Q = 42 – 21


P = N1, Q = 42 – 2 x N1 – 42 – N2 = N40

P = N3, Q = 42 – 2 x N3 = 42 – N6 = N36

P = N5, Q = 42 – 2 x N5 = 42 – N10 = N32

P = N7, Q = 42 x 2 x N7 = 42 – N14 = N28

P = N9, Q = 42 – 2x N9 = 42 – N18 = N24

Price (N)                                             Quantity (Units)

     1                                                                 40

     3                                                                 56

     5                                                                 32

     7                                                                 28

     9                                                                 24

Item                                         Amount

Agric                                              70

Sports                                            80

Education                                     110

Health                                            60

Development                                 30

Finance                                            50

Read also:

Economic Problems: What, How & Whom to produce

Opportunity Cost

Concepts of Economics

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Economics: Meaning, Scope, Branches & Study

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