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A Guide to Scatter Diagram in PMP

Scatter diagrams, a fundamental tool in project management, offer a visual representation of the relationship between two variables. These diagrams are pivotal in planning and monitoring operations, especially when addressing quality-related issues within an organization. Unlike typical charts that use lines or bars, scatter diagrams employ dots to represent data points, providing a clear and straightforward depiction of correlations.

What is a Scatter Diagram?

A scatter diagram is essentially a graph that illustrates the association between two variables using a collection of numerical data. It plots one variable along the x-axis and another along the y-axis, revealing patterns or correlations. For instance, it can show how a change in a process component (independent variable) impacts a quality fault (dependent variable), aiding in process optimization. The beauty of scatter diagrams lies in their simplicity and their ability to visually represent complex data in an easily digestible format.

Applications of Scatter Plots

Scatter plots are versatile tools used across various fields for analyzing and interpreting data. Here are some key applications:

Quality Control and Improvement: In manufacturing and production, scatter plots are used to identify relationships between different variables affecting product quality. For example, they can help determine if there’s a correlation between the temperature of a machine and the incidence of defects in the products it produces.

Healthcare Analysis: In the healthcare sector, scatter plots are instrumental in studying the relationship between lifestyle factors and health outcomes. For instance, a scatter plot can be used to analyze the correlation between exercise frequency and blood pressure levels among patients.

Market Research: Businesses often use scatter plots to understand customer behavior and preferences. By plotting data such as customer age against spending habits, companies can identify trends and target specific market segments more effectively.

Environmental Studies: Scatter plots are used in environmental science to study the relationships between various environmental factors. For example, researchers might use a scatter plot to examine the relationship between air pollution levels and respiratory health issues in a community.

Education and Research: In educational research, scatter plots can help in understanding the relationship between study habits and academic performance. By plotting the number of hours students spend studying against their grades, educators can gain insights into the effectiveness of different study strategies.

Financial Analysis: In finance, scatter plots are used to analyze the relationship between different economic variables. For example, they can show the correlation between interest rates and stock market performance, helping investors make informed decisions.

Engineering and Design: Engineers use scatter plots to analyze the relationship between design parameters and performance outcomes. This can involve studying how changes in a component’s dimensions affect the overall efficiency of a machine or system.

Sports Science: In sports, scatter plots can be used to analyze the relationship between training methods and performance outcomes. Coaches and athletes can use these insights to optimize training regimens for better performance.

Social Science Research: Scatter plots are valuable in social sciences for studying relationships between social factors. For instance, they can be used to analyze the correlation between education levels and income across different demographic groups.

Agricultural Studies: In agriculture, scatter plots help in understanding the relationships between various factors like soil quality, rainfall, and crop yield. This information is crucial for optimizing farming practices for better yield and sustainability.

Types of Scatter Diagrams

Positive Correlation: This occurs when an increase in one variable leads to an increase in the other. For example, a scatter plot might show that as the number of hours spent studying increases, so does the grade achieved in an exam.

Negative Correlation: Here, an increase in one variable results in a decrease in the other. A classic example is the relationship between outdoor temperature and heating costs; as the temperature goes up, heating costs go down.

No Correlation: Sometimes, two variables show no relationship. For instance, a scatter plot might reveal no correlation between the color of a car and its fuel efficiency.

Advantages and Disadvantages of Scatter Diagrams


  • Visual Clarity: Scatter diagrams provide a clear visual representation of the relationship between two variables. This makes it easier to identify patterns and trends in the data.
  • Identification of Correlations: They are excellent for showing whether a correlation exists between variables, and if so, whether it’s positive, negative, or non-existent.
  • Simplicity and Ease of Use: Creating a scatter plot is relatively straightforward, and they can be easily interpreted by those who are not experts in statistical analysis.
  • Revealing Outliers: Scatter diagrams are useful for spotting outliers or anomalies in data, which might indicate errors or special cases worth further investigation.
  • Non-linear Pattern Recognition: They can show a wide variety of pattern types, not just linear relationships, but also more complex non-linear patterns that might be missed with other types of graphs.
  • Predictive Analysis: In some cases, scatter plots can be used to make predictions about future trends based on the observed data patterns.


