1281 LearnersLast updated on June 18th, 2025
Scatter Plot
A scatter plot is a chart or graphical representation that helps you show the relationship between two things by using dots on a graph. A scatter plot is widely utilized in determining the rate of change of one quantity in relation to another. For example, inflation and employment. We will now talk about the scatter plot and its significance in detail.
What is a scatter plot?
A scatter plot also known as a scatter diagram is a simple visual representation of data. It is regarded as one of the seven basic tools of quality and is plotted on a two-dimensional graph known as the Cartesian plane.
In scatter diagrams, the different values are represented by drawing dots on the graph. These are utilized to understand the mathematical relationship between two variables in various sectors.
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How to Construct a Scatter Plot
To construct a scatter plot, follow the simple steps given below:
- Identify the two numerical values to compare
- Determine which variable will be placed on the x-axis and which on the y-axis.
- Define and label the axes.
- Plot each pair of data corresponding to its values on the graph.
What are the types of Scatter Plot
There are three main types of scatter plots based on the relationship between two variables. Let’s learn about the different types based on their correlation:
Positive Correlation
When the value of the y-axis rises as you move from left to right, it is considered to have a positive correlation. In simple terms, if the two quantities are directly proportional, they are in a positive correlation. It can be divided into three types: Perfect positive, High Positive, and Low Positive.
Negative Correlation
A scatter plot represents a negative correlation when the value of the y-axis decreases on moving left to right. In negative correlation, the variables are said to be inversely proportional. The different types of negative correlation include Perfect Negative, High Negative and Low Negative.
Null Correlation
We generally use null correlation to represent that there is no relationship between the two variables plotted on the scatter plot. The points denoted do not have a specific pattern and are scattered randomly, conveying that they have a null correlation.
Real-Life Applications of Scatter Plot
Scatter plots are widely used in various sectors to determine the relationship between two variables or quantities. Let’s look at a few applications of scatter plots:
- Teachers and students often use scatter plots to understand the relationship between study hours and test scores.
- Companies use scatter plots to analyze the relationship between expenditure and sales.
- Athletes can plot a scatter plot to analyze how training hours impact their performance.
- In social science, scatter plots can be applied to determine the relationship between illiteracy and the unemployment rate.
- Scatter plots help students understand how screen time directly affects their productivity.
Common Mistakes and How to Avoid Them in Scatter Plot
Students make mistakes when plotting on a scatter plot. These mistakes can be avoided with a proper understanding of the concepts and solutions. Let’s look at some common errors along with their solutions:
Mistake 1
Overcrowding of Points
They include numerous data points, leading to congestion and unreadability.
Always ensure that you plot only relevant points. To make it easier, you can break the data into simpler sets.
Mistake 2
Not Labeling Axes
Some students may forget to label the axes or include the units.
To avoid this error, always label the x-axis and y-axis with the correct names and units. For example, if the kids are making a graph about Distance vs. Time, write Distance (miles) on the y-axis and Time (hours) on the x-axis. This makes the graph clear and easy to understand.
Mistake 3
Forgetting Outliers
Sometimes they may forget to plot the value that is higher or lower than the other values on the graph.
Double-check that all relevant points are plotted on the graph.
Mistake 4
Not Using a Legend
They mistakenly do not use a key for large sets of data, leading to misinterpretation.
Always use a clear legend to distinguish between the different variables.
Mistake 5
Incorrect Scale on Axes
One common mistake is using an incorrect or uneven scale for the axes.
Ensure you use a scale where the numbers are spaced evenly with proper intervals.
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Solved examples of Scatter Plot
Problem 1
The following table shows the height (in cm) and weight (in kg) of 6 workers. Plot a scatter plot for this data and analyze the relationship between height and weight.
| Workers | Height (cm) | Weight (kg) |
| A | 145 | 52 |
| B | 140 | 45 |
| C | 170 | 65 |
| D | 154 | 60 |
| E | 160 | 70 |
| F | 150 | 54 |
Explanation
We plot the data points on the graph:
Height (in cm) along the x-axis
Weight (in kg) along the y-axis
Represent each worker’s data point as a coordinate (height, weight)
Now, plot the scatter plot for the coordinates (height, weight) for each worker as follows:
A: (145, 52)
B: (140, 45)
C: (170, 65)
D: (154, 60)
E: (160, 70)
F: (150, 54)
Here, height and weight are in positive correlation, i.e., they are directly proportional. Since the graph is not a perfectly straight line, it indicates that other factors may also affect the relationship. The data is consistent and has no extreme outliers.
Problem 2
The following table shows the temperature (°C) and the number of ice creams sold on different days. Plot a scatter plot and analyze the relationship.
| Day | Temperature (°C) | Ice Creams Sold |
| 1 | 25 | 80 |
| 2 | 15 | 50 |
| 3 | 30 | 100 |
| 4 | 25 | 70 |
| 5 | 35 | 150 |
| 6 | 40 | 200 |
Explanation
Plot:
The temperature in oC X-axis
The number of ice creams sold (out of 100) Y-axis.
Coordinates should be represented as (Temperature, Ice Creams Sold).
A: (25, 80)
B: (15, 50)
C: (30, 100)
D: (25, 70)
E: (35, 150)
F: (40, 200)
Here, the temperature is directly proportional to the number of ice creams sold (positive correlation).
15oC 50 ice creams were sold.
35oC 150 ice creams were sold.
40oC 200 ice creams were sold.
Problem 3
The following table shows the number of hours of study and the test scores of students. Create a scatter plot and interpret the results.
| Students | Study (hours) | Test Scores (out of 100) |
| A | 8 | 85 |
| B | 3 | 50 |
| C | 4 | 60 |
| D | 6 | 70 |
| E | 5 | 65 |
| F | 9 | 95 |
Explanation
Plot:
The number of hours of study X-axis
Test scores (out of 100) Y-axis.
Coordinates should be represented as (study hours, Test scores).
The coordinates representing the study hour and test score of each student are as follows:
A: (8, 85)
B: (3, 50)
C: (4,60)
D: (6, 70)
E: (5,65)
F: (9,95)
Based on the data, there is a positive correlation between the variables. Since the graph is not a straight line, it means even other factors could affect the test results.
Problem 4
The following table shows the water drunk (liters) and the hydration level of 5 students in a week. Plot a scatter plot and analyze the relationship.
NA
Explanation
Plot:
The water drunk in liters X-axis
Hydration level (out of 10) Y-axis.
Coordinates should be represented as (Water drunk, Hydration level).
A: (10, 8)
B: (5, 4)
C: (15, 9)
D: (8, 6)
E: (20, 10)
Based on the data, there is a positive correlation between the variables which depicts that the more water you drink, the higher their hydration will be.
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FAQs on the Scatter Plot
1.What do you mean by a scatter plot?
2.What is a positive correlation?
3.What do you mean by outliers in a scatter plot?
4.Why do we use a scatter plot?
5.What is the major difference between a scatter plot and a line graph?
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Jaipreet Kour Wazir
About the Author
Jaipreet Kour Wazir is a data wizard with over 5 years of expertise in simplifying complex data concepts. From crunching numbers to crafting insightful visualizations, she turns raw data into compelling stories. Her journey from analytics to education ref
Fun Fact
: She compares datasets to puzzle games—the more you play with them, the clearer the picture becomes!
