Selecting the Correct Frequency Table for a Histogram

Oct 13, 2025 by talynt.org 6 views

Understanding the Importance of Frequency Tables

When it comes to creating a histogram, selecting the correct frequency table is crucial. A frequency table is a table that displays the number of times each value in a dataset occurs. In this article, we will discuss how to select the correct frequency table for a histogram, specifically in the context of the mathematics discussion category.

What is a Frequency Table?

A frequency table is a table that displays the number of times each value in a dataset occurs. It is a useful tool for understanding the distribution of data and identifying patterns. Frequency tables can be used to create histograms, which are graphical representations of data that show the distribution of values.

Types of Frequency Tables

There are two main types of frequency tables: absolute frequency tables and relative frequency tables.

Absolute Frequency Tables: An absolute frequency table displays the number of times each value in a dataset occurs. For example, if we have a dataset of exam scores, an absolute frequency table would show the number of students who scored each possible score.
Relative Frequency Tables: A relative frequency table displays the proportion of times each value in a dataset occurs. For example, if we have a dataset of exam scores, a relative frequency table would show the proportion of students who scored each possible score.

Selecting the Correct Frequency Table for a Histogram

When selecting a frequency table for a histogram, we need to consider the type of data we are working with. In this case, we are working with a dataset of exam scores.

Absolute Frequency Table: If we want to create a histogram that shows the number of students who scored each possible score, we would use an absolute frequency table.
Relative Frequency Table: If we want to create a histogram that shows the proportion of students who scored each possible score, we would use a relative frequency table.

Creating a Frequency Table for the Given Data

Let's create a frequency table for the given data:

${82, 83, 89, 67, 65, 88, 66, 69, 83, 81, 94, 68, 82, 69, 86, 83, 88, 62, 64, 93}$

Absolute Frequency Table

Score	Frequency
62	1
64	1
65	1
67	1
68	1
69	2
81	1
82	2
83	3
86	1
88	2
89	1
93	1
94	1

Relative Frequency Table

Score	Relative Frequency
62	0.05
64	0.05
65	0.05
67	0.05
68	0.05
69	0.1
81	0.05
82	0.1
83	0.15
86	0.05
88	0.1
89	0.05
93	0.05
94	0.05

Selecting the Correct Frequency Table

Based on the type of data we are working with, we can select the correct frequency table for the histogram.

If we want to create a histogram that shows the number of students who scored each possible score, we would use the absolute frequency table.
If we want to create a histogram that shows the proportion of students who scored each possible score, we would use the relative frequency table.

Conclusion

In conclusion, selecting the correct frequency table for a histogram is crucial. We need to consider the type of data we are working with and choose the frequency table that best represents the data. In this article, we discussed how to select the correct frequency table for a histogram, specifically in the context of the mathematics discussion category. We created an absolute frequency table and a relative frequency table for the given data and selected the correct frequency table based on the type of data we were working with.

Frequently Asked Questions

What is a frequency table?
What are the two main types of frequency tables?
How do I select the correct frequency table for a histogram?
What is the difference between an absolute frequency table and a relative frequency table?

References

[1] Wikipedia. (n.d.). Frequency table. Retrieved from https://en.wikipedia.org/wiki/Frequency_table
[2] Khan Academy. (n.d.). Frequency tables. Retrieved from https://www.khanacademy.org/math/statistics-probability/summarizing-quantitative-data/frequency-tables/v/frequency-tables
[3] Math Is Fun. (n.d.). Frequency tables. Retrieved from https://www.mathisfun.com/data/frequency-tables.html
Frequently Asked Questions (FAQs) about Frequency Tables ===========================================================

Q: What is a frequency table?

A: A frequency table is a table that displays the number of times each value in a dataset occurs. It is a useful tool for understanding the distribution of data and identifying patterns.

Q: What are the two main types of frequency tables?

A: The two main types of frequency tables are:

Absolute Frequency Tables: An absolute frequency table displays the number of times each value in a dataset occurs.
Relative Frequency Tables: A relative frequency table displays the proportion of times each value in a dataset occurs.

Q: How do I select the correct frequency table for a histogram?

A: To select the correct frequency table for a histogram, you need to consider the type of data you are working with. If you want to create a histogram that shows the number of students who scored each possible score, you would use an absolute frequency table. If you want to create a histogram that shows the proportion of students who scored each possible score, you would use a relative frequency table.

Q: What is the difference between an absolute frequency table and a relative frequency table?

A: The main difference between an absolute frequency table and a relative frequency table is the way the data is displayed. An absolute frequency table displays the number of times each value in a dataset occurs, while a relative frequency table displays the proportion of times each value in a dataset occurs.

Q: How do I create a frequency table?

A: To create a frequency table, you need to count the number of times each value in a dataset occurs. You can use a table or a spreadsheet to help you with this process.

Q: What are some common uses of frequency tables?

A: Frequency tables are commonly used in a variety of fields, including:

Statistics: Frequency tables are used to understand the distribution of data and identify patterns.
Data Analysis: Frequency tables are used to summarize and analyze large datasets.
Research: Frequency tables are used to identify trends and patterns in data.

Q: How do I interpret a frequency table?

A: To interpret a frequency table, you need to understand the data and the patterns that are present. You can use the frequency table to identify trends, patterns, and correlations in the data.

Q: What are some common mistakes to avoid when creating a frequency table?

A: Some common mistakes to avoid when creating a frequency table include:

Not counting the data correctly: Make sure to count the data correctly and accurately.
Not using the correct type of frequency table: Make sure to use the correct type of frequency table for the data you are working with.
Not interpreting the data correctly: Make sure to interpret the data correctly and understand the patterns and trends that are present.

Q: How do I use a frequency table to create a histogram?

A: To use a frequency table to create a histogram, you need to follow these steps:

Create a frequency table: Create a frequency table for the data you are working with.
Determine the type of frequency table: Determine the type of frequency table you need to use (absolute or relative).
Create the histogram: Use the frequency table to create the histogram.

Q: What are some common applications of frequency tables?

A: Frequency tables have a wide range of applications, including:

Business: Frequency tables are used to analyze customer data and identify trends and patterns.
Healthcare: Frequency tables are used to analyze patient data and identify trends and patterns.
Education: Frequency tables are used to analyze student data and identify trends and patterns.

Conclusion

In conclusion, frequency tables are a powerful tool for understanding and analyzing data. By following the steps outlined in this article, you can create a frequency table and use it to identify trends and patterns in your data. Remember to select the correct type of frequency table for the data you are working with and to interpret the data correctly.