  • Over-Simplification: Scatter diagrams can sometimes oversimplify complex relationships, leading to misinterpretation of the data.
  • Limited to Two Variables: They can only compare two variables at a time. This limitation means they cannot be used to analyze more complex relationships involving multiple variables.
  • Correlation vs. Causation: A common misinterpretation with scatter plots is confusing correlation with causation. Just because two variables have a relationship, it doesn’t mean one causes the other.
  • Difficulty with Large Data Sets: When dealing with very large data sets, scatter plots can become cluttered and hard to interpret, especially if many data points overlap.
  • Subjectivity in Interpretation: The interpretation of scatter plots can be somewhat subjective; different people might draw different conclusions from the same plot.
  • No Quantitative Measure: Scatter diagrams do not provide a quantitative measure of the strength of the relationship between variables.

Scatter Diagram Example

Consider a workplace scenario where we want to understand the relationship between the number of shift hours and the occurrence of accidents. By plotting these two variables on a scatter diagram, we can observe if there’s a correlation. For instance, a positive correlation might be indicated if the diagram shows that more accidents occur as the number of shift hours increases.

Scatter Diagrams in Project Management Professional (PMP)

For those pursuing Project Management Professional (PMP) certification, understanding scatter diagrams is crucial. These diagrams are not just theoretical concepts but practical tools that can provide insights into project data, aiding in making informed decisions.

Creating a Scatter Diagram

Creating a scatter diagram is a straightforward process that involves plotting two variables against each other on a graph to determine if there is a relationship between them. Here’s a brief overview of the steps involved:

Identify the Variables: First, determine the two variables you want to analyze. One will be the independent variable (typically plotted on the x-axis), and the other will be the dependent variable (plotted on the y-axis).

Collect Data: Gather the data for both variables. This data should be paired; each value of the independent variable should correspond to a value of the dependent variable.

Choose a Scale: Decide on the scale for each axis. The scale should cover the range of your data and be divided into equal intervals for accuracy.

Plot the Data Points: For each pair of values, find the corresponding position on the graph and plot a dot. For example, if your independent variable value is 5 and your dependent variable value is 3, find 5 on the x-axis and 3 on the y-axis and plot a dot where these two values intersect.

Draw the Scatter Diagram: Once all data points are plotted, you will have your scatter diagram. The pattern of the dots will give you an initial visual indication of any correlation between the variables.

Analyze the Diagram: Look for patterns in the dots. If they seem to form a line or curve, there may be a correlation. The slope of this line/curve can indicate the nature of the relationship (positive, negative, or no correlation).

Based on the pattern, draw conclusions about the relationship between the variables. Remember, correlation does not imply causation, so be cautious about making causal inferences.

Report Findings: Finally, present your scatter diagram with a clear explanation of what it shows, often including it in reports or presentations to support your analysis.


In conclusion, scatter diagrams are an invaluable tool in data analysis, offering a straightforward and visually intuitive method to explore and understand the relationships between two variables. Their simplicity in design and ease of interpretation make them accessible to a wide range of users, from professionals in various fields to students and researchers.

The primary strength of scatter diagrams lies in their ability to provide clear visual evidence of the correlation (or lack thereof) between variables. This can be particularly useful in identifying trends, patterns, and outliers, which are crucial for making informed decisions in areas such as quality control, market research, healthcare, and environmental studies. However, it’s important to use scatter diagrams judiciously, keeping in mind their limitations such as the potential for oversimplification, the inability to handle more than two variables at a time, and the risk of confusing correlation with causation.

When creating a scatter diagram, the process involves selecting appropriate variables, collecting and plotting data, and then analyzing the resulting graph to draw conclusions. While interpreting these diagrams can be somewhat subjective, they are a powerful tool when used in conjunction with other statistical analysis methods.