## Introduction

**Frequency distributions** demonstrate the number of occurrences of a given item within a data set. This type of analysis can be used in many areas where datasets are collected, including market research and scientific studies. A frequency distribution provides a visual picture that can quickly provide insight into patterns or trends in the dataset.

*Microsoft Excel* is an ideal software platform for creating frequency distributions, and allows you to tailor your distribution to your particular data set. This article outlines how to create a frequency distribution table in Microsoft Excel, graphing the results, and using the results to interpret information about your data set:

- Create a frequency distribution table.
- Graph the results.
- Interpret the results.

## Data Preparation

**Data preparation** is one of the *most important steps in data analysis*. It involves the selection, cleansing, and formatting of data so that it is ready for further analysis. One of the key steps in data preparation is the creation of a **frequency distribution**.

In this article, we will look at how to create a frequency distribution in **Excel**.

### Prepare the data set

Creating a frequency distribution in Excel is a simple process. The first step is to prepare the data set. Make sure all of the data points are recorded and organized in numerical order, with no blank cells between them, along with any subintervals that are part of the original set. Also, make sure that there is a column for each category of data.

If data points do not fall exactly along the upper and lower limits of an interval, use one or more decimal places to correctly record the exact value. If any two numbers are identical, record them in separate rows so they will appear as separate values when later graphed.

In addition to organizing the data points numerically and into intervals, give each row a unique identifier (perhaps through numbering or via labels such as *“first,” “second,”* etc.) so that you can refer back to individual data elements when needed. Lastly, take care to **clearly label each column** to reflect its contents – this is particularly useful once frequency distributions begin being created and compared using multiple forms of visualization such as charts or tables.

### Enter the data into a spreadsheet

Entering data into the spreadsheet is one of the most important steps in frequency distribution. Make sure you *accurately enter all required information* into the cells correctly, starting with the first row and then proceeding with each successive row of data. Double-check all entries to ensure accuracy before moving onto the next step.

Frequency distribution tables typically require specific information like **values, labels and frequencies**. Values are intended to show the numerical values present in a given set of data. It is best to enter these in **ascending order** so that they appear neat and orderly in the final chart. Labels provide context or break down a larger group into smaller categories that are easier to interpret on a chart. It is important to remember that labels should be properly formatted as percentages, fractions or other valid notation (as needed). Lastly, frequencies refer to how often certain values appear together within a certain dataset or label category; it is usually represented by a *count number* which indicates how many instances of each value were recorded relative to one another.

When entering your data into the spreadsheet, always consider the **layout first and foremost**; it will make it much more readable after you’ve finished creating your frequency distribution table. Check for any erroneous items then format everything precisely so that you can easily comprehend what’s being presented eventually once you finish setting up your charting components.

## Creating the Frequency Distribution

**Creating a frequency distribution in Excel** is a great way to analyse data that has been collected. It allows you to group and organize data so that you can better understand it. Frequency distributions make it easier to determine the *most commonly occurring values* in a dataset. It is also a useful tool for creating charts and graphs.

In this article, we will cover the steps necessary to create a frequency distribution in Excel:

- Step 1:
- Step 2:
- Step 3:
- Step 4:
- Step 5:

### Select the data set

When you create a frequency distribution in Microsoft Excel, the program first examines all of the values in your data set to identify how many times each value occurs. This process is fairly simple if you are using raw numerical data, such as test scores or weights. If your data contains categories, such as *hair color or political affiliation*, it is important to sort it so that like categories (e.g., **blonde and brown hair**) are grouped together. This grouping makes it easier to calculate the total count and percentage of each category in your data set.

To begin creating your frequency distribution, select all of your data and sort it in either ascending or descending order (e.g., A-Z or Z-A). While sorting the data helps create a neat presentation, it does not affect the accuracy of the results if your categories already have some logical order (e.g., **light roast, medium roast**). Once all of your data is sorted correctly, highlight it by clicking and dragging over it with your mouse before copying and pasting this selection into a new worksheet within Excel.

From here, you can use Excel’s standard functions to create a frequency distribution for further analysis and visualization purposes.

### Set up the frequency distribution table

**Creating a frequency distribution table in Excel** is a useful way to organize data into useful categories. This guide will show you how to set up the frequency distribution table and calculate the distribution of frequencies for that set of data.

