A scatter diagram, also known as a scatter plot or scatter graph, is a graphical representation of data points in a two-dimensional coordinate system. It is used to visualize the relationship between two continuous variables, showing how they are distributed and whether there is any pattern or correlation between them. Each data point on the scatter diagram represents the values of both variables, with one variable plotted on the x-axis and the other on the y-axis.
The Prupose of Scater Diagram
The main purpose of using scatter diagrams is to identify the nature of the relationship between two variables. Here are some common reasons why we use scatter plots:
- Visualizing correlation: Scatter plots help to assess the strength and direction of the relationship between two variables. If the points on the plot tend to form a pattern, it indicates that the variables may be correlated.
- Detecting outliers: Scatter diagrams can quickly reveal outliers, which are data points that deviate significantly from the general pattern. Outliers can have a substantial impact on statistical analyses and might require special consideration.
- Identifying patterns: By observing the distribution of data points on the plot, you can identify any underlying patterns, clusters, or trends in the data.
- Checking assumptions: Scatter diagrams are commonly used in regression analysis to check assumptions, such as linearity and homoscedasticity (constant variance of residuals).
- Comparing data sets: When you have two sets of data, plotting them on the same scatter diagram can help to visually compare their relationships.
Scatter Diagram and Regression Analysis
Scatter diagrams are often used in regression analysis to visually assess the relationship between variables and check assumptions.
Scatter Diagram Correlation Patterns
| Correlation Pattern | X / Y Values |
| Strong Positive Correlation | The value of Y increases slightly as the value of X increases. e.g Height vs. Weight: as height increases, weight tends to increase as well |
| Strong Negative Correlation | The value of Y increases as the value of X increases. e.g Temperature vs Ice Cream Sales: on hot days, ice cream sales tend to be higher |
| Weak Positive Correlation | The value of Y increases slightly as the value of X increases. e.g Height vs. Weight : as height increases, weight tends to increase as well |
| Weak Negative Correlation | The value of Y decreases slightly as the value of X increases. e.g Commuting to work daily vs job satisfaction: Employees with longer commute times have slightly lower job satisfaction scores, but the overall trend is not strongly apparent |
| Complex Correlation | The value of Y seems to be related to the value of X, but the relationship is not easily determined. |
| No Correlation | The value of Y decreases as the value of X increases. e.g hours spent watching TV vs grades of a group of students: as the number of hours spent watching TV increases, the grades of the students decrease |
Further Reading
What is a Scatter Diagram? (visual-paradigm.com)
scatter diagram (slideshare.net)
