# Frequency Distribution – What It Is & Use in Trading

## What Is a Frequency Distribution?

A frequency distribution is a table that displays the frequency of various outcomes in a sample.

The first column lists each unique value (or category) of the variable in question, and the second column lists how often that value occurs.

For example, imagine you surveyed 100 people about which type of ice cream they liked best. You could display the results of your survey in a frequency distribution table:

### Type of Ice Cream Frequency

• Vanilla 30
• Chocolate 25
• Strawberry 15
• Mint 10
• Butter Pecan 5
• Other 5

As you can see from this table, 30 out of 100 survey respondents said that vanilla was their favorite type of ice cream, 25 said chocolate, and so on.

Frequency distributions are a useful way to organize data because they provide a quick overview of the distribution of that data.

They can also be used to spot patterns and outliers, which can then be investigated further.

## What Are the Different Types of Frequency Distributions?

There are a few main types of frequency distributions:

• grouped
• ungrouped
• cumulative frequency distribution
• relative frequency distribution, and
• relative cumulative frequency distribution

### Grouped frequency distribution

An ungrouped frequency distribution is simply a list of all the values in a sample with their corresponding frequencies.

### Grouped frequency distribution

Grouped frequency distributions group together values that are similar in some way. For example, you could group together all ice cream flavors that contain chocolate, or all respondents who said they liked either vanilla or strawberry ice cream.

Grouping the data makes it easier to see patterns and compare groups of values, but it also means that you lose some information about the individual values.

### Cumulative frequency distribution

A cumulative frequency distribution shows the total number of items in a sample that have values equal to or less than each value on a scale.

Cumulative frequency distributions are often used because they make it easy to see how many items in a sample fall below (or above) a certain value.

### Relative frequency distribution

A relative frequency distribution is very similar to a regular frequency distribution, except that the frequencies are expressed as percentages instead of raw numbers.

For example, if 30 out of 100 respondents said vanilla was their favorite type of ice cream, then the relative frequency for vanilla would be 30%.

### Relative cumulative frequency distribution

Relative cumulative frequency can be found by dividing the frequency of each interval by the aggregate number of observations.

This type of distribution is important because it can help you to see how many items in a sample fall below (or above) a certain value.

## Use of the Frequency Distribution in Trading

The frequency distribution was used among some floor traders to help them keep track of uptrends and downtrends.

They would mark charts by hand with X’s and O’s, with X denoting an uptrend within a certain block of time and O denoted a downtrend within a certain amount of time.

If several X’s emerged in a row, that meant demand was exceeding supply. And if several O’s emerged in a row, that meant supply exceeded demand.

Pit trading has largely been replaced with electronic trading, but using frequency distributions to keep track of price trends was once a common practice.

## How to Create a Frequency Distribution

To create a frequency distribution, start by organizing your data into a list or table.

Then, count how many times each value occurs and list this information in the second column of your table.

Finally, label both columns and give your table a title.

## Conclusion – Frequency Distribution

Frequency distributions are a useful way to organize data because they provide a quick overview of the distribution of that data.

They can also be used to spot patterns and outliers, which can then be investigated further.

There are a few main types of frequency distributions, including grouped, ungrouped, cumulative, relative, and relative cumulative frequency distributions.

The type of distribution you use will depend on your specific goals and the context in which you plan to use the data.