- Identify number of classes needed

The number of classes you’ll need for your frequency table depends on the amount and variability of your input data. The more variable the data, the more classes you’ll need. The general rule is that you’ll need approximately**seven or eight classes**, but it could be anywhere from five to twenty depending on the complexity of your data set. - Select class limits

To determine class limits, pick two numbers that are close together and encompass all values in your dataset. Find one number slightly above the highest value in your dataset and one slightly below the lowest value in your dataset. These two numbers will mark the**upper and lower bounds**for your class limits respectively. - Calculate each class width

Calculating each class width is key to effectively grouping your dataset by class type. Subtract upper limit by lower limit divided by adjusted number from Step 1 (7-9) then divide result into**equal parts per class width units**(11-13). After completing this step, you should have determined each class’s range as its own unique interval unit size which can be listed as part of identifying criteria for that interval/class type (e.g., range ±0-5). - Count items & populate frequency distribution table

After determining appropriate intervals & identifying criteria per interval/class type, ‘count items’ involved within shape created & drag box shape over within every interval/class type to represent counted items populated across their respective permissioned areas (e.g., range ±0-3). As different colored shapes get pulled apart from populated area zones, make sure list those results on section name labeled ‘**Frequency Distribution Table**’ at bottom lower portion corner designated countable areas so people can draw conclusions about collected item sets mentioned here present whenever needed most…

### Calculate the frequency

To calculate the frequency of a set of data, use the **COUNTIFS** function. This function counts the number of times specific values occur in a range. To create this function, start with “**COUNTIFS**” followed by two brackets and then two parameters. The first parameter is the *range of elements to count*, and the second is the *condition that must be met for it to be counted*.

The format should look like this: **COUNTIFS(range,criteria)**. For example, if you wanted to count how many students received a grade of A in your class, you would use **COUNTIFS(A1:A50,”A”)**.

Once you have created your function in Excel, you can then apply it to other data points to get your frequency distribution. For example, if you wanted to find out how many students got grades between 80 and 90 in your class, you can use **COUNTIFS(A1:A50,”>=80″, A1:A50,”<=90″)**. You will receive a numerical value representing how many times this particular criteria was met throughout your data set.

By continuing this process for every data point within its own frequency range, you will soon have an accurate frequency distribution for all of your data points.

## Visualizing the Frequency Distribution

**Creating a frequency distribution in Excel** allows you to visually represent your data, making it easier to understand. You can use the frequency distribution to identify trends and outliers in your data and draw meaningful conclusions. It’s a great way to quickly summarize a large dataset and make sure all of the important points are understood.

Let’s take a look at how we can use Excel to create a frequency distribution:

### Create a bar chart

To create a bar chart in Excel, start by selecting all the data that you would like to use. Then, open the Insert tab on the ribbon, click on **Bar Chart** and choose the appropriate chart type.

In this example, we will select a *clustered bar chart* with four columns of data. The **Chart Title** option allows you to customize the title for the chart. Next, use the **Axis Titles** and **Legend** options to organize your labels and data labels. This makes it easier for viewers of your graph to understand what is being portrayed in each column.

The **Color** button allows you to customize colors for each bar and choose from preset color schemes. Finally, if needed, you can modify where axis lines are located by using **Gridlines**. Once you have finished setting up your chart using these steps, click OK and finish creating your frequency distribution visual!

### Create a line chart

Once the frequency distribution table is complete, you can create a line chart to visualize it. To create the graph, first select the table data, including the labels in column A and both columns of numbers. Then click on **Insert – Line – Line with Markers** to open the Chart Editor. In this window you can edit your graph by formatting colors, lines, and fonts. You can also add axis titles and legends to label each series on the chart. Finally, click outside of your graph to make sure changes are saved before you close the Chart Editor window.

Once finished, your chart will look like a histogram – it will show how many times various values appear in your table by displaying their individual sizes as *bars next to each other along an axis*. This makes it easy to compare different groups of data or track changes over time in a single glance!

## Conclusion

Creating a frequency distribution in Excel is fairly simple, however you do need an organized data set (or rows/columns of numbers) to achieve meaningful results. The **FREQUENCY** function is often the best choice since it allows you to easily get counts, percentages and cumulative percentages.

Additionally, you may want to consider investing in a statistical software program that better fits the analysis needs of your specific project. For example, **SPSS Statistics** is designed to quickly and accurately analyze data from questionnaires, surveys or experiments, including almost any frequency distribution needed for your research or study.

Regardless of which method you choose for creating a frequency distribution in Excel (or other software package), this tool will help you determine various qualities about the populations or elements contained in your dataset. Whether it’s understanding how many people fall into each age group within a certain demographic or analyzing responses from questions on a survey – understanding *frequencies* can be the difference between making effective decisions and guesswork.