EDEXCEL GCSE FOUNDATION MATHS - What are Frequency Polygons

Edexcel GCSE Foundation Maths: Frequency Polygons

What is a Frequency Polygon?

A frequency polygon is a graph that uses lines to connect points representing the frequency of data. It is similar to a histogram, but instead of bars, it uses points and lines.

Steps to Construct a Frequency Polygon:

1. Create a table: Organize your data into a frequency table, listing the categories or groups and their corresponding frequencies.
2. Plot the points: On a graph, mark the midpoint of each category on the x-axis and the corresponding frequency on the y-axis.
3. Connect the points: Use straight lines to connect the plotted points.
4. Close the polygon: Connect the first and last points to the x-axis at the midpoints of the categories before and after your data range.

Example:

Let's say you have the following data representing the number of students in each year group at a school:

To create a frequency polygon:

1. Plot the points: Plot the points (7, 120), (8, 100), (9, 90), (10, 80), and (11, 70).
2. Connect the points: Draw lines connecting these points.
3. Close the polygon: Connect the first point (7, 120) to the x-axis at the midpoint of the category before Year 7 (6.5) and connect the last point (11, 70) to the x-axis at the midpoint of the category after Year 11 (11.5).

Advantages of Frequency Polygons:

• Visual representation of trends: Frequency polygons clearly show the distribution and trends in data.
• Comparison of data: You can easily compare different data sets by plotting their frequency polygons on the same graph.
• Smoothness: They provide a smoother representation of data compared to histograms.

Limitations of Frequency Polygons:

• Less accurate for small data sets: They can be less accurate for data with a small number of observations.
• Difficulty in representing exact values: Frequency polygons don't show the exact values of the data, only their frequencies.

In conclusion, frequency polygons are a useful tool for visualizing and analyzing data, especially for showing trends and making comparisons between data sets.